1
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Hollenbach JD, Pate CM, Jia H, Hart JL, Clancy P, Taheri ML. Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning. Sci Rep 2024; 14:17881. [PMID: 39095485 PMCID: PMC11297154 DOI: 10.1038/s41598-024-66902-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/05/2024] [Indexed: 08/04/2024] Open
Abstract
In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.
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Affiliation(s)
- Jonathan D Hollenbach
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Cassandra M Pate
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Haili Jia
- Department of Chemical and Biomolecular and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - James L Hart
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Paulette Clancy
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemical and Biomolecular and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mitra L Taheri
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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2
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Levell Z, Le J, Yu S, Wang R, Ethirajan S, Rana R, Kulkarni A, Resasco J, Lu D, Cheng J, Liu Y. Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis. Chem Rev 2024; 124:8620-8656. [PMID: 38990563 DOI: 10.1021/acs.chemrev.3c00735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.
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Affiliation(s)
- Zachary Levell
- Texas Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jiabo Le
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, 1219 Zhongguan West Road, Ningbo 315201, China
| | - Saerom Yu
- Texas Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Ruoyu Wang
- Texas Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sudheesh Ethirajan
- Department of Chemical Engineering, University of California, Davis, California 95616, United States
| | - Rachita Rana
- Department of Chemical Engineering, University of California, Davis, California 95616, United States
| | - Ambarish Kulkarni
- Department of Chemical Engineering, University of California, Davis, California 95616, United States
| | - Joaquin Resasco
- Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Deyu Lu
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Jun Cheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Laboratory of AI for Electrochemistry (AI4EC), Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
| | - Yuanyue Liu
- Texas Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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3
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Kvashnina KO. Electronic-Structure Interpretation: How Much Do We Understand Ce L 3 XANES? Chemistry 2024:e202400755. [PMID: 38860741 DOI: 10.1002/chem.202400755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/12/2024]
Abstract
Historically, cerium has been attractive for pharmaceutical and industrial applications. The cerium atom has the unique ability to cycle between two chemical states (Ce(III) and Ce(IV)) and drastically adjust its electronic configuration: [Xe] 4f15d16s2 in response to a chemical reaction. Understanding how electrons drive chemical reactions is an important topic. The most direct way of probing the chemical and electronic structure of materials is by X-ray absorption spectroscopy (XAS) or X-ray absorption near-edge structure (XANES) in high energy resolution fluorescence detection (HERFD) mode. Such measurements at the Ce L3 edge have the advantage of a high penetration depth, enabling in-situ reaction studies in a time-resolved manner and investigation of material production or material performance under specific conditions. But how much do we understand Ce L3 XANES? This article provides an overview of the information that can be extracted from experimental Ce L3 XAS/XANES/HERFD data. A collection of XANES data recorded on various cerium systems in HERFD mode is presented here together with detailed discussions on data analysis and the current status of spectral interpretation, including electronic structure calculations.
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Affiliation(s)
- Kristina O Kvashnina
- The Rossendorf Beamline at ESRF, The European Synchrotron, CS40220, 38043, Grenoble Cedex 9, France
- Institute of Resource Ecology, Helmholtz Zentrum Dresden Rossendorf, Dresden, 01328, Germany
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4
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Zarrouk T, Ibragimova R, Bartók AP, Caro MA. Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon. J Am Chem Soc 2024; 146:14645-14659. [PMID: 38749497 PMCID: PMC11140750 DOI: 10.1021/jacs.4c01897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024]
Abstract
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.
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Affiliation(s)
- Tigany Zarrouk
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| | - Rina Ibragimova
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| | - Albert P. Bartók
- Department
of Physics, University of Warwick, Coventry CV4 7AL, U.K.
- Warwick
Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K.
| | - Miguel A. Caro
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
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5
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Abrosimov SV, Protsenko BO, Mannaa AS, Vlasenko VG, Guda SA, Pankin IA, Burlov AS, Koshchienko YV, Guda AA, Soldatov AV. Improving sensitivity of XANES structural fit to the bridged metal-metal coordination. JOURNAL OF SYNCHROTRON RADIATION 2024; 31:447-455. [PMID: 38530834 DOI: 10.1107/s1600577524002091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 03/03/2024] [Indexed: 03/28/2024]
Abstract
Hard X-ray absorption spectroscopy is a valuable in situ probe for non-destructive diagnostics of metal sites. The low-energy interval of a spectrum (XANES) contains information about the metal oxidation state, ligand type, symmetry and distances in the first coordination shell but shows almost no dependency on the bridged metal-metal bond length. The higher-energy interval (EXAFS), on the contrary, is more sensitive to the coordination numbers and can decouple the contribution from distances in different coordination shells. Supervised machine-learning methods can combine information from different intervals of a spectrum; however, computational approaches for the near-edge region of the spectrum and higher energies are different. This work aims to keep all benefits of XANES and extend its sensitivity towards the interatomic distances in the first and second coordination shells. Using a binuclear bridged copper complex as a case study and cross-validation analysis as a quantitative tool it is shown that the first 170 eV above the edge are already sufficient to balance the contributions of Cu-O/N scattering and Cu-Cu scattering. As a more general outcome this work highlights the trivial but often overlooked importance of using `longer' energy intervals of XANES for structural refinement and machine-learning predictions. The first 200 eV above the absorption edge still do not require parametrization of Debye-Waller damping and can be calculated within full multiple scattering or finite difference approximations with only moderately increased computational costs.
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Affiliation(s)
- S V Abrosimov
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - B O Protsenko
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - A S Mannaa
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - V G Vlasenko
- Institute of Physics, Southern Federal University, Stachki Ave 194, 344090 Rostov-on-Don, Russian Federation
| | - S A Guda
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - I A Pankin
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - A S Burlov
- Institute of Physical and Organic Chemistry, Stachki Ave 194/2, 344090 Rostov-on-Don, Russian Federation
| | - Y V Koshchienko
- Institute of Physical and Organic Chemistry, Stachki Ave 194/2, 344090 Rostov-on-Don, Russian Federation
| | - A A Guda
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - A V Soldatov
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
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6
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Liu J, Zhao J, Du J, Peng S, Wu J, Zhang W, Yan X, Lin Z. Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS. JOURNAL OF HAZARDOUS MATERIALS 2024; 468:133797. [PMID: 38377906 DOI: 10.1016/j.jhazmat.2024.133797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 02/22/2024]
Abstract
Heavy metals raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment. However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well-trained multi-grained cascade forest models exhibited high accuracy (> 90%) and predictive performance (R2 ∼ 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations. This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would provide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.
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Affiliation(s)
- Junqin Liu
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China
| | - Jiang Zhao
- School of Mathmatics and Statistics, Beijing Technology and Business University, Beijing 100048, China
| | - Jiapan Du
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China
| | - Suyi Peng
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China
| | - Jiahui Wu
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China
| | - Wenchao Zhang
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China; State Key Laboratory of Advanced Metallurgy for Non-ferrous Metals, Changsha 410083, China; Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha, Hunan 410083, China
| | - Xu Yan
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China; State Key Laboratory of Advanced Metallurgy for Non-ferrous Metals, Changsha 410083, China; Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha, Hunan 410083, China.
| | - Zhang Lin
- School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China; State Key Laboratory of Advanced Metallurgy for Non-ferrous Metals, Changsha 410083, China; Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha, Hunan 410083, China
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7
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Carbone MR, Maffettone PM, Qu X, Yoo S, Lu D. Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy. J Phys Chem A 2024. [PMID: 38416723 DOI: 10.1021/acs.jpca.3c06910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multimodal advantage" over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.
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Affiliation(s)
- Matthew R Carbone
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Phillip M Maffettone
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Xiaohui Qu
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Deyu Lu
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
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8
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Zhao Y, Li H, Shan J, Zhang Z, Li X, Shi JQ, Jiao Y, Li H. Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis. J Phys Chem Lett 2023:11058-11062. [PMID: 38048178 DOI: 10.1021/acs.jpclett.3c02896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
Single-atom catalysts (SACs) offer significant potential across various applications, yet our understanding of their formation mechanism remains limited. Notably, the pyrolysis of zeolitic imidazolate frameworks (ZIFs) stands as a pivotal avenue for SAC synthesis, of which the mechanism can be assessed through infrared (IR) spectroscopy. However, the prevailing analysis techniques still rely on manual interpretation. Here, we report a machine learning (ML)-driven analysis of the IR spectroscopy to unravel the pyrolysis process of Pt-doped ZIF-67 to synthesize Pt-Co3O4 SAC. Demonstrating a total Pearson correlation exceeding 0.7 with experimental data, the algorithm provides correlation coefficients for the selected structures, thereby confirming crucial structural changes with time and temperature, including the decomposition of ZIF and formation of Pt-O bonds. These findings reveal and confirm the formation mechanism of SACs. As demonstrated, the integration of ML algorithms, theoretical simulations, and experimental spectral analysis introduces an approach to deciphering experimental characterization data, implying its potential for broader adoption.
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Affiliation(s)
- Yanzhang Zhao
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Huan Li
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jieqiong Shan
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia
- Department of Chemistry, City University of Hong Kong, Kowloon 999077, Hong Kong Special Administrative Region of the People's Republic of China
| | - Zhen Zhang
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia 5000, Australia
| | - Xinyu Li
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia 5000, Australia
| | - Javen Qinfeng Shi
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia 5000, Australia
| | - Yan Jiao
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Haobo Li
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia
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9
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Watson L, Pope T, Jay RM, Banerjee A, Wernet P, Penfold TJ. A Δ-learning strategy for interpretation of spectroscopic observables. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2023; 10:064101. [PMID: 37941993 PMCID: PMC10629969 DOI: 10.1063/4.0000215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023]
Abstract
Accurate computations of experimental observables are essential for interpreting the high information content held within x-ray spectra. However, for complicated systems this can be difficult, a challenge compounded when dynamics becomes important owing to the large number of calculations required to capture the time-evolving observable. While machine learning architectures have been shown to represent a promising approach for rapidly predicting spectral lineshapes, achieving simultaneously accurate and sufficiently comprehensive training data is challenging. Herein, we introduce Δ-learning for x-ray spectroscopy. Instead of directly learning the structure-spectrum relationship, the Δ-model learns the structure dependent difference between a higher and lower level of theory. Consequently, once developed these models can be used to translate spectral shapes obtained from lower levels of theory to mimic those corresponding to higher levels of theory. Ultimately, this achieves accurate simulations with a much reduced computational burden as only the lower level of theory is computed, while the model can instantaneously transform this to a spectrum equivalent to a higher level of theory. Our present model, demonstrated herein, learns the difference between TDDFT(BLYP) and TDDFT(B3LYP) spectra. Its effectiveness is illustrated using simulations of Rh L3-edge spectra tracking the C-H activation of octane by a cyclopentadienyl rhodium carbonyl complex.
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Affiliation(s)
- Luke Watson
- Chemistry, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Thomas Pope
- Chemistry, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Raphael M. Jay
- Department of Physics and Astronomy, Uppsala University, 751 20 Uppsala, Sweden
| | - Ambar Banerjee
- Department of Physics and Astronomy, Uppsala University, 751 20 Uppsala, Sweden
| | - Philippe Wernet
- Department of Physics and Astronomy, Uppsala University, 751 20 Uppsala, Sweden
| | - Thomas J. Penfold
- Chemistry, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
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10
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Wang J, Hsu CS, Wu TS, Chan TS, Suen NT, Lee JF, Chen HM. In situ X-ray spectroscopies beyond conventional X-ray absorption spectroscopy on deciphering dynamic configuration of electrocatalysts. Nat Commun 2023; 14:6576. [PMID: 37852958 PMCID: PMC10584842 DOI: 10.1038/s41467-023-42370-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 10/04/2023] [Indexed: 10/20/2023] Open
Abstract
Realizing viable electrocatalytic processes for energy conversion/storage strongly relies on an atomic-level understanding of dynamic configurations on catalyst-electrolyte interface. X-ray absorption spectroscopy (XAS) has become an indispensable tool to in situ investigate dynamic natures of electrocatalysts but still suffers from limited energy resolution, leading to significant electronic transitions poorly resolved. Herein, we highlight advanced X-ray spectroscopies beyond conventional XAS, with emphasis on their unprecedented capabilities of deciphering key configurations of electrocatalysts. The profound complementarities of X-ray spectroscopies from various aspects are established in a probing energy-dependent "in situ spectroscopy map" for comprehensively understanding the solid-liquid interface. This perspective establishes an indispensable in situ research model for future studies and offers exciting research prospects for scientists and spectroscopists.
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Affiliation(s)
- Jiali Wang
- Department of Chemistry and Center for Emerging Materials and Advanced Devices, National Taiwan University, Taipei, 10617, Taiwan
| | - Chia-Shuo Hsu
- Department of Chemistry and Center for Emerging Materials and Advanced Devices, National Taiwan University, Taipei, 10617, Taiwan
| | - Tai-Sing Wu
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan
| | - Ting-Shan Chan
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan.
| | - Nian-Tzu Suen
- College of Chemistry & Chemical Engineering, Yangzhou University, 225002, Yangzhou, China
| | - Jyh-Fu Lee
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan
| | - Hao Ming Chen
- Department of Chemistry and Center for Emerging Materials and Advanced Devices, National Taiwan University, Taipei, 10617, Taiwan.
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan.
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, 11031, Taiwan.
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11
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Pielsticker L, Nicholls RL, DeBeer S, Greiner M. Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra. Anal Chim Acta 2023; 1271:341433. [PMID: 37328241 DOI: 10.1016/j.aca.2023.341433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/15/2023] [Accepted: 05/26/2023] [Indexed: 06/18/2023]
Abstract
X-ray photoelectron spectroscopy is an indispensable technique for the quantitative determination of sample composition and electronic structure in diverse research fields. Quantitative analysis of the phases present in XP spectra is usually conducted manually by means of empirical peak fitting performed by trained spectroscopists. However, with recent advancements in the usability and reliability of XPS instruments, ever more (inexperienced) users are creating increasingly large data sets that are harder to analyze by hand. In order to aid users with the analysis of large XPS data sets, more automated, easy-to-use analysis techniques are needed. Here, we propose a supervised machine learning framework based on artificial convolutional neural networks. By training such networks on large numbers of artificially created XP spectra with known quantifications (i.e., for each spectrum, the concentration of each chemical species is known), we created universally applicable models for auto-quantification of transition-metal XPS data that are able to predict the sample composition from spectra within seconds. Upon evaluation against more traditional peak fitting methods, we showed that these neural networks achieve competitive quantification accuracy. The proposed framework is shown to be flexible enough to accommodate spectra containing multiple chemical elements and measured with different experimental parameters. The use of dropout variational inference for the determination of quantification uncertainty is illustrated.
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Affiliation(s)
- Lukas Pielsticker
- Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany.
| | - Rachel L Nicholls
- Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany
| | - Serena DeBeer
- Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany
| | - Mark Greiner
- Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany
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12
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Martini A, Hursán D, Timoshenko J, Rüscher M, Haase F, Rettenmaier C, Ortega E, Etxebarria A, Roldan Cuenya B. Tracking the Evolution of Single-Atom Catalysts for the CO 2 Electrocatalytic Reduction Using Operando X-ray Absorption Spectroscopy and Machine Learning. J Am Chem Soc 2023; 145:17351-17366. [PMID: 37524049 PMCID: PMC10416299 DOI: 10.1021/jacs.3c04826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Indexed: 08/02/2023]
Abstract
Transition metal-nitrogen-doped carbons (TMNCs) are a promising class of catalysts for the CO2 electrochemical reduction reaction. In particular, high CO2-to-CO conversion activities and selectivities were demonstrated for Ni-based TMNCs. Nonetheless, open questions remain about the nature, stability, and evolution of the Ni active sites during the reaction. In this work, we address this issue by combining operando X-ray absorption spectroscopy with advanced data analysis. In particular, we show that the combination of unsupervised and supervised machine learning approaches is able to decipher the X-ray absorption near edge structure (XANES) of the TMNCs, disentangling the contributions of different metal sites coexisting in the working TMNC catalyst. Moreover, quantitative structural information about the local environment of active species, including their interaction with adsorbates, has been obtained, shedding light on the complex dynamic mechanism of the CO2 electroreduction.
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Affiliation(s)
- Andrea Martini
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | | | - Janis Timoshenko
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Martina Rüscher
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Felix Haase
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Clara Rettenmaier
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Eduardo Ortega
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Ane Etxebarria
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Beatriz Roldan Cuenya
- Department of Interface Science, Fritz-Haber Institute of the Max Planck Society, 14195 Berlin, Germany
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13
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Polyanichenko DS, Protsenko BO, Egil NV, Kartashov OO. Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5321. [PMID: 37570025 PMCID: PMC10419857 DOI: 10.3390/ma16155321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/16/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023]
Abstract
The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material's electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents. However, this approach requires the creation of a special environment that provides a reliable level of response to the agent's actions. As the physical experimental environment of nanocatalyst diagnostics is potentially a complex multiscale system, there are no unified comprehensive representations that formalize the structure and states as a single digital model. This study proposes an approach based on the decomposition of the experimental system into the original physically plausible nodes, with subsequent merging and optimization as a metagraphic representation with which to model the complex multiscale physicochemical environments. The advantage of this approach is the possibility to directly use the numerical model to predict the system states and to optimize the experimental conditions and parameters. Additionally, the obtained model can form the basic planning principles and allow for the optimization of the search for the optimal strategy with which to control the experiment when it is used as a training environment to provide different abstraction levels of system state reactions.
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Affiliation(s)
- Dmitry S. Polyanichenko
- The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia; (B.O.P.); (N.V.E.); (O.O.K.)
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14
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Rajan A, Pushkar AP, Dharmalingam BC, Varghese JJ. Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling. iScience 2023; 26:107029. [PMID: 37360694 PMCID: PMC10285649 DOI: 10.1016/j.isci.2023.107029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.
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Affiliation(s)
- Ajin Rajan
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Anoop P. Pushkar
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Balaji C. Dharmalingam
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Jithin John Varghese
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
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15
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Chen H, Zheng Y, Li J, Li L, Wang X. AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS). ACS NANO 2023. [PMID: 37267448 DOI: 10.1021/acsnano.3c01062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, and nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth in data, the adoption and exploitation of artificial intelligence (AI) as part of the materials research framework have had a tremendous impact on the development of nanomaterials. AI has enabled revolutionary next-generation paradigms to significantly accelerate all stages of material discovery and facilitate the exploration of the enormous design space. In this review, we summarize recent advancements of AI applications in nanomaterials discovery, with a special emphasis on the selected applications of AI and nanotechnology for the net-zero emission future including the development of solar cells, hydrogen energy, battery materials for renewable energy, and CO2 capture and conversion materials for carbon capture, utilization and storage (CCUS) technologies. In addition, we discuss the limitations and challenges of current AI applications in this area by identifying the gaps that exist in current development. Finally, we present the prospect for future research directions in order to facilitate the large-scale applications of artificial intelligence for advancements in nanomaterials.
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Affiliation(s)
- Honghao Chen
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yingzhe Zheng
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Jiali Li
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Lanyu Li
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
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16
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Takahashi K, Takahashi L. Toward the Golden Age of Materials Informatics: Perspective and Opportunities. J Phys Chem Lett 2023; 14:4726-4733. [PMID: 37172318 DOI: 10.1021/acs.jpclett.3c00648] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
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17
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Liu J, Zhao X, Zhao K, Goncharov VG, Delhommelle J, Lin J, Guo X. A modulated fingerprint assisted machine learning method for retrieving elastic moduli from resonant ultrasound spectroscopy. Sci Rep 2023; 13:5919. [PMID: 37041266 PMCID: PMC10090122 DOI: 10.1038/s41598-023-33046-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/06/2023] [Indexed: 04/13/2023] Open
Abstract
We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium-aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximum of 26% missing frequencies. In summary, our modulated fingerprint method is an efficient tool to transform raw spectroscopy data and train neural network models with high accuracy and resistance to spectra distortion.
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Affiliation(s)
- Juejing Liu
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
- School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA
| | - Xiaodong Zhao
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
| | - Ke Zhao
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
| | - Vitaliy G Goncharov
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA
- School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA
| | - Jerome Delhommelle
- Department of Chemistry, University of Massachusetts, Lowell, MA, 01854, USA
| | - Jian Lin
- School of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Xiaofeng Guo
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA.
- Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA.
- School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA.
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18
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Rossi K. What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO 2 reduction? A data-driven retrospective. Phys Chem Chem Phys 2023; 25:6867-6876. [PMID: 36799456 DOI: 10.1039/d2cp04576a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO2 reduction reaction products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2 reduction reactions trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2 reduction reaction measurements, which result in quantitative disagreement between experiments.
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Affiliation(s)
- Kevin Rossi
- Institut des sciences et ingénierie chimiques, École Polytechnique Fédérale de Lausanne, 1950 Sion, Switzerland.
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19
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Vladyka A, Sahle CJ, Niskanen J. Towards structural reconstruction from X-ray spectra. Phys Chem Chem Phys 2023; 25:6707-6713. [PMID: 36804587 DOI: 10.1039/d2cp05420e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO2 at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the Kβ'' and Kβ2 peaks in the spectrum from the Coulomb matrix descriptor with a training set of ∼ 104 samples. Spectral-significance-guided dimensionality reduction techniques allow us to construct an approximate inverse mapping from spectral moments to pseudo-Coulomb matrices. When applying this to the moments of the ensemble-mean spectrum, we obtain distances from the active site that match closely to those of the ensemble mean and which moreover reproduce the pressure-induced coordination change in amorphous GeO2. With this approach utilizing emulator-based component analysis, we are able to filter out the artificially complete structural information available from simulated snapshots, and quantitatively analyse structural changes that can be inferred from the changes in the Kβ emission spectrum alone.
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Affiliation(s)
- Anton Vladyka
- University of Turku, Department of Physics and Astronomy, 20014 Turun yliopisto, Finland.
| | - Christoph J Sahle
- European Synchrotron Radiation Source, 71 Avenue des Martyrs, 38000 Grenoble, France.
| | - Johannes Niskanen
- University of Turku, Department of Physics and Astronomy, 20014 Turun yliopisto, Finland.
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20
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Timoshenko J, Haase FT, Saddeler S, Rüscher M, Jeon HS, Herzog A, Hejral U, Bergmann A, Schulz S, Roldan Cuenya B. Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning. J Am Chem Soc 2023; 145:4065-4080. [PMID: 36762901 PMCID: PMC9951215 DOI: 10.1021/jacs.2c11824] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Indexed: 02/11/2023]
Abstract
Bimetallic transition-metal oxides, such as spinel-like CoxFe3-xO4 materials, are known as attractive catalysts for the oxygen evolution reaction (OER) in alkaline electrolytes. Nonetheless, unveiling the real active species and active states in these catalysts remains a challenge. The coexistence of metal ions in different chemical states and in different chemical environments, including disordered X-ray amorphous phases that all evolve under reaction conditions, hinders the application of common operando techniques. Here, we address this issue by relying on operando quick X-ray absorption fine structure spectroscopy, coupled with unsupervised and supervised machine learning methods. We use principal component analysis to understand the subtle changes in the X-ray absorption near-edge structure spectra and develop an artificial neural network to decipher the extended X-ray absorption fine structure spectra. This allows us to separately track the evolution of tetrahedrally and octahedrally coordinated species and to disentangle the chemical changes and several phase transitions taking place in CoxFe3-xO4 catalysts and on their active surface, related to the conversion of disordered oxides into spinel-like structures, transformation of spinels into active oxyhydroxides, and changes in the degree of spinel inversion in the course of the activation treatment and under OER conditions. By correlating the revealed structural changes with the distinct catalytic activity for a series of CoxFe3-xO4 samples, we elucidate the active species and OER mechanism.
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Affiliation(s)
- Janis Timoshenko
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Felix T. Haase
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Sascha Saddeler
- Institute
of Inorganic Chemistry and Center for Nanointegration Duisburg-Essen
(CENIDE), University of Duisburg-Essen, 45117 Essen, Germany
| | - Martina Rüscher
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Hyo Sang Jeon
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Antonia Herzog
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Uta Hejral
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Arno Bergmann
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Stephan Schulz
- Institute
of Inorganic Chemistry and Center for Nanointegration Duisburg-Essen
(CENIDE), University of Duisburg-Essen, 45117 Essen, Germany
| | - Beatriz Roldan Cuenya
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
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21
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Li H, Jiao Y, Davey K, Qiao SZ. Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angew Chem Int Ed Engl 2023; 62:e202216383. [PMID: 36509704 DOI: 10.1002/anie.202216383] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.
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Affiliation(s)
- Haobo Li
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yan Jiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Kenneth Davey
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Shi-Zhang Qiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
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22
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Qi X, Hu Y, Wang R, Yang Y, Zhao Y. Recent Advance of Machine Learning in Selecting New Materials. ACTA CHIMICA SINICA 2023. [DOI: 10.6023/a22110446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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23
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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24
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Genz NS, Kallio A, Oord R, Krumeich F, Pokle A, Prytz Ø, Olsbye U, Meirer F, Huotari S, Weckhuysen BM. Operando Laboratory-Based Multi-Edge X-Ray Absorption Near-Edge Spectroscopy of Solid Catalysts. Angew Chem Int Ed Engl 2022; 61:e202209334. [PMID: 36205032 PMCID: PMC9828672 DOI: 10.1002/anie.202209334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Indexed: 11/19/2022]
Abstract
Laboratory-based X-ray absorption spectroscopy (XAS) and especially X-ray absorption near-edge structure (XANES) offers new opportunities in catalyst characterization and presents not only an alternative, but also a complementary approach to precious beamtime at synchrotron facilities. We successfully designed a laboratory-based setup for performing operando, quasi-simultaneous XANES analysis at multiple K-edges, more specifically, operando XANES of mono-, bi-, and trimetallic CO2 hydrogenation catalysts containing Ni, Fe, and Cu. Detailed operando XANES studies of the multielement solid catalysts revealed metal-dependent differences in the reducibility and re-oxidation behavior and their influence on the catalytic performance in CO2 hydrogenation. The applicability of operando laboratory-based XANES at multiple K-edges paves the way for advanced multielement catalyst characterization complementing detailed studies at synchrotron facilities.
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Affiliation(s)
- Nina S. Genz
- Inorganic Chemistry and Catalysis groupDepartment of ChemistryUtrecht UniversityUniversiteitsweg 993584 CGUtrechtThe Netherlands
| | - Antti‐Jussi Kallio
- Department of PhysicsUniversity of HelsinkiP. O. Box 6400014HelsinkiFinland
| | - Ramon Oord
- Inorganic Chemistry and Catalysis groupDepartment of ChemistryUtrecht UniversityUniversiteitsweg 993584 CGUtrechtThe Netherlands
| | - Frank Krumeich
- Laboratory of Inorganic ChemistryDepartment of ChemistryETH ZürichVladimir-Prelog-Weg 18093ZürichSwitzerland
| | - Anuj Pokle
- Department of PhysicsCenter for Materials Science and NanotechnologyUniversity of OsloP.O. Box 10480316OsloNorway
| | - Øystein Prytz
- Department of PhysicsCenter for Materials Science and NanotechnologyUniversity of OsloP.O. Box 10480316OsloNorway
| | - Unni Olsbye
- Department of ChemistryUniversity of OsloP.O. Box 10330315OsloNorway
| | - Florian Meirer
- Inorganic Chemistry and Catalysis groupDepartment of ChemistryUtrecht UniversityUniversiteitsweg 993584 CGUtrechtThe Netherlands
| | - Simo Huotari
- Department of PhysicsUniversity of HelsinkiP. O. Box 6400014HelsinkiFinland
| | - Bert M. Weckhuysen
- Inorganic Chemistry and Catalysis groupDepartment of ChemistryUtrecht UniversityUniversiteitsweg 993584 CGUtrechtThe Netherlands
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25
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Sarma BB, Maurer F, Doronkin DE, Grunwaldt JD. Design of Single-Atom Catalysts and Tracking Their Fate Using Operando and Advanced X-ray Spectroscopic Tools. Chem Rev 2022; 123:379-444. [PMID: 36418229 PMCID: PMC9837826 DOI: 10.1021/acs.chemrev.2c00495] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The potential of operando X-ray techniques for following the structure, fate, and active site of single-atom catalysts (SACs) is highlighted with emphasis on a synergetic approach of both topics. X-ray absorption spectroscopy (XAS) and related X-ray techniques have become fascinating tools to characterize solids and they can be applied to almost all the transition metals deriving information about the symmetry, oxidation state, local coordination, and many more structural and electronic properties. SACs, a newly coined concept, recently gained much attention in the field of heterogeneous catalysis. In this way, one can achieve a minimum use of the metal, theoretically highest efficiency, and the design of only one active site-so-called single site catalysts. While single sites are not easy to characterize especially under operating conditions, XAS as local probe together with complementary methods (infrared spectroscopy, electron microscopy) is ideal in this research area to prove the structure of these sites and the dynamic changes during reaction. In this review, starting from their fundamentals, various techniques related to conventional XAS and X-ray photon in/out techniques applied to single sites are discussed with detailed mechanistic and in situ/operando studies. We systematically summarize the design strategies of SACs and outline their exploration with XAS supported by density functional theory (DFT) calculations and recent machine learning tools.
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Affiliation(s)
- Bidyut Bikash Sarma
- Institute
for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, 76131 Karlsruhe, Germany,Institute
of Catalysis Research and Technology, Karlsruhe
Institute of Technology, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, Germany,
| | - Florian Maurer
- Institute
for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, 76131 Karlsruhe, Germany
| | - Dmitry E. Doronkin
- Institute
for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, 76131 Karlsruhe, Germany,Institute
of Catalysis Research and Technology, Karlsruhe
Institute of Technology, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, Germany
| | - Jan-Dierk Grunwaldt
- Institute
for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstraße 20, 76131 Karlsruhe, Germany,Institute
of Catalysis Research and Technology, Karlsruhe
Institute of Technology, Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, Germany,
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26
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Li G, Marinkovic N, Wang B, Komarneni MR, Resasco DE. Manipulating the Microenvironment of Surfactant-Encapsulated Pt Nanoparticles to Promote Activity and Selectivity. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Gengnan Li
- School of Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma73019, United States
| | - Nebojsa Marinkovic
- Synchrotron Catalysis Consortium and Department of Chemical Engineering, Columbia University, New York, New York10027, United States
| | - Bin Wang
- School of Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma73019, United States
| | - Mallikharjuna Rao Komarneni
- School of Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma73019, United States
| | - Daniel E. Resasco
- School of Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma73019, United States
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27
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Rana R, Vila FD, Kulkarni AR, Bare SR. Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Rachita Rana
- Department of Chemical Engineering, University of California, Davis, California95616, United States
| | - Fernando D. Vila
- Department of Physics, University of Washington, Seattle, Washington98195, United States
| | - Ambarish R. Kulkarni
- Department of Chemical Engineering, University of California, Davis, California95616, United States
| | - Simon R. Bare
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California94025, United States
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28
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Tetef S, Kashyap V, Holden WM, Velian A, Govind N, Seidler GT. Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy. J Phys Chem A 2022; 126:4862-4872. [PMID: 35839329 DOI: 10.1021/acs.jpca.2c03635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We analyze an ensemble of organophosphorus compounds to form an unbiased characterization of the information encoded in their X-ray absorption near-edge structure (XANES) and valence-to-core X-ray emission spectra (VtC-XES). Data-driven emergence of chemical classes via unsupervised machine learning, specifically cluster analysis in the Uniform Manifold Approximation and Projection (UMAP) embedding, finds spectral sensitivity to coordination, oxidation, aromaticity, intramolecular hydrogen bonding, and ligand identity. Subsequently, we implement supervised machine learning via Gaussian process classifiers to identify confidence in predictions that match our initial qualitative assessments of clustering. The results further support the benefit of utilizing unsupervised machine learning as a precursor to supervised machine learning, which we term Unsupervised Validation of Classes (UVC), a result that goes beyond the present case of X-ray spectroscopies.
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Affiliation(s)
- Samantha Tetef
- Department of Physics, University of Washington, Seattle, Washington 98195, United States
| | - Vikram Kashyap
- Department of Physics, University of Washington, Seattle, Washington 98195, United States
| | - William M Holden
- Department of Physics, University of Washington, Seattle, Washington 98195, United States
| | - Alexandra Velian
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Niranjan Govind
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Gerald T Seidler
- Department of Physics, University of Washington, Seattle, Washington 98195, United States
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29
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Niskanen J, Vladyka A, Niemi J, Sahle C. Emulator-based decomposition for structural sensitivity of core-level spectra. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220093. [PMID: 35706659 PMCID: PMC9174725 DOI: 10.1098/rsos.220093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 05/09/2022] [Indexed: 05/03/2023]
Abstract
We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least-squares fitting and the observed behaviour is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.
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Affiliation(s)
- J. Niskanen
- Department of Physics and Astronomy, University of Turku, 20014 Turun yliopisto, Finland
| | - A. Vladyka
- Department of Physics and Astronomy, University of Turku, 20014 Turun yliopisto, Finland
| | - J. Niemi
- Department of Physics and Astronomy, University of Turku, 20014 Turun yliopisto, Finland
| | - C.J. Sahle
- European Synchrotron Radiation Source, 71 Avenue des Martyrs, 38000 Grenoble, France
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30
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Poths P, Alexandrova AN. Theoretical Perspective on Operando Spectroscopy of Fluxional Nanocatalysts. J Phys Chem Lett 2022; 13:4321-4334. [PMID: 35536346 DOI: 10.1021/acs.jpclett.2c00628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Improvements in operando spectroscopy have enabled the catalysis community to investigate the dynamic nature of catalysts under operating conditions with increasing detail. Still, the highly dynamic nature of some catalysts, such as fluxional supported subnano clusters, presents a formidable challenge even for the most state-of-the-art techniques. The reason is that such fluxional catalytic interfaces contain a variety of thermally accessible states. Operando spectroscopies used in catalysis generally fall into two categories: ensemble-based techniques, which provide spectra containing the signals of the entire ensemble of states of the catalyst and are not necessarily dominated by the most active species, and localized techniques, which provide atomistic-level information about the dynamics of active sites in a very small area, which might not include the most active species. Combining many different kinds of techniques can provide detailed insight; however, we propose that effective utilization of specific computational techniques and approaches within the fluxionality paradigm can fill the gap and enable atomistic characterization of the most relevant catalytic sites.
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Affiliation(s)
- Patricia Poths
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United States
| | - Anastassia N Alexandrova
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United States
- California NanoSystems Institute, Los Angeles, California 90095, United States
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31
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Lv H, Chen X. Intelligent control of nanoparticle synthesis through machine learning. NANOSCALE 2022; 14:6688-6708. [PMID: 35450983 DOI: 10.1039/d2nr00124a] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined by factors such as their size, shape and surface chemistry. In order for the synthesized nanoparticles to have functions suitable for different fields (for example, optics, electronics, sensor applications and so on), precise control of their properties is essential. However, with the current technology of preparing nanoparticles on a microreactor, it is time-consuming and laborious to achieve precise synthesis. In order to improve the efficiency of synthesizing nanoparticles with the expected functionality, the application of machine learning-assisted synthesis is an intelligent choice. In this article, we mainly introduce the typical methods of preparing nanoparticles on microreactors, and explain the principles and procedures of machine learning, as well as the main ways of obtaining data sets. We have studied three types of representative nanoparticle preparation methods assisted by machine learning. Finally, the current problems in machine learning-assisted nanoparticle synthesis and future development prospects are discussed.
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Affiliation(s)
- Honglin Lv
- College of Transportation, Ludong University, Yantai, Shandong 264025, China.
| | - Xueye Chen
- College of Transportation, Ludong University, Yantai, Shandong 264025, China.
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32
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Vottero E, Carosso M, Ricchebuono A, Jiménez-Ruiz M, Pellegrini R, Chizallet C, Raybaud P, Groppo E, Piovano A. Evidence for H 2-Induced Ductility in a Pt/Al 2O 3 Catalyst. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Eleonora Vottero
- Department of Chemistry, INSTM and NIS Centre, University of Torino, Via Quarello 15, I-10135 Torino, Italy
- Institut Laue-Langevin (ILL), 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Michele Carosso
- Department of Chemistry, INSTM and NIS Centre, University of Torino, Via Quarello 15, I-10135 Torino, Italy
| | - Alberto Ricchebuono
- Department of Chemistry, INSTM and NIS Centre, University of Torino, Via Quarello 15, I-10135 Torino, Italy
| | | | - Riccardo Pellegrini
- Chimet SpA - Catalyst Division, Via di Pescaiola 74, I-52041 Viciomaggio Arezzo, Italy
| | - Céline Chizallet
- IFP Energies nouvelles, Rond-point de L’Échangeur de Solaize, BP3-69360 Solaize, France
| | - Pascal Raybaud
- IFP Energies nouvelles, Rond-point de L’Échangeur de Solaize, BP3-69360 Solaize, France
| | - Elena Groppo
- Department of Chemistry, INSTM and NIS Centre, University of Torino, Via Quarello 15, I-10135 Torino, Italy
| | - Andrea Piovano
- Institut Laue-Langevin (ILL), 71 Avenue des Martyrs, 38042 Grenoble, France
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33
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Rankine CD, Penfold TJ. Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network. J Chem Phys 2022; 156:164102. [PMID: 35490005 DOI: 10.1063/5.0087255] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The affordable, accurate, and generalizable prediction of spectroscopic observables plays a key role in the analysis of increasingly complex experiments. In this article, we develop and deploy a deep neural network-XANESNET-for predicting the lineshape of first-row transition metal K-edge x-ray absorption near-edge structure (XANES) spectra. XANESNET predicts the spectral intensities using only information about the local coordination geometry of the transition metal complexes encoded in a feature vector of weighted atom-centered symmetry functions. We address in detail the calibration of the feature vector for the particularities of the problem at hand, and we explore the individual feature importance to reveal the physical insight that XANESNET obtains at the Fe K-edge. XANESNET relies on only a few judiciously selected features-radial information on the first and second coordination shells suffices along with angular information sufficient to separate satisfactorily key coordination geometries. The feature importance is found to reflect the XANES spectral window under consideration and is consistent with the expected underlying physics. We subsequently apply XANESNET at nine first-row transition metal (Ti-Zn) K-edges. It can be optimized in as little as a minute, predicts instantaneously, and provides K-edge XANES spectra with an average accuracy of ∼±2%-4% in which the positions of prominent peaks are matched with a >90% hit rate to sub-eV (∼0.8 eV) error.
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Affiliation(s)
- C D Rankine
- Chemistry-School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, United Kingdom
| | - T J Penfold
- Chemistry-School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, United Kingdom
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34
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Watson L, Rankine CD, Penfold TJ. Beyond structural insight: a deep neural network for the prediction of Pt L 2/3-edge X-ray absorption spectra. Phys Chem Chem Phys 2022; 24:9156-9167. [PMID: 35393987 DOI: 10.1039/d2cp00567k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
X-ray absorption spectroscopy at the L2/3 edge can be used to obtain detailed information about the local electronic and geometric structure of transition metal complexes. By virtue of the dipole selection rules, the transition metal L2/3 edge usually exhibits two distinct spectral regions: (i) the "white line", which is dominated by bound electronic transitions from metal-centred 2p orbitals into unoccupied orbitals with d character; the intensity and shape of this band consequently reflects the d density of states (d-DOS), which is strongly modulated by mixing with ligand orbitals involved in chemical bonding, and (ii) the post-edge, where oscillations encode the local geometric structure around the X-ray absorption site. In this Article, we extend our recently-developed XANESNET deep neural network (DNN) beyond the K-edge to predict X-ray absorption spectra at the Pt L2/3 edge. We demonstrate that XANESNET is able to predict Pt L2/3 -edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in practical situations is demonstrated by application to two Pt complexes, and by simulating the transient spectrum of a photoexcited dimeric Pt complex. Our discussion includes an analysis of the feature importance in our DNN which demonstrates the role of key features and assists with interpreting the performance of the network.
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Affiliation(s)
- Luke Watson
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle, upon Tyne, NE1 7RU, UK.
| | - Conor D Rankine
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle, upon Tyne, NE1 7RU, UK.
| | - Thomas J Penfold
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle, upon Tyne, NE1 7RU, UK.
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35
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Lee JD, Miller JB, Shneidman AV, Sun L, Weaver JF, Aizenberg J, Biener J, Boscoboinik JA, Foucher AC, Frenkel AI, van der Hoeven JES, Kozinsky B, Marcella N, Montemore MM, Ngan HT, O'Connor CR, Owen CJ, Stacchiola DJ, Stach EA, Madix RJ, Sautet P, Friend CM. Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites. Chem Rev 2022; 122:8758-8808. [PMID: 35254051 DOI: 10.1021/acs.chemrev.1c00967] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.
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Affiliation(s)
- Jennifer D Lee
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Jeffrey B Miller
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Anna V Shneidman
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Lixin Sun
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Jason F Weaver
- Department of Chemical Engineering, University of Florida, Gainesville, Florida 32611, United States
| | - Joanna Aizenberg
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States.,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Juergen Biener
- Nanoscale Synthesis and Characterization Laboratory, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - J Anibal Boscoboinik
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Alexandre C Foucher
- Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Anatoly I Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States.,Division of Chemistry, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Jessi E S van der Hoeven
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States.,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Boris Kozinsky
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Nicholas Marcella
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
| | - Matthew M Montemore
- Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Hio Tong Ngan
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Christopher R O'Connor
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Cameron J Owen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States.,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Dario J Stacchiola
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Eric A Stach
- Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Robert J Madix
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Philippe Sautet
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States.,Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Cynthia M Friend
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States.,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
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36
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Operando Photo-Electrochemical Catalysts Synchrotron Studies. NANOMATERIALS 2022; 12:nano12050839. [PMID: 35269331 PMCID: PMC8912469 DOI: 10.3390/nano12050839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/25/2022] [Accepted: 02/27/2022] [Indexed: 01/27/2023]
Abstract
The attempts to develop efficient methods of solar energy conversion into chemical fuel are ongoing amid climate changes associated with global warming. Photo-electrocatalytic (PEC) water splitting and CO2 reduction reactions show high potential to tackle this challenge. However, the development of economically feasible solutions of PEC solar energy conversion requires novel efficient and stable earth-abundant nanostructured materials. The latter are hardly available without detailed understanding of the local atomic and electronic structure dynamics and mechanisms of the processes occurring during chemical reactions on the catalyst–electrolyte interface. This review considers recent efforts to study photo-electrocatalytic reactions using in situ and operando synchrotron spectroscopies. Particular attention is paid to the operando reaction mechanisms, which were established using X-ray Absorption (XAS) and X-ray Photoelectron (XPS) Spectroscopies. Operando cells that are needed to perform such experiments on synchrotron are covered. Classical and modern theoretical approaches to extract structural information from X-ray Absorption Near-Edge Structure (XANES) spectra are discussed.
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37
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Kiyohara S, Kikumasa K, Shibata K, Mizoguchi T. Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised learning. Ultramicroscopy 2022; 233:113438. [PMID: 34915289 DOI: 10.1016/j.ultramic.2021.113438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/19/2021] [Accepted: 11/27/2021] [Indexed: 11/20/2022]
Abstract
Spectroscopy is widely used for the analysis of chemical, vibrational, and bonding information. Interpretations of the spectral features have been performed by comparing the objective spectra with reference spectra from experiments or simulations. However, the interpretation process by humans is not always straightforward, especially for spectra obtained from unknown or new materials. In the present study, we developed a method using machine learning techniques to obtain human-like interpretation automatically. We combined unsupervised and supervised learning methods; then applied it to the spectrum database which includes more than 400 spectra of water and organic molecules containing various ligands and chemical bonds. The proposed method has successfully found the correlations between the spectral features and descriptors of the atoms, bonds, and ligands. We demonstrated that the proposed method enabled the automatic determination of reasonable spectrum-structure relationships such as between π* resonance in C-K edges and multiple bonds. The proposed method enables the automatic determination of physically and chemically reasonable spectrum-structure relationships without arbitrariness in data-driven manner, which is considerably difficult only with simulation or conventional machine leaning techniques. Such relationships are useful for understanding what structural parameters cause changes in the spectrum, providing a way for the better interpretation of spatial distributed or time evolutionary data. Furthermore, although the present work focused on the ELNES/XANES spectrum from small organic molecules, the proposed method can be readily extended to other spectral data. It is expected to contribute to a better understanding of the spectrum-structure relationship in various spectroscopy applications.
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Affiliation(s)
- Shin Kiyohara
- Institute of Industrial Science, the University of Tokyo, Tokyo 153-8505, Japan; Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan.
| | - Kakeru Kikumasa
- Institute of Industrial Science, the University of Tokyo, Tokyo 153-8505, Japan
| | - Kiyou Shibata
- Institute of Industrial Science, the University of Tokyo, Tokyo 153-8505, Japan
| | - Teruyasu Mizoguchi
- Institute of Industrial Science, the University of Tokyo, Tokyo 153-8505, Japan
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38
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Xiang S, Huang P, Li J, Liu Y, Marcella N, Routh PK, Li G, Frenkel AI. Solving the structure of "single-atom" catalysts using machine learning - assisted XANES analysis. Phys Chem Chem Phys 2022; 24:5116-5124. [PMID: 35156671 DOI: 10.1039/d1cp05513e] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
"Single-atom" catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment, thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.
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Affiliation(s)
- Shuting Xiang
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Peipei Huang
- Department of Chemistry, University of New Hampshire, Durham, New Hampshire 03824, USA.
| | - Junying Li
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Yang Liu
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Nicholas Marcella
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Prahlad K Routh
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Gonghu Li
- Department of Chemistry, University of New Hampshire, Durham, New Hampshire 03824, USA.
| | - Anatoly I Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA. .,Chemistry Division, Brookhaven National Laboratory, Upton, New York 11973, USA
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39
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Marcella N, Lim JS, Płonka AM, Yan G, Owen CJ, van der Hoeven JES, Foucher AC, Ngan HT, Torrisi SB, Marinkovic NS, Stach EA, Weaver JF, Aizenberg J, Sautet P, Kozinsky B, Frenkel AI. Decoding reactive structures in dilute alloy catalysts. Nat Commun 2022; 13:832. [PMID: 35149699 PMCID: PMC8837610 DOI: 10.1038/s41467-022-28366-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/04/2022] [Indexed: 11/09/2022] Open
Abstract
Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics require atomic level knowledge of the active sites. These structures often change dynamically during reactions and are difficult to decipher. A prototypical example is the hydrogen-deuterium exchange reaction catalyzed by dilute Pd-in-Au alloy nanoparticles. From a combination of catalytic activity measurements, machine learning-enabled spectroscopic analysis, and first-principles based kinetic modeling, we demonstrate that the active species are surface Pd ensembles containing only a few (from 1 to 3) Pd atoms. These species simultaneously explain the observed X-ray spectra and equate the experimental and theoretical values of the apparent activation energy. Remarkably, we find that the catalytic activity can be tuned on demand by controlling the size of the Pd ensembles through catalyst pretreatment. Our data-driven multimodal approach enables decoding of reactive structures in complex and dynamic alloy catalysts. Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Here the authors report a data-driven approach for understanding catalytic reactions mechanisms in dilute bimetallic catalysts by combining X-ray absorption spectroscopy with activity studies and kinetic modeling.
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Affiliation(s)
- Nicholas Marcella
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jin Soo Lim
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Anna M Płonka
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - George Yan
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Cameron J Owen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Jessi E S van der Hoeven
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Alexandre C Foucher
- Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hio Tong Ngan
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Steven B Torrisi
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA
| | - Nebojsa S Marinkovic
- Department of Chemical Engineering, Columbia University, New York, NY, 10027, USA
| | - Eric A Stach
- Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jason F Weaver
- Department of Chemical Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Joanna Aizenberg
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Philippe Sautet
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Boris Kozinsky
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA. .,Robert Bosch LLC, Research and Technology Center, Cambridge, MA, 02139, USA.
| | - Anatoly I Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. .,Chemistry Division, Brookhaven National Laboratory, Upton, NY, 11973, USA.
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40
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Guan Y, Chaffart D, Liu G, Tan Z, Zhang D, Wang Y, Li J, Ricardez-Sandoval L. Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117224] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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41
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Tereshchenko A, Pashkov D, Guda A, Guda S, Rusalev Y, Soldatov A. Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27020357. [PMID: 35056671 PMCID: PMC8780420 DOI: 10.3390/molecules27020357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 01/08/2023]
Abstract
Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface-adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd-C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd55 nanocluster and extended it to new geometries, Pd79, and Pd85, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.
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Affiliation(s)
- Andrei Tereshchenko
- The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, Russia; (D.P.); (A.G.); (S.G.); (Y.R.); (A.S.)
- Correspondence:
| | - Danil Pashkov
- The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, Russia; (D.P.); (A.G.); (S.G.); (Y.R.); (A.S.)
- Vorovich Institute of Mathematics, Mechanics, and Computer Sciences, Southern Federal University, 344058 Rostov-on-Don, Russia
| | - Alexander Guda
- The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, Russia; (D.P.); (A.G.); (S.G.); (Y.R.); (A.S.)
| | - Sergey Guda
- The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, Russia; (D.P.); (A.G.); (S.G.); (Y.R.); (A.S.)
- Vorovich Institute of Mathematics, Mechanics, and Computer Sciences, Southern Federal University, 344058 Rostov-on-Don, Russia
| | - Yury Rusalev
- The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, Russia; (D.P.); (A.G.); (S.G.); (Y.R.); (A.S.)
| | - Alexander Soldatov
- The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, Russia; (D.P.); (A.G.); (S.G.); (Y.R.); (A.S.)
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42
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Sensitive recognition of Shiga toxin using biosensor technology: An efficient platform towards bioanalysis of pathogenic bacterial. Microchem J 2022. [DOI: 10.1016/j.microc.2021.106900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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43
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Fujita M, Yamamoto A, Tsuchiya N, Yoshida H. Hydrogen Adsorption/Desorption Isotherms on Supported Platinum Nanoparticles Determined by in‐situ XAS and ΔXANES Analysis. ChemCatChem 2021. [DOI: 10.1002/cctc.202101709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Masami Fujita
- Kyoto University: Kyoto Daigaku Graduate School of Human and Environmental Studies 606-8501 Kyoto JAPAN
| | - Akira Yamamoto
- Kyoto University: Kyoto Daigaku Graduate School of Human and Environmental Studies #219 Building 2, Yoshida South Campus, Yoshida-Nihonmatsu-cho, Sakyo-ku 606-8501 Kyoto JAPAN
| | - Naoki Tsuchiya
- Kyoto University: Kyoto Daigaku Graduate School of Human and Environmental Studies 606-8501 Kyoto JAPAN
| | - Hisao Yoshida
- Kyoto University: Kyoto Daigaku Graduate School of Human and Environmental Studies 606-8501 Kyoto JAPAN
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44
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Perras FA, Kanbur U, Paterson AL, Chatterjee P, Slowing II, Sadow AD. Determining the Three-Dimensional Structures of Silica-Supported Metal Complexes from the Ground Up. Inorg Chem 2021; 61:1067-1078. [PMID: 34962783 DOI: 10.1021/acs.inorgchem.1c03200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The immobilization of molecularly precise metal complexes to substrates, such as silica, provides an attractive platform for the design of active sites in heterogeneous catalysts. Specific steric and electronic variations of the ligand environment enable the development of structure-activity relationships and the knowledge-driven design of catalysts. At present, however, the three-dimensional environment of the precatalyst, much less the active site, is generally not known for heterogeneous single-site catalysts. We explored the degree to which NMR-based surface-to-complex interatomic distances could be used to solve the three-dimensional structures of three silica-supported metal complexes. The structure solution revealed unexpected features related to the environment around the metal that would be difficult to discern otherwise. This approach appears to be highly robust and, due to its simplicity, is readily applied to most single-site catalysts with little extra effort.
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Affiliation(s)
| | - Uddhav Kanbur
- US DOE, Ames Laboratory, Ames, Iowa 50011, United States.,Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | | | - Puranjan Chatterjee
- US DOE, Ames Laboratory, Ames, Iowa 50011, United States.,Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | - Igor I Slowing
- US DOE, Ames Laboratory, Ames, Iowa 50011, United States.,Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | - Aaron D Sadow
- US DOE, Ames Laboratory, Ames, Iowa 50011, United States.,Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
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45
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Movsesyan A, Besteiro LV, Wang Z, Govorov AO. Mie Sensing with Neural Networks: Recognition of Nano‐Object Parameters, the Invisibility Point, and Restricted Models. ADVANCED THEORY AND SIMULATIONS 2021. [DOI: 10.1002/adts.202100369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Artur Movsesyan
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China
- Department of Physics and Astronomy Ohio University Athens OH 45701 USA
| | | | - Zhiming Wang
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China
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46
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Liu Y, Halder A, Seifert S, Marcella N, Vajda S, Frenkel AI. Probing Active Sites in Cu xPd y Cluster Catalysts by Machine-Learning-Assisted X-ray Absorption Spectroscopy. ACS APPLIED MATERIALS & INTERFACES 2021; 13:53363-53374. [PMID: 34255469 DOI: 10.1021/acsami.1c06714] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Size-selected clusters are important model catalysts because of their narrow size and compositional distributions, as well as enhanced activity and selectivity in many reactions. Still, their structure-activity relationships are, in general, elusive. The main reason is the difficulty in identifying and quantitatively characterizing the catalytic active site in the clusters when it is confined within subnanometric dimensions and under the continuous structural changes the clusters can undergo in reaction conditions. Using machine learning approaches for analysis of the operando X-ray absorption near-edge structure spectra, we obtained accurate speciation of the CuxPdy cluster types during the propane oxidation reaction and the structural information about each type. As a result, we elucidated the information about active species and relative roles of Cu and Pd in the clusters.
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Affiliation(s)
- Yang Liu
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
| | - Avik Halder
- Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States
| | - Soenke Seifert
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States
| | - Nicholas Marcella
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
| | - Stefan Vajda
- Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States
- Institute for Molecular Engineering, The University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Nanocatalysis, J. Heyrovský Institute of Physical Chemistry, Czech Academy of Sciences, Prague 8 18223, Czech Republic
| | - Anatoly I Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
- Chemistry Division, Brookhaven National Laboratory, Upton, New York 11973, United States
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47
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Tetef S, Govind N, Seidler GT. Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy. Phys Chem Chem Phys 2021; 23:23586-23601. [PMID: 34651631 DOI: 10.1039/d1cp02903g] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
We report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e., the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.
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Affiliation(s)
- Samantha Tetef
- Department of Physics, University of Washington, Seattle, WA 98195, USA.
| | - Niranjan Govind
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Gerald T Seidler
- Department of Physics, University of Washington, Seattle, WA 98195, USA.
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48
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Rangel-Martinez D, Nigam K, Ricardez-Sandoval LA. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.08.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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49
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Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chem Rev 2021; 121:9816-9872. [PMID: 34232033 PMCID: PMC8391798 DOI: 10.1021/acs.chemrev.1c00107] [Citation(s) in RCA: 190] [Impact Index Per Article: 63.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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Affiliation(s)
- John A. Keith
- Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Valentin Vassilev-Galindo
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Bingqing Cheng
- Accelerate
Programme for Scientific Discovery, Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
| | - Stefan Chmiela
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea
- Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
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50
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Westermayr J, Maurer RJ. Physically inspired deep learning of molecular excitations and photoemission spectra. Chem Sci 2021; 12:10755-10764. [PMID: 34447563 PMCID: PMC8372319 DOI: 10.1039/d1sc01542g] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/29/2021] [Indexed: 12/29/2022] Open
Abstract
Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick Gibbet Hill Road Coventry CV4 7AL UK
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick Gibbet Hill Road Coventry CV4 7AL UK
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