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Wan K, He J, Shi X. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305758. [PMID: 37640376 DOI: 10.1002/adma.202305758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/24/2023] [Indexed: 08/31/2023]
Abstract
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.
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Affiliation(s)
- Kaiwei Wan
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jianxin He
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xinghua Shi
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
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2
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Li Z, Wang J, Yang C, Liu L, Yang JY. Thermal transport across TiO2-H2O interface involving water dissociation: Ab initio-assisted deep potential molecular dynamics. J Chem Phys 2023; 159:144701. [PMID: 37811827 DOI: 10.1063/5.0167238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
Water dissociation on TiO2 surfaces has been known for decades and holds great potential in various applications, many of which require a proper understanding of thermal transport across the TiO2-H2O interface. Molecular dynamics (MD) simulations play an important role in characterizing complex systems' interfacial thermal transport properties. Nevertheless, due to the imprecision of empirical force field potentials, the interfacial thermal transport mechanism involving water dissociation remains to be determined. To cope with this, a deep potential (DP) model is formulated through the utilization of ab initio datasets. This model successfully simulates interfacial thermal transport accompanied by water dissociation on the TiO2 surfaces. The trained DP achieves a total energy accuracy of ∼238.8 meV and a force accuracy of ∼197.05 meV/Å. The DPMD simulations show that water dissociation induces the formation of hydrogen bonding networks and molecular bridges. Structural modifications further affect interfacial thermal transport. The interfacial thermal conductance estimated by DP is ∼8.54 × 109 W/m2 K, smaller than ∼13.17 × 109 W/m2 K by empirical potentials. The vibrational density of states (VDOS) quantifies the differences between the DP model and empirical potentials. Notably, the VDOS disparity between the adsorbed hydrogen atoms and normal hydrogen atoms demonstrates the influence of water dissociation on heat transfer processes. This work aims to understand the effect of water dissociation on thermal transport at the TiO2-H2O interface. The findings will provide valuable guidance for the thermal management of photocatalytic devices.
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Affiliation(s)
- Zhiqiang Li
- Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong 266237, China
| | - Jian Wang
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Chao Yang
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Linhua Liu
- Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong 266237, China
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Jia-Yue Yang
- Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong 266237, China
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
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3
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Brezina K, Beck H, Marsalek O. Reducing the Cost of Neural Network Potential Generation for Reactive Molecular Systems. J Chem Theory Comput 2023; 19:6589-6604. [PMID: 37747971 PMCID: PMC10569056 DOI: 10.1021/acs.jctc.3c00391] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Indexed: 09/27/2023]
Abstract
Although machine learning potentials have recently had a substantial impact on molecular simulations, the construction of a robust training set can still become a limiting factor, especially due to the requirement of a reference ab initio simulation that covers all the relevant geometries of the system. Recognizing that this can be prohibitive for certain systems, we develop the method of transition tube sampling that mitigates the computational cost of training set and model generation. In this approach, we generate classical or quantum thermal geometries around a transition path describing a conformational change or a chemical reaction using only a sparse set of local normal mode expansions along this path and select from these geometries by an active learning protocol. This yields a training set with geometries that characterize the whole transition without the need for a costly reference trajectory. The performance of the method is evaluated on different molecular systems with the complexity of the potential energy landscape increasing from a single minimum to a double proton-transfer reaction with high barriers. Our results show that the method leads to training sets that give rise to models applicable in classical and path integral simulations alike that are on par with those based directly on ab initio calculations while providing the computational speedup we have come to expect from machine learning potentials.
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Affiliation(s)
- Krystof Brezina
- Charles University, Faculty of Mathematics
and Physics, Ke Karlovu
3, 121 16, Prague
2, Czech Republic
| | - Hubert Beck
- Charles University, Faculty of Mathematics
and Physics, Ke Karlovu
3, 121 16, Prague
2, Czech Republic
| | - Ondrej Marsalek
- Charles University, Faculty of Mathematics
and Physics, Ke Karlovu
3, 121 16, Prague
2, Czech Republic
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4
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Manzhos S, Ihara M. Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression. J Phys Chem A 2023; 127:7823-7835. [PMID: 37698519 DOI: 10.1021/acs.jpca.3c02949] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Feed-forward neural networks (NNs) are a staple machine learning method widely used in many areas of science and technology, including physical chemistry, computational chemistry, and materials informatics. While even a single-hidden-layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow the use of fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids nonlinear fitting of neural network parameters by defining them with rules. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of an NN. We demonstrate the approach by fitting the potential energy surfaces of the water molecule and formaldehyde. Without requiring any nonlinear optimization, the additive-GPR-based approach outperforms a conventional NN in the high-accuracy regime, where a conventional NN suffers more from overfitting.
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Affiliation(s)
- Sergei Manzhos
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Manabu Ihara
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
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5
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M V, Singh S, Bononi F, Andreussi O, Karmodak N. Thermodynamic and kinetic modeling of electrocatalytic reactions using a first-principles approach. J Chem Phys 2023; 159:111001. [PMID: 37728202 DOI: 10.1063/5.0165835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/28/2023] [Indexed: 09/21/2023] Open
Abstract
The computational modeling of electrochemical interfaces and their applications in electrocatalysis has attracted great attention in recent years. While tremendous progress has been made in this area, however, the accurate atomistic descriptions at the electrode/electrolyte interfaces remain a great challenge. The Computational Hydrogen Electrode (CHE) method and continuum modeling of the solvent and electrolyte interactions form the basis for most of these methodological developments. Several posterior corrections have been added to the CHE method to improve its accuracy and widen its applications. The most recently developed grand canonical potential approaches with the embedded diffuse layer models have shown considerable improvement in defining interfacial interactions at electrode/electrolyte interfaces over the state-of-the-art computational models for electrocatalysis. In this Review, we present an overview of these different computational models developed over the years to quantitatively probe the thermodynamics and kinetics of electrochemical reactions in the presence of an electrified catalyst surface under various electrochemical environments. We begin our discussion by giving a brief picture of the different continuum solvation approaches, implemented within the ab initio method to effectively model the solvent and electrolyte interactions. Next, we present the thermodynamic and kinetic modeling approaches to determine the activity and stability of the electrocatalysts. A few applications to these approaches are also discussed. We conclude by giving an outlook on the different machine learning models that have been integrated with the thermodynamic approaches to improve their efficiency and widen their applicability.
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Affiliation(s)
- Vasanthapandiyan M
- Department of Chemistry, Shiv Nadar Institution of Eminence, Dadri, Gautam Buddha Nagar, Uttar Pradesh 201314, India
| | - Shagun Singh
- Department of Chemistry, Shiv Nadar Institution of Eminence, Dadri, Gautam Buddha Nagar, Uttar Pradesh 201314, India
| | - Fernanda Bononi
- Department of Physics, University of North Texas, Denton, Texas 76203, USA
| | - Oliviero Andreussi
- Department of Chemistry and Biochemistry, Boise State University, Boise, Idaho 83725, USA
| | - Naiwrit Karmodak
- Department of Chemistry, Shiv Nadar Institution of Eminence, Dadri, Gautam Buddha Nagar, Uttar Pradesh 201314, India
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6
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Chen BWJ, Zhang X, Zhang J. Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials. Chem Sci 2023; 14:8338-8354. [PMID: 37564405 PMCID: PMC10411631 DOI: 10.1039/d3sc02482b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/11/2023] [Indexed: 08/12/2023] Open
Abstract
Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate ab initio MD simulations by up to 4 orders of magnitude while reproducing with high fidelity the geometrical features of water in the bulk and at metal-water interfaces. Using these ML-accelerated simulations, we accurately predicted key catalytic quantities such as the adsorption energies of CO*, OH*, COH*, HCO*, and OCCHO* on Cu surfaces and the free energy barriers of C-H scission of ethylene glycol over Cu and Pd surfaces, as validated with ab initio calculations. We envision that such simulations will pave the way towards detailed and realistic studies of solvated catalysts at large time- and length-scales.
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Affiliation(s)
- Benjamin W J Chen
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
| | - Xinglong Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
| | - Jia Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
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7
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Huang J, Zhang Y, Li M, Groß A, Sakong S. Comparing Ab Initio Molecular Dynamics and a Semiclassical Grand Canonical Scheme for the Electric Double Layer of the Pt(111)/Water Interface. J Phys Chem Lett 2023; 14:2354-2363. [PMID: 36848227 DOI: 10.1021/acs.jpclett.2c03892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The theoretical modeling of metal/water interfaces centers on an appropriate configuration of the electric double layer (EDL) under grand canonical conditions. In principle, ab initio molecular dynamics (AIMD) simulations would be the appropriate choice for treating the competing water-water and water-metal interactions and explicitly considering the atomic and electronic degrees of freedom. However, this approach only allows simulations of relatively small canonical ensembles over a limited period (shorter than 100 ps). On the other hand, computationally efficient semiclassical approaches can treat the EDL model based on a grand canonical scheme by averaging the microscopic details. Thus, an improved description of the EDL can be obtained by combining AIMD simulations and semiclassical methods based on a grand canonical scheme. By taking the Pt(111)/water interface as an example, we compare these approaches in terms of the electric field, water configuration, and double-layer capacitance. Furthermore, we discuss how the combined merits of the approaches can contribute to advances in EDL theory.
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Affiliation(s)
- Jun Huang
- Institute of Theoretical Chemistry, Ulm University, 89081 Ulm, Germany
- IEK-13, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Yufan Zhang
- IEK-13, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Mengru Li
- Institute of Theoretical Chemistry, Ulm University, 89081 Ulm, Germany
| | - Axel Groß
- Institute of Theoretical Chemistry, Ulm University, 89081 Ulm, Germany
- Electrochemical Energy Storage, Helmholtz Institute Ulm (HIU), 89069 Ulm, Germany
| | - Sung Sakong
- Institute of Theoretical Chemistry, Ulm University, 89081 Ulm, Germany
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8
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Zhou Y, Ouyang Y, Zhang Y, Li Q, Wang J. Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges. J Phys Chem Lett 2023; 14:2308-2316. [PMID: 36847421 DOI: 10.1021/acs.jpclett.2c03288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The electrochemical interface, where the adsorption of reactants and electrocatalytic reactions take place, has long been a focus of attention. Some of the important processes on it tend to possess relatively slow kinetic characteristics, which are usually beyond the scope of ab initio molecular dynamics. The newly emerging technique, machine learning methods, provides an alternative approach to achieve thousands of atoms and nanosecond time scale while ensuring precision and efficiency. In this Perspective, we summarize in detail the recent progress and achievements made by the introduction of machine learning to simulate electrochemical interfaces, and focus on the limitations of current machine learning models, such as accurate descriptions of long-range electrostatic interactions and the kinetics of the electrochemical reactions occurring at the interface. Finally, we further point out the future directions for machine learning to expand in the field of electrochemical interfaces.
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Affiliation(s)
- Yipeng Zhou
- School of Physics, Southeast University, Nanjing 211189, China
| | - Yixin Ouyang
- School of Physics, Southeast University, Nanjing 211189, China
| | - Yehui Zhang
- School of Physics, Southeast University, Nanjing 211189, China
| | - Qiang Li
- School of Physics, Southeast University, Nanjing 211189, China
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing 211189, China
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9
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Li Z, Tan X, Fu Z, Liu L, Yang JY. Thermal transport across copper-water interfaces according to deep potential molecular dynamics. Phys Chem Chem Phys 2023; 25:6746-6756. [PMID: 36807438 DOI: 10.1039/d2cp05530a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Nanoscale thermal transport at solid-liquid interfaces plays an essential role in many engineering fields. This work performs deep potential molecular dynamics (DPMD) simulations to investigate thermal transport across copper-water interfaces. Unlike traditional classical molecular dynamics (CMD) simulations, we independently train a deep learning potential (DLP) based on density functional theory (DFT) calculations and demonstrated its high computational efficiency and accuracy. The trained DLP predicts radial distribution functions (RDFs), vibrational densities of states (VDOS), density curves, and thermal conductivity of water confined in the nanochannel at a DFT accuracy. The thermal conductivity decreases slightly with an increase in the channel height, while the influence of the cross-sectional area is negligible. Moreover, the predicted interfacial thermal conductance (ITC) across the copper-water interface by DPMD is 2.505 × 108 W m-2 K-1, the same order of magnitude as the CMD and experimental results but with a high computational accuracy. This work seeks to simulate the thermal transport properties of solid-liquid interfaces with DFT accuracy at large-system and long-time scales.
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Affiliation(s)
- Zhiqiang Li
- Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong 266237, China.
| | - Xiaoyu Tan
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Zhiwei Fu
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China.,Science and Technology on Reliability Physics and Application of Electronic Component Laboratory, The 5th Electronics Research Institute of the Ministry of Industry and Information Technology, Guangzhou 511370, China
| | - Linhua Liu
- Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong 266237, China. .,School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Jia-Yue Yang
- Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong 266237, China. .,School of Energy and Power Engineering, Shandong University, Jinan, Shandong 250061, China
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10
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Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-023-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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11
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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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12
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Wu R, Wang L. Insight into the solvent effects on ethanol oxidation on Ir(100). Phys Chem Chem Phys 2023; 25:2190-2202. [PMID: 36594349 DOI: 10.1039/d2cp04899j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Solvent effects have always been a non-negligible factor for aqueous catalytic reactions, though few studies have been devoted towards the molecular understanding and impact of solvent effects on catalysis. In this work, we investigated ethanol dehydrogenation and C-C bond cleavage over Ir(100) in an aqueous solution using density functional theory calculations with both the implicit and explicit solvent models and transition state theory-based kinetics simulations. The results show that solvent polarization assists the α- and β-dehydrogenation of ethanol on Ir(100) in the aqueous solution and hydrogen bonding also assists the ethanol β-dehydrogenation and C-C bond cleavage in CH2CO. The hydrogen bond between the ethanol and water molecule hinders ethanol hydroxyl dehydrogenation while the CHCO⋯H2O hydrogen bond radically alters the adsorption configuration of CHCO, which leads to an increase in the C-C cleavage barrier by 2.5 fold. Furthermore, the solvent changes the reaction pathways significantly. In an aqueous solution, ethanol β-dehydrogenation on Ir(100) is the dominant ethanol dehydrogenation pathway and C-C bond cleavage occurs predominantly via CH2CO species.
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Affiliation(s)
- Ruitao Wu
- School of Chemical and Biomolecular Sciences and the Materials Technology Center, Southern Illinois University, Carbondale, Illinois 62901, USA.
| | - Lichang Wang
- School of Chemical and Biomolecular Sciences and the Materials Technology Center, Southern Illinois University, Carbondale, Illinois 62901, USA.
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13
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Zhang Y, Lin Q, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Yaolong Zhang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Qidong Lin
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Bin Jiang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
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14
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Hao H, Ruiz Pestana L, Qian J, Liu M, Xu Q, Head‐Gordon T. Chemical transformations and transport phenomena at interfaces. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Hongxia Hao
- Kenneth S. Pitzer Theory Center and Department of Chemistry University of California Berkeley California USA
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Luis Ruiz Pestana
- Department of Civil and Architectural Engineering University of Miami Coral Gables Florida USA
| | - Jin Qian
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Meili Liu
- Department of Civil and Architectural Engineering University of Miami Coral Gables Florida USA
| | - Qiang Xu
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Teresa Head‐Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry University of California Berkeley California USA
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
- Department of Bioengineering and Chemical and Biomolecular Engineering University of California Berkeley California USA
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15
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Zare M, Saleheen MS, Singh N, Uline MJ, Faheem M, Heyden A. Liquid-Phase Effects on Adsorption Processes in Heterogeneous Catalysis. JACS AU 2022; 2:2119-2134. [PMID: 36186571 PMCID: PMC9516566 DOI: 10.1021/jacsau.2c00389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/21/2022] [Accepted: 07/26/2022] [Indexed: 06/16/2023]
Abstract
Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values. Computational, implicit solvation models predict exergonic solvation effects for phenol adsorption, failing to agree with measurements even qualitatively. In this study, an explicit, hybrid quantum mechanical/molecular mechanical approach for computing solvation free energies of adsorption is developed, solvation free energies of phenol adsorption are computed, and experimental data for solvation free energies of phenol adsorption are reanalyzed using multiple adsorption isotherm models. Explicit solvation calculations predict an endergonic solvation free energy for phenol adsorption that agrees well with measurements to within the experimental and force field uncertainties. Computed adsorption free energies of solvation of carbon monoxide, ethylene glycol, benzene, and phenol over the (111) facet of Pt and Cu suggest that liquid water destabilizes all adsorbed species, with the largest impact on the largest adsorbates.
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Affiliation(s)
- Mehdi Zare
- Department
of Chemical Engineering, University of South
Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
| | - Mohammad S. Saleheen
- Department
of Chemical Engineering, University of South
Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
| | - Nirala Singh
- Department
of Chemical Engineering and Catalysis Science and Technology Institute, University of Michigan, Ann Arbor, Michigan 48109-2136, United States
| | - Mark J. Uline
- Department
of Chemical Engineering, University of South
Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
| | - Muhammad Faheem
- Department
of Chemical Engineering, University of South
Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
- Department
of Chemical Engineering, University of Engineering
& Technology, Lahore 54890, Pakistan
| | - Andreas Heyden
- Department
of Chemical Engineering, University of South
Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
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16
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Schienbein P, Blumberger J. Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials. Phys Chem Chem Phys 2022; 24:15365-15375. [PMID: 35703465 DOI: 10.1039/d2cp01708c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Metal oxide/water interfaces play an important role in biology, catalysis, energy storage and photocatalytic water splitting. The atomistic structure at these interfaces is often difficult to characterize by experimental techniques, whilst results from ab initio molecular dynamics simulations tend to be uncertain due to the limited length and time scales accessible. In this work, we train a committee neural network potential to simulate the hematite/water interface at the hybrid DFT level of theory to reach the nanosecond timescale and systems containing more than 3000 atoms. The NNP enables us to converge dynamical properties, not possible with brute-force ab initio molecular dynamics. Our simulations uncover a rich solvation dynamics at the hematite/water interface spanning three different time scales: picosecond H-bond dynamics between surface hydroxyls and the first water layer, in-plane/out-of-plane tilt motion of surface hydroxyls on the 10 ps time scale, and diffusion of water molecules from the oxide surface characterized by a mean residence lifetime of about 60 ps. Calculation of vibrational spectra confirm that H-bonds between surface hydroxyls and first layer water molecules are stronger than H-bonds in bulk water. Our study showcases how state of the art machine learning approaches can routinely be utilized to explore the structural dynamics at transition metal oxide interfaces with complex electronic structure. It foreshadows that c-NNPs are a promising tool to tackle the sampling problem in ab initio electrochemistry with explicit solvent molecules.
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Affiliation(s)
- Philipp Schienbein
- Department of Physics and Astronomy and Thomas Young Centre, University College London, London, WC1E 6BT, UK.
| | - Jochen Blumberger
- Department of Physics and Astronomy and Thomas Young Centre, University College London, London, WC1E 6BT, UK.
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17
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Steinmann SN, Michel C. How to Gain Atomistic Insights on Reactions at the Water/Solid Interface? ACS Catal 2022. [DOI: 10.1021/acscatal.2c00594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Stephan N. Steinmann
- Ecole Normale Supérieure de Lyon, CNRS, Laboratoire de Chimie
UMR 5182, 46 allée d’Italie, F-69364 Lyon, France
| | - Carine Michel
- Ecole Normale Supérieure de Lyon, CNRS, Laboratoire de Chimie
UMR 5182, 46 allée d’Italie, F-69364 Lyon, France
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18
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Mikkelsen AEG, Kristoffersen HH, Schiøtz J, Vegge T, Hansen HA, Jacobsen KW. Structure and energetics of liquid water-hydroxyl layers on Pt(111). Phys Chem Chem Phys 2022; 24:9885-9890. [PMID: 35416202 DOI: 10.1039/d2cp00190j] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The interactions between liquid water and hydroxyl species on Pt(111) surfaces have been intensely investigated due to their importance to fuel cell electrocatalysis. Here we present a molecular dynamics study of their structure and energetics using an ensemble of neural network potentials, which allow us to obtain unprecedented statistical sampling. We first study the energetics of hydroxyl formation, where we find a near-linear adsorption energy profile, which exhibits a soft and gradual increase in the differential adsorption energy at high hydroxyl coverages. This is strikingly different from the predictions of the conventional bilayer model, which displays a kink at 1/3ML OH coverage indicating a sizeable jump in differential adsorption energy, but within the statistical uncertainty of previously reported ab initio molecular dynamics studies. We then analyze the structure of the interface, where we provide evidence for the water-OH/Pt(111) interface being hydrophobic at high hydroxyl coverages. We furthermore explain the observed adsorption energetics by analyzing the hydrogen bonding in the water-hydroxyl adlayers, where we argue that the increase in differential adsorption energy at high OH coverage can be explained by a reduction in the number of hydrogen bonds from the adsorbed water molecules to the hydroxyls.
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Affiliation(s)
- August E G Mikkelsen
- Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
| | | | - Jakob Schiøtz
- CAMD, Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
| | - Heine A Hansen
- Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
| | - Karsten W Jacobsen
- CAMD, Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
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19
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Juraskova V, Celerse F, Laplaza R, Corminboeuf C. Assessing the persistence of chalcogen bonds in solution with neural network potentials. J Chem Phys 2022; 156:154112. [DOI: 10.1063/5.0085153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what are the most prevalent non-covalent interactions occurring in a solute-Cl$^-$-THF mixture. The simulations in explicit solvent highlight competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.
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20
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Warburton RE, Soudackov AV, Hammes-Schiffer S. Theoretical Modeling of Electrochemical Proton-Coupled Electron Transfer. Chem Rev 2022; 122:10599-10650. [PMID: 35230812 DOI: 10.1021/acs.chemrev.1c00929] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Proton-coupled electron transfer (PCET) plays an essential role in a wide range of electrocatalytic processes. A vast array of theoretical and computational methods have been developed to study electrochemical PCET. These methods can be used to calculate redox potentials and pKa values for molecular electrocatalysts, proton-coupled redox potentials and bond dissociation free energies for PCET at metal and semiconductor interfaces, and reorganization energies associated with electrochemical PCET. Periodic density functional theory can also be used to compute PCET activation energies and perform molecular dynamics simulations of electrochemical interfaces. Various approaches for maintaining a constant electrode potential in electronic structure calculations and modeling complex interactions in the electric double layer (EDL) have been developed. Theoretical formulations for both homogeneous and heterogeneous electrochemical PCET spanning the adiabatic, nonadiabatic, and solvent-controlled regimes have been developed and provide analytical expressions for the rate constants and current densities as functions of applied potential. The quantum mechanical treatment of the proton and inclusion of excited vibronic states have been shown to be critical for describing experimental data, such as Tafel slopes and potential-dependent kinetic isotope effects. The calculated rate constants can be used as input to microkinetic models and voltammogram simulations to elucidate complex electrocatalytic processes.
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Affiliation(s)
- Robert E Warburton
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, Connecticut 06520, United States
| | - Alexander V Soudackov
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, Connecticut 06520, United States
| | - Sharon Hammes-Schiffer
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, Connecticut 06520, United States
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21
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Ketkaew R, Creazzo F, Luber S. Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space. J Phys Chem Lett 2022; 13:1797-1805. [PMID: 35171614 DOI: 10.1021/acs.jpclett.1c04004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.
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Affiliation(s)
- Rangsiman Ketkaew
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Fabrizio Creazzo
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Sandra Luber
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
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22
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Xu A, Govindarajan N, Kastlunger G, Vijay S, Chan K. Theories for Electrolyte Effects in CO 2 Electroreduction. Acc Chem Res 2022; 55:495-503. [PMID: 35107967 DOI: 10.1021/acs.accounts.1c00679] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Electrochemical CO2 reduction (eCO2R) enables the conversion of waste CO2 to high-value fuels and commodity chemicals powered by renewable electricity, thereby offering a viable strategy for reaching the goal of net-zero carbon emissions. Research in the past few decades has focused both on the optimization of the catalyst (electrode) and the electrolyte environment. Surface-area normalized current densities show that the latter can affect the CO2 reduction activity by up to a few orders of magnitude.In this Account, we review theories of the mechanisms behind the effects of the electrolyte (cations, anions, and the electrolyte pH) on eCO2R. As summarized in the conspectus graphic, the electrolyte influences eCO2R activity via a field (ε) effect on dipolar (μ) reaction intermediates, changing the proton donor for the multi-step proton-electron transfer reaction, specifically adsorbed anions on the catalyst surface to block active sites, and tuning the local environment by homogeneous reactions. To be specific, alkali metal cations (M+) can stabilize reaction intermediates via electrostatic interactions with dipolar intermediates or buffer the interfacial pH via hydrolysis reactions, thereby promoting the eCO2R activity with the following trend in hydrated size (corresponding to the local field strength ε)/hydrolysis ability: Cs+ > K+ > Na+ > Li+. The effect of the electrolyte pH can give a change in eCO2R activity of up to several orders of magnitude, arising from linearly shifting the absolute interfacial field via the relationship USHE = URHE - (2.3kBT)pH, homogeneous reactions between OH- and desorbed intermediates, or changing the proton donor from hydronium to water along with increasing pH. Anions have been suggested to affect the eCO2R reaction process by solution-phase reactions (e.g., buffer reactions to tune local pH), acting as proton donors or as a surface poison.So far, the existing models of electrolyte effects have been used to rationalize various experimentally observed trends, having yet to demonstrate general predictive capabilities. The major challenges in our understanding of the electrolyte effect in eCO2R are (i) the long time scale associated with a dynamic ab initio picture of the catalyst|electrolyte interface and (ii) the overall activity determined by the length-scale interplay of intrinsic microkinetics, homogeneous reactions, and mass transport limitations. New developments in ab initio dynamic models and coupling the effects of mass transport can provide a more accurate view of the structure and intrinsic functions of the electrode-electrolyte interface and the corresponding reaction energetics toward comprehensive and predictive models for electrolyte design.
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Affiliation(s)
- Aoni Xu
- Catalysis Theory Center, Department of Physics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Nitish Govindarajan
- Catalysis Theory Center, Department of Physics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Georg Kastlunger
- Catalysis Theory Center, Department of Physics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Sudarshan Vijay
- Catalysis Theory Center, Department of Physics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Karen Chan
- Catalysis Theory Center, Department of Physics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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23
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Abstract
Structures and processes at water/metal interfaces play an important technological role in electrochemical energy conversion and storage, photoconversion, sensors, and corrosion, just to name a few. However, they are also of fundamental significance as a model system for the study of solid-liquid interfaces, which requires combining concepts from the chemistry and physics of crystalline materials and liquids. Particularly interesting is the fact that the water-water and water-metal interactions are of similar strength so that the structures at water/metal interfaces result from a competition between these comparable interactions. Because water is a polar molecule and water and metal surfaces are both polarizable, explicit consideration of the electronic degrees of freedom at water/metal interfaces is mandatory. In principle, ab initio molecular dynamics simulations are thus the method of choice to model water/metal interfaces, but they are computationally still rather demanding. Here, ab initio simulations of water/metal interfaces will be reviewed, starting from static systems such as the adsorption of single water molecules, water clusters, and icelike layers, followed by the properties of liquid water layers at metal surfaces. Technical issues such as the appropriate first-principles description of the water-water and water-metal interactions will be discussed, and electrochemical aspects will be addressed. Finally, more approximate but numerically less demanding approaches to treat water at metal surfaces from first-principles will be briefly discussed.
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Affiliation(s)
- Axel Groß
- Institute of Theoretical Chemistry, Ulm University, 89069 Ulm, Germany.,Electrochemical Energy Storage, Helmholtz Institute Ulm (HIU), 89069 Ulm, Germany
| | - Sung Sakong
- Institute of Theoretical Chemistry, Ulm University, 89069 Ulm, Germany
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24
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Luo J, Canuso V, Jang JB, Wu Z, Morales-Guio CG, Christofides PD. Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04176] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Junwei Luo
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, United States
| | - Vito Canuso
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, United States
| | - Joon Baek Jang
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, United States
| | - Zhe Wu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Carlos G. Morales-Guio
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, United States
| | - Panagiotis D. Christofides
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, United States
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095, United States
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25
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Machine-learning-based many-body energy analysis of argon clusters: Fit for size? Chem Phys 2022. [DOI: 10.1016/j.chemphys.2021.111347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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26
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Eckhoff M, Behler J. Insights into lithium manganese oxide-water interfaces using machine learning potentials. J Chem Phys 2021; 155:244703. [PMID: 34972388 DOI: 10.1063/5.0073449] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catalysis to battery technology. Density functional theory (DFT) calculations can, in principle, provide a reliable description of such interfaces, but the high computational costs severely restrict the accessible time and length scales. Here, we report machine learning-driven simulations of various interfaces between water and lithium manganese oxide (LixMn2O4), an important electrode material in lithium ion batteries and a catalyst for the oxygen evolution reaction. We employ a high-dimensional neural network potential to compute the energies and forces several orders of magnitude faster than DFT without loss in accuracy. In addition, a high-dimensional neural network for spin prediction is utilized to analyze the electronic structure of the manganese ions. Combining these methods, a series of interfaces is investigated by large-scale molecular dynamics. The simulations allow us to gain insights into a variety of properties, such as the dissociation of water molecules, proton transfer processes, and hydrogen bonds, as well as the geometric and electronic structure of the solid surfaces, including the manganese oxidation state distribution, Jahn-Teller distortions, and electron hopping.
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Affiliation(s)
- Marco Eckhoff
- Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstraße 6, 37077 Göttingen, Germany
| | - Jörg Behler
- Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstraße 6, 37077 Göttingen, Germany
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27
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Shi X, Lin X, Luo R, Wu S, Li L, Zhao ZJ, Gong J. Dynamics of Heterogeneous Catalytic Processes at Operando Conditions. JACS AU 2021; 1:2100-2120. [PMID: 34977883 PMCID: PMC8715484 DOI: 10.1021/jacsau.1c00355] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Indexed: 05/02/2023]
Abstract
The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.
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Affiliation(s)
- Xiangcheng Shi
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
- Joint
School of National University of Singapore and Tianjin University,
International Campus of Tianjin University, Fuzhou 350207, China
| | - Xiaoyun Lin
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Ran Luo
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Shican Wu
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Lulu Li
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Zhi-Jian Zhao
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Jinlong Gong
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
- Joint
School of National University of Singapore and Tianjin University,
International Campus of Tianjin University, Fuzhou 350207, China
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28
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Yokoi T, Adachi K, Iwase S, Matsunaga K. Accurate prediction of grain boundary structures and energetics in CdTe: a machine-learning potential approach. Phys Chem Chem Phys 2021; 24:1620-1629. [PMID: 34951419 DOI: 10.1039/d1cp04329c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
To accurately predict grain boundary (GB) atomic structures and their energetics in CdTe, the present study constructs an artificial-neural-network (ANN) interatomic potential. To cover a wide range of atomic environments, large amounts of density functional theory (DFT) data are used as a training dataset including point defects, surfaces and GBs. Structural relaxation combined with the trained ANN potential is applied to symmetric tilt and twist GBs, many of which are not included in the training dataset. The relative stability of the relaxed structures and their GB energies are then evaluated with the DFT level. The ANN potential is found to accurately predict low-energy structures and their energetics with reasonable accuracy with respect to DFT results, while conventional empirical potentials critically fail to find low-energy structures. The present study also provides a way to further improve the transferability of the ANN potential to more complicated GBs, using only low-Σ GBs as training datasets. Such improvement will offer a way to accurately predict atomic structures of general GBs within practical computational cost.
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Affiliation(s)
- Tatsuya Yokoi
- Department of Materials Physics, Nagoya University, Nagoya 464-8603, Japan.
| | - Kosuke Adachi
- Department of Materials Physics, Nagoya University, Nagoya 464-8603, Japan.
| | - Sayuri Iwase
- Department of Materials Physics, Nagoya University, Nagoya 464-8603, Japan.
| | - Katsuyuki Matsunaga
- Department of Materials Physics, Nagoya University, Nagoya 464-8603, Japan. .,Nanostructures Research Laboratory, Japan Fine Ceramics Center, Nagoya, 456-8587, Japan
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29
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Ringe S, Hörmann NG, Oberhofer H, Reuter K. Implicit Solvation Methods for Catalysis at Electrified Interfaces. Chem Rev 2021; 122:10777-10820. [PMID: 34928131 PMCID: PMC9227731 DOI: 10.1021/acs.chemrev.1c00675] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
![]()
Implicit solvation
is an effective, highly coarse-grained approach
in atomic-scale simulations to account for a surrounding liquid electrolyte
on the level of a continuous polarizable medium. Originating in molecular
chemistry with finite solutes, implicit solvation techniques are now
increasingly used in the context of first-principles modeling of electrochemistry
and electrocatalysis at extended (often metallic) electrodes. The
prevalent ansatz to model the latter electrodes and the reactive surface
chemistry at them through slabs in periodic boundary condition supercells
brings its specific challenges. Foremost this concerns the difficulty
of describing the entire double layer forming at the electrified solid–liquid
interface (SLI) within supercell sizes tractable by commonly employed
density functional theory (DFT). We review liquid solvation methodology
from this specific application angle, highlighting in particular its
use in the widespread ab initio thermodynamics approach
to surface catalysis. Notably, implicit solvation can be employed
to mimic a polarization of the electrode’s electronic density
under the applied potential and the concomitant capacitive charging
of the entire double layer beyond the limitations of the employed
DFT supercell. Most critical for continuing advances of this effective
methodology for the SLI context is the lack of pertinent (experimental
or high-level theoretical) reference data needed for parametrization.
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Affiliation(s)
- Stefan Ringe
- Department of Energy Science and Engineering, Daegu Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea.,Energy Science & Engineering Research Center, Daegu Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea
| | - Nicolas G Hörmann
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany.,Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstraße 4, D-85747 Garching, Germany
| | - Harald Oberhofer
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstraße 4, D-85747 Garching, Germany.,Chair for Theoretical Physics VII and Bavarian Center for Battery Technology (BayBatt), University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany
| | - Karsten Reuter
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
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30
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Mikkelsen AEG, Schiøtz J, Vegge T, Jacobsen KW. Is the water/Pt(111) interface ordered at room temperature? J Chem Phys 2021; 155:224701. [PMID: 34911304 DOI: 10.1063/5.0077580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The structure of the water/Pt(111) interface has been a subject of debate over the past decades. Here, we report the results of a room temperature molecular dynamics study based on neural network potentials, which allow us to access long time scale simulations while retaining ab initio accuracy. We find that the water/Pt(111) interface is characterized by a double layer composed of a primary, strongly bound adsorption layer with a coverage of ∼0.15 ML, which is coupled to a secondary, weakly bound adsorption layer with a coverage of ∼0.58 ML. By studying the order of the primary adsorption layer, we find that there is an effective repulsion between the adsorbed water molecules, which gives rise to a dynamically changing, semi-ordered interfacial structure, where the water molecules in the primary adsorption layer are distributed homogeneously across the interface, forming frequent hydrogen bonds to water molecules in the secondary adsorption layer. We further show that these conclusions are beyond the time scales accessible to ab initio molecular dynamics.
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Affiliation(s)
- August E G Mikkelsen
- Department of Energy Conversion and Storage, Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Jakob Schiøtz
- CAMD, Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Karsten W Jacobsen
- CAMD, Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
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31
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Dávila López AC, Eggert T, Reuter K, Hörmann NG. Static and dynamic water structures at interfaces: A case study with focus on Pt(111). J Chem Phys 2021; 155:194702. [PMID: 34800953 DOI: 10.1063/5.0067106] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
An accurate atomistic treatment of aqueous solid-liquid interfaces necessitates the explicit description of interfacial water ideally via ab initio molecular dynamics simulations. Many applications, however, still rely on static interfacial water models, e.g., for the computation of (electro)chemical reaction barriers and focus on a single, prototypical structure. In this work, we systematically study the relation between density functional theory-derived static and dynamic interfacial water models with specific focus on the water-Pt(111) interface. We first introduce a general construction protocol for static 2D water layers on any substrate, which we apply to the low index surfaces of Pt. Subsequently, we compare these with structures from a broad selection of reference works based on the Smooth Overlap of Atomic Positions descriptor. The analysis reveals some structural overlap between static and dynamic water ensembles; however, static structures tend to overemphasize the in-plane hydrogen bonding network. This feature is especially pronounced for the widely used low-temperature hexagonal ice-like structure. In addition, a complex relation between structure, work function, and adsorption energy is observed, which suggests that the concentration on single, static water models might introduce systematic biases that are likely reduced by averaging over consistently created structural ensembles, as introduced here.
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Affiliation(s)
| | - Thorben Eggert
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Karsten Reuter
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Nicolas G Hörmann
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
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32
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Niblett SP, Galib M, Limmer DT. Learning intermolecular forces at liquid-vapor interfaces. J Chem Phys 2021; 155:164101. [PMID: 34717371 DOI: 10.1063/5.0067565] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. We find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.
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Affiliation(s)
- Samuel P Niblett
- Department of Chemistry, University of California, Berkeley California 94609, USA
| | - Mirza Galib
- Department of Chemistry, University of California, Berkeley California 94609, USA
| | - David T Limmer
- Department of Chemistry, University of California, Berkeley California 94609, USA
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Rey J, Blanck S, Clabaut P, Loehlé S, Steinmann SN, Michel C. Transferable Gaussian Attractive Potentials for Organic/Oxide Interfaces. J Phys Chem B 2021; 125:10843-10853. [PMID: 34533310 DOI: 10.1021/acs.jpcb.1c05156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Organic/oxide interfaces play an important role in many areas of chemistry and in particular for lubrication and corrosion. Molecular dynamics simulations are the method of choice for providing complementary insight to experiments. However, the force fields used to simulate the interaction between molecules and oxide surfaces tend to capture only weak physisorption interactions, discarding the stabilizing Lewis acid/base interactions. We here propose a simple complement to the straightforward molecular mechanics description based on "out-of-the-box" Lennard-Jones potentials and electrostatic interactions: the addition of an attractive Gaussian potential between reactive sites of the surface and heteroatoms of adsorbed organic molecules, leading to the Gaussian Lennard-Jones (GLJ) potential. The interactions of four oxygenated and four amine molecules with the typical and widespread hematite and γ-alumina surfaces are investigated. The root mean square deviation (RMSD) for all probed molecules is only 5.7 kcal/mol, which corresponds to an error of 23% over hematite. On γ-alumina, the RMSD is 11.2 kcal/mol using a single parameter for all five chemically inequivalent surface aluminum atoms. Applying GLJ to the simulation of organic films on oxide surfaces demonstrates that the mobility of the surfactants is overestimated by the simplistic LJ potential, while GLJ and other qualitatively correct potentials show a strong structuration and slow dynamics of the surface films, as could be expected from the first-principles adsorption energies for model head groups.
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Affiliation(s)
- Jérôme Rey
- Université de Lyon, École Normale Supérieure de Lyon, CNRS UMR 5182, Laboratoire de Chimie, 46 allée d'Italie, Lyon F69364, France
| | - Sarah Blanck
- Université de Lyon, École Normale Supérieure de Lyon, CNRS UMR 5182, Laboratoire de Chimie, 46 allée d'Italie, Lyon F69364, France.,Total Marketing & Services, Chemin du Canal-BP 22, Solaize 69360, France
| | - Paul Clabaut
- Université de Lyon, École Normale Supérieure de Lyon, CNRS UMR 5182, Laboratoire de Chimie, 46 allée d'Italie, Lyon F69364, France
| | - Sophie Loehlé
- Total Marketing & Services, Chemin du Canal-BP 22, Solaize 69360, France
| | - Stephan N Steinmann
- Université de Lyon, École Normale Supérieure de Lyon, CNRS UMR 5182, Laboratoire de Chimie, 46 allée d'Italie, Lyon F69364, France
| | - Carine Michel
- Université de Lyon, École Normale Supérieure de Lyon, CNRS UMR 5182, Laboratoire de Chimie, 46 allée d'Italie, Lyon F69364, France
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Le JB, Yang XH, Zhuang YB, Jia M, Cheng J. Recent Progress toward Ab Initio Modeling of Electrocatalysis. J Phys Chem Lett 2021; 12:8924-8931. [PMID: 34499508 DOI: 10.1021/acs.jpclett.1c02086] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Electrode potential is the key factor for controlling electrocatalytic reactions at electrochemical interfaces, and moreover, it is also known that the pH and solutes (e.g., cations) of the solution have prominent effects on electrocatalysis. Understanding these effects requires microscopic information on the electrochemical interfaces, in which theoretical simulations can play an important role. This Perspective summarizes the recent progress in method development for modeling electrochemical interfaces, including different methods for describing the electrolytes at the interfaces and different schemes for charging up the electrode surfaces. In the final section, we provide an outlook for future development in modeling methods and their applications to electrocatalysis.
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Affiliation(s)
- Jia-Bo Le
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xiao-Hui Yang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yong-Bin Zhuang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Mei Jia
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jun Cheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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Machine learning potentials for complex aqueous systems made simple. Proc Natl Acad Sci U S A 2021; 118:2110077118. [PMID: 34518232 PMCID: PMC8463804 DOI: 10.1073/pnas.2110077118] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2021] [Indexed: 12/23/2022] Open
Abstract
Understanding complex materials, in particular those with solid–liquid interfaces, such as water on surfaces or under confinement, is a key challenge for technological and scientific progress. Although established simulation approaches have been able to provide important atomistic insight, ab initio techniques struggle with the required time and length scales, while force field methods can often be limited in terms of their accuracy. Here we show how these limitations can be overcome in a simple and automated machine learning procedure to provide accurate models of interactions at the ab initio level, as illustrated for a variety of complex aqueous systems. These developments open up the prospect of the straightforward exploration of many technologically relevant systems by molecular simulations. Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid–liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.
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Schwalbe-Koda D, Tan AR, Gómez-Bombarelli R. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks. Nat Commun 2021; 12:5104. [PMID: 34429418 PMCID: PMC8384857 DOI: 10.1038/s41467-021-25342-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/02/2021] [Indexed: 12/14/2022] Open
Abstract
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system.
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Affiliation(s)
- Daniel Schwalbe-Koda
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aik Rui Tan
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Serrano Jiménez A, Sánchez Muzas AP, Zhang Y, Ovčar J, Jiang B, Lončarić I, Juaristi JI, Alducin M. Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach. J Chem Theory Comput 2021; 17:4648-4659. [PMID: 34278798 PMCID: PMC8389528 DOI: 10.1021/acs.jctc.1c00347] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
![]()
Modeling the ultrafast
photoinduced dynamics and reactivity of
adsorbates on metals requires including the effect of the laser-excited
electrons and, in many cases, also the effect of the highly excited
surface lattice. Although the recent ab initio molecular dynamics
with electronic friction and thermostats, (Te,Tl)-AIMDEF [AlducinM.;Phys. Rev. Lett.2019, 123, 246802]31922860, enables such complex
modeling, its computational cost may limit its applicability. Here,
we use the new embedded atom neural network (EANN) method [ZhangY.;J. Phys. Chem. Lett.2019, 10, 496231397157] to develop an accurate and extremely
complex potential energy surface (PES) that allows us a detailed and
reliable description of the photoinduced desorption of CO from the
Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics
simulations performed on this EANN-PES reproduce the (Te,Tl)-AIMDEF results with
a remarkable level of accuracy. This demonstrates the outstanding
performance of the obtained EANN-PES that is able to reproduce available
density functional theory (DFT) data for an extensive range of surface
temperatures (90–1000 K); a large number of degrees of freedom,
those corresponding to six CO adsorbates and 24 moving surface atoms;
and the varying CO coverage caused by the abundant desorption events.
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Affiliation(s)
- Alfredo Serrano Jiménez
- Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Paseo Manuel de Lardizabal 5, 20018 Donostia-San Sebastián, Spain
| | - Alberto P Sánchez Muzas
- Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Paseo Manuel de Lardizabal 5, 20018 Donostia-San Sebastián, Spain
| | - Yaolong Zhang
- Hefei National Laboratory for Physical Science at the Microscale, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Juraj Ovčar
- Ruđer Bošković Institute, Bijenička 54, HR-10000 Zagreb, Croatia
| | - Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Ivor Lončarić
- Ruđer Bošković Institute, Bijenička 54, HR-10000 Zagreb, Croatia.,Donostia International Physics Center (DIPC), Paseo Manuel de Lardizabal 4, 20018 Donostia-San Sebastián, Spain
| | - J Iñaki Juaristi
- Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Paseo Manuel de Lardizabal 5, 20018 Donostia-San Sebastián, Spain.,Donostia International Physics Center (DIPC), Paseo Manuel de Lardizabal 4, 20018 Donostia-San Sebastián, Spain.,Departamento de Polímeros y Materiales Avanzados: Física, Química y Tecnología, Facultad de Químicas (UPV/EHU), Apartado 1072, 20080 Donostia-San Sebastián, Spain
| | - Maite Alducin
- Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Paseo Manuel de Lardizabal 5, 20018 Donostia-San Sebastián, Spain.,Donostia International Physics Center (DIPC), Paseo Manuel de Lardizabal 4, 20018 Donostia-San Sebastián, Spain
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Miksch AM, Morawietz T, Kästner J, Urban A, Artrith N. Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abfd96] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (a) data collection, (b) model selection, (c) training and validation, and (d) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies.
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Zhang L, Wang H, Car R, E W. Phase Diagram of a Deep Potential Water Model. PHYSICAL REVIEW LETTERS 2021; 126:236001. [PMID: 34170175 DOI: 10.1103/physrevlett.126.236001] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/28/2021] [Indexed: 06/13/2023]
Abstract
Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region. The computational efficiency of the model makes it possible to predict its phase diagram using molecular dynamics. Satisfactory overall agreement with experimental results is obtained. The fluid phases, molecular and ionic, and all the stable ice polymorphs, ordered and disordered, are predicted correctly, with the exception of ice III and XV that are stable in experiments, but metastable in the model. The evolution of the atomic dynamics upon heating, as ice VII transforms first into ice VII^{''} and then into an ionic fluid, reveals that molecular dissociation and breaking of the ice rules coexist with strong covalent fluctuations, explaining why only partial ionization was inferred in experiments.
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Affiliation(s)
- Linfeng Zhang
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
| | - Han Wang
- Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People's Republic of China
| | - Roberto Car
- Department of Chemistry, Department of Physics, Program in Applied and Computational Mathematics, Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA
| | - Weinan E
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA and Beijing Institute of Big Data Research, Beijing 100871, People's Republic of China
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40
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Laurens G, Rabary M, Lam J, Peláez D, Allouche AR. Infrared spectra of neutral polycyclic aromatic hydrocarbons based on machine learning potential energy surface and dipole mapping. Theor Chem Acc 2021. [DOI: 10.1007/s00214-021-02773-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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41
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Oshiki J, Nakano H, Sato H. Controlling potential difference between electrodes based on self-consistent-charge density functional tight binding. J Chem Phys 2021; 154:144107. [PMID: 33858148 DOI: 10.1063/5.0047992] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
A proper understanding and description of the electronic response of the electrode surfaces in electrochemical systems are quite important because the interactions between the electrode surface and electrolyte give rise to unique and useful interfacial properties. Atomistic modeling of the electrodes requires not only an accurate description of the electronic response under a constant-potential condition but also computational efficiency in order to deal with systems large enough to investigate the interfacial electrolyte structures. We thus develop a self-consistent-charge density functional tight binding based method to model a pair of electrodes in electrochemical cells under the constant-potential condition. The method is more efficient than the (ab initio) density functional theory calculations so that it can treat systems as large as those studied in classical atomistic simulations. It can also describe the electronic response of electrodes quantum mechanically and more accurately than the classical counterparts. The constant-potential condition is introduced through a Legendre transformation of the electronic energy with respect to the difference in the number of electrons in the two electrodes and their electrochemical potential difference, through which the Kohn-Sham equations for each electrode are variationally derived. The method is applied to platinum electrodes faced parallel to each other under an applied voltage. The electronic response to the voltage and a charged particle is compared with the result of a classical constant-potential method based on the chemical potential equalization principle.
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Affiliation(s)
- Jun Oshiki
- Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto 615-8510, Japan
| | - Hiroshi Nakano
- Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto 615-8510, Japan
| | - Hirofumi Sato
- Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Kyoto 615-8510, Japan
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42
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Kroes GJ. Computational approaches to dissociative chemisorption on metals: towards chemical accuracy. Phys Chem Chem Phys 2021; 23:8962-9048. [PMID: 33885053 DOI: 10.1039/d1cp00044f] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We review the state-of-the-art in the theory of dissociative chemisorption (DC) of small gas phase molecules on metal surfaces, which is important to modeling heterogeneous catalysis for practical reasons, and for achieving an understanding of the wealth of experimental information that exists for this topic, for fundamental reasons. We first give a quick overview of the experimental state of the field. Turning to the theory, we address the challenge that barrier heights (Eb, which are not observables) for DC on metals cannot yet be calculated with chemical accuracy, although embedded correlated wave function theory and diffusion Monte-Carlo are moving in this direction. For benchmarking, at present chemically accurate Eb can only be derived from dynamics calculations based on a semi-empirically derived density functional (DF), by computing a sticking curve and demonstrating that it is shifted from the curve measured in a supersonic beam experiment by no more than 1 kcal mol-1. The approach capable of delivering this accuracy is called the specific reaction parameter (SRP) approach to density functional theory (DFT). SRP-DFT relies on DFT and on dynamics calculations, which are most efficiently performed if a potential energy surface (PES) is available. We therefore present a brief review of the DFs that now exist, also considering their performance on databases for Eb for gas phase reactions and DC on metals, and for adsorption to metals. We also consider expressions for SRP-DFs and briefly discuss other electronic structure methods that have addressed the interaction of molecules with metal surfaces. An overview is presented of dynamical models, which make a distinction as to whether or not, and which dissipative channels are modeled, the dissipative channels being surface phonons and electronically non-adiabatic channels such as electron-hole pair excitation. We also discuss the dynamical methods that have been used, such as the quasi-classical trajectory method and quantum dynamical methods like the time-dependent wave packet method and the reaction path Hamiltonian method. Limits on the accuracy of these methods are discussed for DC of diatomic and polyatomic molecules on metal surfaces, paying particular attention to reduced dimensionality approximations that still have to be invoked in wave packet calculations on polyatomic molecules like CH4. We also address the accuracy of fitting methods, such as recent machine learning methods (like neural network methods) and the corrugation reducing procedure. In discussing the calculation of observables we emphasize the importance of modeling the properties of the supersonic beams in simulating the sticking probability curves measured in the associated experiments. We show that chemically accurate barrier heights have now been extracted for DC in 11 molecule-metal surface systems, some of which form the most accurate core of the only existing database of Eb for DC reactions on metal surfaces (SBH10). The SRP-DFs (or candidate SRP-DFs) that have been derived show transferability in many cases, i.e., they have been shown also to yield chemically accurate Eb for chemically related systems. This can in principle be exploited in simulating rates of catalyzed reactions on nano-particles containing facets and edges, as SRP-DFs may be transferable among systems in which a molecule dissociates on low index and stepped surfaces of the same metal. In many instances SRP-DFs have allowed important conclusions regarding the mechanisms underlying observed experimental trends. An important recent observation is that SRP-DFT based on semi-local exchange DFs has so far only been successful for systems for which the difference of the metal work function and the molecule's electron affinity exceeds 7 eV. A main challenge to SRP-DFT is to extend its applicability to the other systems, which involve a range of important DC reactions of e.g. O2, H2O, NH3, CO2, and CH3OH. Recent calculations employing a PES based on a screened hybrid exchange functional suggest that the road to success may be based on using exchange functionals of this category.
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Affiliation(s)
- Geert-Jan Kroes
- Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands.
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43
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
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Liu Z, Lin L, Jia Q, Cheng Z, Jiang Y, Guo Y, Ma J. Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning. J Chem Inf Model 2021; 61:1066-1082. [PMID: 33629839 DOI: 10.1021/acs.jcim.0c01224] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier molecular orbital highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels and their HOMO-LUMO gaps from the small-sized molecule data to larger molecules remains a challenge. Here, we develop a multilevel attention neural network, named DeepMoleNet, to enable chemical interpretable insights being fused into multitask learning through (1) weighting contributions from various atoms and (2) taking the atom-centered symmetry functions (ACSFs) as the teacher descriptor. The efficient prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using multiple benchmarks, both at the equilibrium and nonequilibrium geometries, including up to 110,000 records of data in QM9, 400,000 records in MD17, and 280,000 records in ANI-1ccx for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both equilibrium QM9 and Alchemy data sets at the density functional theory (DFT) level. Additional tests on nonequilibrium molecular conformations from DFT-based MD17 data set and ANI-1ccx data set with coupled cluster accuracy as well as the public test sets of singlet fission molecules, biomolecules, long oligomers, and protein with up to 140 atoms show reasonable predictions for thermodynamics and electronic structure properties. The proposed multilevel attention neural network is applicable to high-throughput screening of numerous chemical species in both equilibrium and nonequilibrium molecular spaces to accelerate rational designs of drug-like molecules, material candidates, and chemical reactions.
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Affiliation(s)
- Ziteng Liu
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Liqiang Lin
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, P. R. China
| | - Qingqing Jia
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Zheng Cheng
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Yanyan Jiang
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, P. R. China
| | - Yanwen Guo
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, P. R. China
| | - Jing Ma
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China.,Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
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45
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Shuaibi M, Sivakumar S, Chen RQ, Ulissi ZW. Enabling robust offline active learning for machine learning potentials using simple physics-based priors. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abcc44] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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46
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Benoit M, Amodeo J, Combettes S, Khaled I, Roux A, Lam J. Measuring transferability issues in machine-learning force fields: the example of gold–iron interactions with linearized potentials. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/abc9fd] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Machine-learning force fields have been increasingly employed in order to extend the possibility of current first-principles calculations. However, the transferability of the obtained potential cannot always be guaranteed in situations that are outside the original database. To study such limitation, we examined the very difficult case of the interactions in gold–iron nanoparticles. For the machine-learning potential, we employed a linearized formulation that is parameterized using a penalizing regression scheme which allows us to control the complexity of the obtained potential. We showed that while having a more complex potential allows for a better agreement with the training database, it can also lead to overfitting issues and a lower accuracy in untrained systems.
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47
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Abstract
The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.
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Affiliation(s)
- Benjamin W J Chen
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lang Xu
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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48
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Cendagorta JR, Shen H, Bačić Z, Tuckerman ME. Enhanced Sampling Path Integral Methods Using Neural Network Potential Energy Surfaces with Application to Diffusion in Hydrogen Hydrates. ADVANCED THEORY AND SIMULATIONS 2020. [DOI: 10.1002/adts.202000258] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
| | - Hengyuan Shen
- Department of Chemistry New York University Shanghai 1555 Century Avenue Pudong Shanghai 200122 China
| | - Zlatko Bačić
- Department of Chemistry New York University New York NY 10003 USA
- NYU‐ECNU Center for Computational Chemistry at NYU Shanghai 3663 Zhongshan Road, North Shanghai 200062 China
| | - Mark E. Tuckerman
- Department of Chemistry New York University New York NY 10003 USA
- NYU‐ECNU Center for Computational Chemistry at NYU Shanghai 3663 Zhongshan Road, North Shanghai 200062 China
- Courant Institute of Mathematical Sciences New York University New York NY 10012 USA
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49
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Dependency of solvation effects on metal identity in surface reactions. Commun Chem 2020; 3:187. [PMID: 36703410 PMCID: PMC9814277 DOI: 10.1038/s42004-020-00428-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 11/09/2020] [Indexed: 01/29/2023] Open
Abstract
Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O-H and C-H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface. A significant metal dependence on aqueous solvation effects was observed that suggests solvation effects must be studied in detail for every reaction system. The main reason for this dependence could be traced back to a different amount of charge-transfer between the adsorbed moieties and metals in the reactant and transition states for the different metal surfaces.
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50
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Bedolla E, Padierna LC, Castañeda-Priego R. Machine learning for condensed matter physics. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2020; 33:053001. [PMID: 32932243 DOI: 10.1088/1361-648x/abb895] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 09/15/2020] [Indexed: 06/11/2023]
Abstract
Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as chemistry, materials science, statistical physics, and high-performance computing. With the advancements in modern machine learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss in detail the main challenges and drawbacks of using ML methods on CMP problems, as well as some perspectives for future developments.
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Affiliation(s)
- Edwin Bedolla
- División de Ciencias e Ingenierías, Universidad de Guanajuato, Loma del Bosque 103, 37150 León, Mexico
| | - Luis Carlos Padierna
- División de Ciencias e Ingenierías, Universidad de Guanajuato, Loma del Bosque 103, 37150 León, Mexico
| | - Ramón Castañeda-Priego
- División de Ciencias e Ingenierías, Universidad de Guanajuato, Loma del Bosque 103, 37150 León, Mexico
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