1
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Kiyohara S, Hinuma Y, Oba F. Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning. J Am Chem Soc 2024; 146:9697-9708. [PMID: 38546127 PMCID: PMC11009958 DOI: 10.1021/jacs.3c13574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 04/11/2024]
Abstract
The band alignment of semiconductors, insulators, and dielectrics is relevant to diverse material properties and device structures utilizing their surfaces and interfaces. In particular, the ionization potential and electron affinity are fundamental quantities that describe surface-dependent band-edge positions with respect to the vacuum level. Their accurate and systematic determination, however, demands elaborate experiments or simulations for well-characterized surfaces. Here, we report machine learning for the band alignment of nonmetallic oxides using a high-throughput first-principles calculation data set containing about 3000 oxide surfaces. Our neural network accurately predicts the band positions for relaxed surfaces of binary oxides simply by using the information on bulk structures and surface termination planes. Moreover, we extend the model to naturally include multiple-cation effects and transfer it to ternary oxides. The present approach enables the band alignment of a vast number of solid surfaces, thereby opening the way to a systematic understanding and materials screening.
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Affiliation(s)
- Shin Kiyohara
- Laboratory
for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, R3-7, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
- Institute
for Materials Research, Tohoku University, 2-2-1 Katahira,
Aoba-ku, Sendai 980-8577, Japan
| | - Yoyo Hinuma
- Department
of Energy and Environment, National Institute
of Advanced Industrial Science and Technology (AIST), 1-8-31 Midorigaoka, Ikeda, Osaka 563-8577, Japan
| | - Fumiyasu Oba
- Laboratory
for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, R3-7, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
- MDX
Research Center for Element Strategy, International Research Frontiers
Initiative, Tokyo Institute of Technology, SE-6, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
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2
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Kneiding H, Nova A, Balcells D. Directional multiobjective optimization of metal complexes at the billion-system scale. NATURE COMPUTATIONAL SCIENCE 2024; 4:263-273. [PMID: 38553635 DOI: 10.1038/s43588-024-00616-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 02/29/2024] [Indexed: 04/14/2024]
Abstract
The discovery of transition metal complexes (TMCs) with optimal properties requires large ligand libraries and efficient multiobjective optimization algorithms. Here we provide the tmQMg-L library, containing 30k diverse and synthesizable ligands with robustly assigned charges and metal coordination modes. tmQMg-L enabled the generation of 1.37 million palladium TMCs, which were used to develop and benchmark the Pareto-Lighthouse multiobjective genetic algorithm (PL-MOGA). With fine control over aim and scope, this algorithm maximized both the polarizability and highest occupied molecular orbital-lowest unoccupied molecular orbital gap of the TMCs within selected regions of the Pareto front, without requiring prior knowledge on the objective limits. Instead of genetic operations on small ligand fragments, the PL-MOGA did whole-ligand mutation and crossover operations, which in chemical spaces containing billions of systems, yielded thousands of highly diverse TMCs in an interpretable manner.
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Affiliation(s)
- Hannes Kneiding
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway
| | - Ainara Nova
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway
- Centre for Materials Science and Nanotechnology, Department of Chemistry, University of Oslo, Oslo, Norway
| | - David Balcells
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway.
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3
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Huang L, Xu T, Yu Y, Zhao P, Chen X, Han J, Xie Z, Li H, Zhong W, Wong KC, Zhang H. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat Commun 2024; 15:2657. [PMID: 38531837 DOI: 10.1038/s41467-024-46569-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.
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Affiliation(s)
- Lei Huang
- City University of Hong Kong, Hong Kong, SAR, China
- Tencent AI Lab, Shenzhen, China
| | | | - Yang Yu
- Tencent AI Lab, Shenzhen, China
| | | | | | - Jing Han
- Regor Therapeutics Group, Shanghai, China
| | - Zhi Xie
- Regor Therapeutics Group, Shanghai, China
| | - Hailong Li
- Regor Therapeutics Group, Shanghai, China.
| | | | - Ka-Chun Wong
- City University of Hong Kong, Hong Kong, SAR, China.
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4
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Gallarati S, van Gerwen P, Laplaza R, Brey L, Makaveev A, Corminboeuf C. A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target. Chem Sci 2024; 15:3640-3660. [PMID: 38455002 PMCID: PMC10915838 DOI: 10.1039/d3sc06208b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 03/09/2024] Open
Abstract
A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as a primary target. We apply this strategy to the Pictet-Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure-performance relationships from the challenging areas of chemical space.
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Affiliation(s)
- Simone Gallarati
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Puck van Gerwen
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Lucien Brey
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Alexander Makaveev
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
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5
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Kalikadien AV, Mirza A, Hossaini AN, Sreenithya A, Pidko EA. Paving the road towards automated homogeneous catalyst design. Chempluschem 2024:e202300702. [PMID: 38279609 DOI: 10.1002/cplu.202300702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/20/2023] [Indexed: 01/28/2024]
Abstract
In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.
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Affiliation(s)
- Adarsh V Kalikadien
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Adrian Mirza
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Aydin Najl Hossaini
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Avadakkam Sreenithya
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Evgeny A Pidko
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
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6
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Carracedo-Reboredo P, Aranzamendi E, He S, Arrasate S, Munteanu CR, Fernandez-Lozano C, Sotomayor N, Lete E, González-Díaz H. MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products. J Cheminform 2024; 16:9. [PMID: 38254200 PMCID: PMC10804835 DOI: 10.1186/s13321-024-00802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Eider Aranzamendi
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Shan He
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Cristian R Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Nuria Sotomayor
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Esther Lete
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.
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7
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Escayola S, Bahri-Laleh N, Poater A. % VBur index and steric maps: from predictive catalysis to machine learning. Chem Soc Rev 2024; 53:853-882. [PMID: 38113051 DOI: 10.1039/d3cs00725a] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.
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Affiliation(s)
- Sílvia Escayola
- Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona, c/Mª Aurèlia Capmany 69, 17003 Girona, Catalonia, Spain.
- Donostia International Physics Center (DIPC), 20018 Donostia, Euskadi, Spain
| | - Naeimeh Bahri-Laleh
- Iran Polymer and Petrochemical Institute (IPPI), P.O. Box 14965/115, Tehran, Iran
- Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM), Hiroshima University, Hiroshima, 739-8526, Japan
| | - Albert Poater
- Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona, c/Mª Aurèlia Capmany 69, 17003 Girona, Catalonia, Spain.
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8
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Xie Z, Evangelopoulos X, Omar ÖH, Troisi A, Cooper AI, Chen L. Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules. Chem Sci 2024; 15:500-510. [PMID: 38179524 PMCID: PMC10762956 DOI: 10.1039/d3sc04610a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/04/2023] [Indexed: 01/06/2024] Open
Abstract
We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.
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Affiliation(s)
- Zikai Xie
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool Liverpool L7 3NY UK
| | - Xenophon Evangelopoulos
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool Liverpool L7 3NY UK
| | - Ömer H Omar
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Andrew I Cooper
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool Liverpool L7 3NY UK
| | - Linjiang Chen
- School of Chemistry, School of Computer Science, University of Birmingham Birmingham B15 2TT UK
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9
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Xia X, Sivonxay E, Helms BA, Blau SM, Chan EM. Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization. NANO LETTERS 2023. [PMID: 38038194 DOI: 10.1021/acs.nanolett.3c03568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb3+/Er3+- and Yb3+/Er3+/Tm3+-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.
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Affiliation(s)
- Xiaojing Xia
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Eric Sivonxay
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Brett A Helms
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Emory M Chan
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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10
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Yoo P, Bhowmik D, Mehta K, Zhang P, Liu F, Lupo Pasini M, Irle S. Deep learning workflow for the inverse design of molecules with specific optoelectronic properties. Sci Rep 2023; 13:20031. [PMID: 37973879 PMCID: PMC10654498 DOI: 10.1038/s41598-023-45385-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).
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Affiliation(s)
- Pilsun Yoo
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Kshitij Mehta
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Pei Zhang
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Frank Liu
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Massimiliano Lupo Pasini
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Stephan Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
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11
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Cheng L, Tang Y, Ostrikov KK, Xiang Q. Single-Atom Heterogeneous Catalysts: Human- and AI-Driven Platform for Augmented Designs, Analytics and Reality-Enabled Manufacturing. Angew Chem Int Ed Engl 2023:e202313599. [PMID: 37891153 DOI: 10.1002/anie.202313599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 10/29/2023]
Abstract
Heterogeneous catalysts with targeted functionality can be designed with atomic precision, but it is challenging to retain the structure and performance upon the scaled-up manufacturing. Particularly challenging is to ensure the "atomic economy", where every catalytic site is most gainfully utilized. Given the emerging synergistic integration of human- and artificial intelligence (AI)-driven augmented designs (AD), augmented analytics (AA), and augmented reality manufacturing (AM) platforms, this minireview focuses on single-atom heterogeneous catalysts (SAHCs) and examines the current status, challenges, and future perspectives of translating atomic-level structural precision and data-driven discovery to next-generation industrial manufacturing. We critically examine the atomistic insights into structure-driven SAHCs functionality and discuss the opportunities and challenges on the way towards the synergistic human-AI collaborative data-driven platform capable of monitoring, analyzing, manufacturing, and retaining the atomic-scale structure and functions. Enhanced by the atomic-level AD, AA, and AM, evolving from the current high-throughput capabilities and digital materials manufacturing acceleration, this synergistic human-AI platform is promising to enable atom-efficient and atomically precise heterogeneous catalyst production.
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Affiliation(s)
- Lei Cheng
- School of Chemistry and Materials Science, Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Yawen Tang
- School of Chemistry and Materials Science, Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Kostya Ken Ostrikov
- School of Chemistry and Physics and Centre for Materials Science, Queensland University of Technology (QUT), Brisbane, Queensland, 4000, Australia
| | - Quanjun Xiang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, P. R. China
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12
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Liu HW, He P, Li WT, Sun W, Shi K, Wang YQ, Mo QK, Zhang XY, Zhu SF. Catalyst-Oriented Design Based on Elementary Reactions (CODER) for Triarylamine Synthesis. Angew Chem Int Ed Engl 2023; 62:e202309111. [PMID: 37698233 DOI: 10.1002/anie.202309111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/09/2023] [Accepted: 09/12/2023] [Indexed: 09/13/2023]
Abstract
Recently, the application of computational tools to the rational design of catalysts has received considerable attention, but progress has been limited by the reliance on databases and because mechanistic data have been almost neglected. Herein, we report a new strategy for catalyst design, designated catalyst-oriented design based on elementary reactions (CODER), which fully utilizes mechanistic data, combines the strengths of computational tools and researcher experience. CODER enabled the development of extremely efficient Pd catalysts for C-N coupling, which markedly improved the efficiency of the synthesis of widely used triarylamine optoelectronic materials by enhancing the turnover numbers (up to 340000) to 1-3 orders of magnitude towards literature values.
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Affiliation(s)
- Hua-Wei Liu
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Peng He
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Wen-Tao Li
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Wei Sun
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Kai Shi
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - You-Qin Wang
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Qian-Kun Mo
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Xin-Yu Zhang
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
| | - Shou-Fei Zhu
- Frontiers Science Center for New Organic Matters, State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, 94th Weijin Road, Tianjin, 300071, China
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13
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Wang Y, Tong C, Liu Q, Han R, Liu C. Intergrowth Zeolites, Synthesis, Characterization, and Catalysis. Chem Rev 2023; 123:11664-11721. [PMID: 37707958 DOI: 10.1021/acs.chemrev.3c00373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Microporous zeolites that can act as heterogeneous catalysts have continued to attract a great deal of academic and industrial interest, but current progress in their synthesis and application is restricted to single-phase zeolites, severely underestimating the potential of intergrowth frameworks. Compared with single-phase zeolites, intergrowth zeolites possess unique properties, such as different diffusion pathways and molecular confinement, or special crystalline pore environments for binding metal active sites. This review first focuses on the structural features and synthetic details of all the intergrowth zeolites, especially providing some insightful discussion of several potential frameworks. Subsequently, characterization methods for intergrowth zeolites are introduced, and highlighting fundamental features of these crystals. Then, the applications of intergrowth zeolites in several of the most active areas of catalysis are presented, including selective catalytic reduction of NOx by ammonia (NH3-SCR), methanol to olefins (MTO), petrochemicals and refining, fine chemicals production, and biomass conversion on Beta, and the relationship between structure and catalytic activity was profiled from the perspective of intergrowth grain boundary structure. Finally, the synthesis, characterization, and catalysis of intergrowth zeolites are summarized in a comprehensive discussion, and a brief outlook on the current challenges and future directions of intergrowth zeolites is indicated.
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Affiliation(s)
- Yanhua Wang
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Chengzheng Tong
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Qingling Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Rui Han
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Caixia Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
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14
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Zhang Z, Liu Q, Lee CK, Hsieh CY, Chen E. An equivariant generative framework for molecular graph-structure Co-design. Chem Sci 2023; 14:8380-8392. [PMID: 37564414 PMCID: PMC10411624 DOI: 10.1039/d3sc02538a] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/05/2023] [Indexed: 08/12/2023] Open
Abstract
Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.
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Affiliation(s)
- Zaixi Zhang
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China
- State Key Laboratory of Cognitive Intelligence Hefei Anhui 230088 China
| | - Qi Liu
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China
- State Key Laboratory of Cognitive Intelligence Hefei Anhui 230088 China
| | | | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang 310058 China
| | - Enhong Chen
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China
- State Key Laboratory of Cognitive Intelligence Hefei Anhui 230088 China
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15
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Schilter O, Vaucher A, Schwaller P, Laino T. Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions. DIGITAL DISCOVERY 2023; 2:728-735. [PMID: 37312682 PMCID: PMC10259369 DOI: 10.1039/d2dd00125j] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/22/2023] [Indexed: 06/15/2023]
Abstract
The need for more efficient catalytic processes is ever-growing, and so are the costs associated with experimentally searching chemical space to find new promising catalysts. Despite the consolidated use of density functional theory (DFT) and other atomistic models for virtually screening molecules based on their simulated performance, data-driven approaches are rising as indispensable tools for designing and improving catalytic processes. Here, we present a deep learning model capable of generating new catalyst-ligand candidates by self-learning meaningful structural features solely from their language representation and computed binding energies. We train a recurrent neural network-based Variational Autoencoder (VAE) to compress the molecular representation of the catalyst into a lower dimensional latent space, in which a feed-forward neural network predicts the corresponding binding energy to be used as the optimization function. The outcome of the optimization in the latent space is then reconstructed back into the original molecular representation. These trained models achieve state-of-the-art predictive performances in catalysts' binding energy prediction and catalysts' design, with a mean absolute error of 2.42 kcal mol-1 and an ability to generate 84% valid and novel catalysts.
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Affiliation(s)
- Oliver Schilter
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | - Alain Vaucher
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | - Philippe Schwaller
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | - Teodoro Laino
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
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16
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Yang KR, Kyro GW, Batista VS. The landscape of computational approaches for artificial photosynthesis. NATURE COMPUTATIONAL SCIENCE 2023; 3:504-513. [PMID: 38177419 DOI: 10.1038/s43588-023-00450-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/11/2023] [Indexed: 01/06/2024]
Abstract
Artificial photosynthesis is an attractive strategy for converting solar energy into fuels, largely because the Earth receives enough solar energy in one hour to meet humanity's energy needs for an entire year. However, developing devices for artificial photosynthesis remains difficult and requires computational approaches to guide and assist the interpretation of experiments. In this Perspective, we discuss current and future computational approaches, as well as the challenges of designing and characterizing molecular assemblies that absorb solar light, transfer electrons between interfaces, and catalyze water-splitting and fuel-forming reactions.
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Affiliation(s)
- Ke R Yang
- Department of Chemistry, Yale University, New Haven, CT, USA
- Energy Sciences Institute, Yale University, West Haven, CT, USA
| | - Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, CT, USA.
- Energy Sciences Institute, Yale University, West Haven, CT, USA.
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17
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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18
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Desmedt E, Smets D, Woller T, Alonso M, De Vleeschouwer F. Designing hexaphyrins for high-potential NLO switches: the synergy of core-modifications and meso-substitutions. Phys Chem Chem Phys 2023. [PMID: 37162298 DOI: 10.1039/d3cp01240a] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Due to the enormous size of the chemical compound space, usually only small regions are traversed with traditional direct molecular design approaches making the discovery for novel functionalized molecules for nonlinear optical applications challenging. By applying inverse molecular design algorithms, we aim to efficiently explore larger regions of the compound space in search of promising hexaphyrin-based molecular switches as measured by their first-hyperpolarizability (βHRS) contrast. We focus on the 28R → 30R switch with a functionalization pattern allowing for centrosymmetric OFF states yielding zero βHRS response. This switch is particularly challenging as full meso-substitution with a single type of functional group or core-modifications result in almost no contrast enhancement. We carried out four inverse design procedures during which two sets of core-modifications and three sets of meso-substitutions sites were systematically optimized. All 4 optimal switches are characterized by a mix of meso-substitutions and core-modifications, of which the best performing switch yields a 10-fold improvement over the parent macrocycle. Throughout the inverse design procedures, we collected and analyzed a database biased towards high NLO contrasts that contains 277 different patterns for hexaphyrin-based switches. We derived three design rules to obtain highly functional 28R → 30R NLO switches: (I) a combination of 2 strong EWG and 1 EDG group is the ideal recipe for increasing the NLO contrast, though their position also plays an important role. (II) The type of core-modification is less important when only the diagonal positions are core-modified. Switches with 4 core-modifications show a clear preference for oxygen. (III) Keeping centrosymmetry in the OFF state remains highly beneficial given the investigated functionalization pattern. Finally, we have demonstrated that combining meso-substitutions with core-modifications can synergistically improve the NLO contrast.
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Affiliation(s)
- Eline Desmedt
- Department of General Chemistry Algemene Chemie (ALGC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
| | - David Smets
- Department of General Chemistry Algemene Chemie (ALGC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
| | - Tatiana Woller
- Department of General Chemistry Algemene Chemie (ALGC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
| | - Mercedes Alonso
- Department of General Chemistry Algemene Chemie (ALGC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
| | - Freija De Vleeschouwer
- Department of General Chemistry Algemene Chemie (ALGC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium.
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19
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Liu Y, Meitei OR, Chin ZE, Dutt A, Tao M, Chuang IL, Van Voorhis T. Bootstrap Embedding on a Quantum Computer. J Chem Theory Comput 2023; 19:2230-2247. [PMID: 37001026 DOI: 10.1021/acs.jctc.3c00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
We extend molecular bootstrap embedding to make it appropriate for implementation on a quantum computer. This enables solution of the electronic structure problem of a large molecule as an optimization problem for a composite Lagrangian governing fragments of the total system, in such a way that fragment solutions can harness the capabilities of quantum computers. By employing state-of-art quantum subroutines including the quantum SWAP test and quantum amplitude amplification, we show how a quadratic speedup can be obtained over the classical algorithm, in principle. Utilization of quantum computation also allows the algorithm to match─at little additional computational cost─full density matrices at fragment boundaries, instead of being limited to 1-RDMs. Current quantum computers are small, but quantum bootstrap embedding provides a potentially generalizable strategy for harnessing such small machines through quantum fragment matching.
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Affiliation(s)
- Yuan Liu
- Department of Physics, Co-Design Center for Quantum Advantage, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Oinam R. Meitei
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Zachary E. Chin
- Department of Physics, Co-Design Center for Quantum Advantage, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Arkopal Dutt
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Max Tao
- Department of Physics, Co-Design Center for Quantum Advantage, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Isaac L. Chuang
- Department of Physics, Co-Design Center for Quantum Advantage, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Troy Van Voorhis
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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20
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Alegre‐Requena JV, Sowndarya S. V. S, Pérez‐Soto R, Alturaifi TM, Paton RS. AQME: Automated quantum mechanical environments for researchers and educators. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- Juan V. Alegre‐Requena
- Dpto. de Química Inorgánica Instituto de Síntesis Química y Catálisis Homogénea (ISQCH) CSIC‐Universidad de Zaragoza Zaragoza Spain
| | | | - Raúl Pérez‐Soto
- Department of Chemistry Colorado State University Fort Collins Colorado USA
| | - Turki M. Alturaifi
- Department of Chemistry Colorado State University Fort Collins Colorado USA
| | - Robert S. Paton
- Department of Chemistry Colorado State University Fort Collins Colorado USA
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21
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Photo-Antibacterial Activity of Two-Dimensional (2D)-Based Hybrid Materials: Effective Treatment Strategy for Controlling Bacterial Infection. Antibiotics (Basel) 2023; 12:antibiotics12020398. [PMID: 36830308 PMCID: PMC9952232 DOI: 10.3390/antibiotics12020398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023] Open
Abstract
Bacterial contamination in water bodies is a severe scourge that affects human health and causes mortality and morbidity. Researchers continue to develop next-generation materials for controlling bacterial infections from water. Photo-antibacterial activity continues to gain the interest of researchers due to its adequate, rapid, and antibiotic-free process. Photo-antibacterial materials do not have any side effects and have a minimal chance of developing bacterial resistance due to their rapid efficacy. Photocatalytic two-dimensional nanomaterials (2D-NMs) have great potential for the control of bacterial infection due to their exceptional properties, such as high surface area, tunable band gap, specific structure, and tunable surface functional groups. Moreover, the optical and electric properties of 2D-NMs might be tuned by creating heterojunctions or by the doping of metals/carbon/polymers, subsequently enhancing their photo-antibacterial ability. This review article focuses on the synthesis of 2D-NM-based hybrid materials, the effect of dopants in 2D-NMs, and their photo-antibacterial application. We also discuss how we could improve photo-antibacterials by using different strategies and the role of artificial intelligence (AI) in the photocatalyst and in the degradation of pollutants. Finally, we discuss was of improving the photo-antibacterial activity of 2D-NMs, the toxicity mechanism, and their challenges.
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22
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Rosen AS, Vijay S, Persson KA. Free-atom-like d states beyond the dilute limit of single-atom alloys. Chem Sci 2023; 14:1503-1511. [PMID: 36794204 PMCID: PMC9906637 DOI: 10.1039/d2sc05772g] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/13/2023] [Indexed: 01/21/2023] Open
Abstract
Through a data-mining and high-throughput density functional theory approach, we identify a diverse range of metallic compounds that are predicted to have transition metals with "free-atom-like" d states that are highly localized in terms of their energetic distribution. Design principles that favor the formation of localized d states are uncovered, among which we note that site isolation is often necessary but that the dilute limit, as in most single-atom alloys, is not a pre-requisite. Additionally, the majority of localized d state transition metals identified from the computational screening study exhibit partial anionic character due to charge transfer from neighboring metal species. Using CO as a representative probe molecule, we show that localized d states for Rh, Ir, Pd, and Pt tend to reduce the binding strength of CO compared to their pure elemental analogues, whereas this does not occur as consistently for the Cu binding sites. These trends are rationalized through the d-band model, which suggests that the significantly reduced d-band width results in an increased orthogonalization energy penalty upon CO chemisorption. With the multitude of inorganic solids that are predicted to have highly localized d states, the results of the screening study are likely to result in new avenues for heterogeneous catalyst design from an electronic structure perspective.
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Affiliation(s)
- Andrew S. Rosen
- Department of Materials Science and Engineering, University of California, BerkeleyBerkeleyCalifornia94720USA,Miller Institute for Basic Research in Science, University of California, BerkeleyBerkeleyCalifornia 94720USA,Materials Science Division, Lawrence Berkeley National LaboratoryBerkeleyCalifornia 94720USA
| | - Sudarshan Vijay
- Department of Materials Science and Engineering, University of California, BerkeleyBerkeleyCalifornia94720USA,Materials Science Division, Lawrence Berkeley National LaboratoryBerkeleyCalifornia 94720USA
| | - Kristin A. Persson
- Department of Materials Science and Engineering, University of California, BerkeleyBerkeleyCalifornia94720USA,Molecular Foundry, Lawrence Berkeley National LaboratoryBerkeleyCalifornia 94720USA
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23
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Tu Z, Stuyver T, Coley CW. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery. Chem Sci 2023; 14:226-244. [PMID: 36743887 PMCID: PMC9811563 DOI: 10.1039/d2sc05089g] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.
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Affiliation(s)
- Zhengkai Tu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Thijs Stuyver
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Connor W Coley
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
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24
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Zang X, Zhou X, Bian H, Jin W, Pan X, Jiang J, Koroleva MY, Shen R. Prediction and Construction of Energetic Materials Based on Machine Learning Methods. MOLECULES (BASEL, SWITZERLAND) 2022; 28:molecules28010322. [PMID: 36615516 PMCID: PMC9821915 DOI: 10.3390/molecules28010322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/18/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023]
Abstract
Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed.
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Affiliation(s)
- Xiaowei Zang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Xiang Zhou
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Haitao Bian
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Weiping Jin
- Jiangxi Xinyu Guoke Technology Co., Ltd., Xinyu 338018, China
| | - Xuhai Pan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Juncheng Jiang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
| | - M. Yu. Koroleva
- Institute of Modern Energetics and Nanomaterials, D. Mendeleev University of Chemical Technology of Russia, Moscow 125047, Russia
| | - Ruiqi Shen
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
- Micro-Nano Energetic Devices Key Laboratory of MIIT, Nanjing 210094, China
- Institute of Space Propulsion, Nanjing University of Science and Technology, Nanjing 210094, China
- Correspondence:
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25
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Zhang W, Huang W, Tan J, Guo Q, Wu B. Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects. CHEMOSPHERE 2022; 308:136447. [PMID: 36116627 DOI: 10.1016/j.chemosphere.2022.136447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Qingwei Guo
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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26
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Cheng L, Sun J, Deustua JE, Bhethanabotla VC, Miller TF. Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression. J Chem Phys 2022; 157:154105. [PMID: 36272799 DOI: 10.1063/5.0110886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML(KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, and GDB-13-T) and open-shell (QMSpin) molecules.
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Affiliation(s)
- Lixue Cheng
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Jiace Sun
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - J Emiliano Deustua
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Vignesh C Bhethanabotla
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Thomas F Miller
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
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27
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Shiraogawa T, Dall'Osto G, Cammi R, Ehara M, Corni S. Inverse design of molecule-metal nanoparticle systems interacting with light for desired photophysical properties. Phys Chem Chem Phys 2022; 24:22768-22777. [PMID: 36111742 DOI: 10.1039/d2cp02870k] [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
Molecules close to a metal nanoparticle (NP) have significantly different photophysical properties from those of the isolated one. In order to harness the potential of the molecule-NP system, appropriate design guidelines are required. Here, we propose an inverse design method of the optimal molecule-NP systems and incident electric field for desired photophysical properties. It is based on a gradient-based optimization search within the time-dependent quantum chemical description for the molecule and the continuum model for the metal NP. We designed the optimal molecule, relative molecule-NP spatial conformation, and incident electric field of a molecule-NP system to maximize the population transfer to the target electronic state of the molecule. The design results were presented and discussed. The present method is promising as the basis for designing molecule-NP systems and incident fields and accelerates discoveries of efficient molecular plasmonics systems.
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Affiliation(s)
- Takafumi Shiraogawa
- SOKENDAI, The Graduate University for Advanced Studies, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan.
| | - Giulia Dall'Osto
- Department of Chemical Sciences, University of Padova, via Marzolo 1, Padova, Italy
| | - Roberto Cammi
- Department of Chemical Science, Life Science and Environmental Sustainability, University of Parma, Parma, Italy
| | - Masahiro Ehara
- SOKENDAI, The Graduate University for Advanced Studies, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan. .,Institute for Molecular Science and Research Center for Computational Science, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan. .,Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Kyoto, 615-8245, Japan
| | - Stefano Corni
- Department of Chemical Sciences, University of Padova, via Marzolo 1, Padova, Italy.,CNR Institute of Nanoscience, via Campi 213/A, Modena, Italy.
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28
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Wang Z, Sun Z, Yin H, Liu X, Wang J, Zhao H, Pang CH, Wu T, Li S, Yin Z, Yu XF. Data-Driven Materials Innovation and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104113. [PMID: 35451528 DOI: 10.1002/adma.202104113] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 03/19/2022] [Indexed: 05/07/2023]
Abstract
Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.
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Affiliation(s)
- Zhuo Wang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Zhehao Sun
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Hang Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xinghui Liu
- Department of Chemistry, Sungkyunkwan University (SKKU), 2066 Seoburo, Jangan-Gu, Suwon, 16419, Republic of Korea
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, P. R. China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Cheng Heng Pang
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- Municipal Key Laboratory of Clean Energy Conversion Technologies, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Tao Wu
- Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- New Materials Institute, University of Nottingham, Ningbo, China, Ningbo, 315100, P. R. China
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zongyou Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
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Sun J, Cheng L, Miller TF. Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning. J Chem Phys 2022; 157:104109. [DOI: 10.1063/5.0101280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree--Fock computations. A molecular-orbital-based (MOB) pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of molecular orbitals (MOs).The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant Gaussian process regression (GPR) with derivatives algorithm is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly-accurate models for both dipole moment and energies on water and fourteen small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering (GMM/GPR) scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with literature results, MOB-ML provides the best test MAEs of 4.21 mDebye and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides.
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Affiliation(s)
- Jiace Sun
- Chemistry and Chemical Engineering, California Institute of Technology, United States of America
| | - Lixue Cheng
- Chemistry, California Institute of Technology, United States of America
| | - Thomas F Miller
- Division of Chemistry and Chemical Engineering, California Institute of Technology, United States of America
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30
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Multivariate optimization of the electrochemical degradation for COD and TN removal from wastewater: An inverse computation machine learning approach. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.121129] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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31
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Nandakumar K, Tyagi M, Xu Y, Valsaraj KT, Joshi JB. Chemical Engineering at Crossroads. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- K. Nandakumar
- Cain Department of Chemical Engineering Louisiana State University Baton Rouge LA USA
| | - Mayank Tyagi
- Cain Department of Chemical Engineering Louisiana State University Baton Rouge LA USA
| | - Ye Xu
- Cain Department of Chemical Engineering Louisiana State University Baton Rouge LA USA
| | - K. T. Valsaraj
- Cain Department of Chemical Engineering Louisiana State University Baton Rouge LA USA
| | - J. B. Joshi
- J. B. Joshi Research Foundation, 401, Shubh Ashirwad Society, 5th Lane, Hindu Colony, Dadar (E) Mumbai India
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32
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Ren C, Lu S, Wu Y, Ouyang Y, Zhang Y, Li Q, Ling C, Wang J. A Universal Descriptor for Complicated Interfacial Effects on Electrochemical Reduction Reactions. J Am Chem Soc 2022; 144:12874-12883. [PMID: 35700099 DOI: 10.1021/jacs.2c04540] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Supported catalysts have exhibited excellent performance in various reactions. However, the rational design of supported catalysts with high activity and certain selectivity remains a great challenge because of the complicated interfacial effects. Using recently emerged two-dimensional materials supported dual-atom catalysts (DACs@2D) as a prototype, we propose a simple and universal descriptor based on inherent atomic properties (electronegativity, electron type, and number), which can well evaluate the complicated interfacial effects on the electrochemical reduction reactions (i.e., CO2, O2, and N2 reduction reactions). Based on this descriptor, activity and selectivity trends in CO2 reduction reaction are successfully elucidated, in good agreement with available experimental data. Moreover, several potential catalysts with superior activity and selectivity for target products are predicted, such as CuCr/g-C3N4 for CH4 and CuSn/N-BN for HCOOH. More importantly, this descriptor can also be extended to evaluate the activity of DACs@2D for O2 and N2 reduction reactions, with very small errors between the prediction and reported experimental/computational results. This work provides feasible principles for the rational design of advanced electrocatalysts and the construction of universal descriptors based on inherent atomic properties.
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Affiliation(s)
- Chunjin Ren
- School of Physics, Southeast University, Nanjing 211189, China
| | - Shuaihua Lu
- School of Physics, Southeast University, Nanjing 211189, China
| | - Yilei Wu
- 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
| | - Chongyi Ling
- School of Physics, Southeast University, Nanjing 211189, China
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing 211189, China
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33
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Lu S, Zhou Q, Chen X, Song Z, Wang J. Inverse design with deep generative models: next step in materials discovery. Natl Sci Rev 2022; 9:nwac111. [PMID: 35992238 PMCID: PMC9385454 DOI: 10.1093/nsr/nwac111] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Shuaihua Lu
- School of Physics, Southeast University , China
| | | | - Xinyu Chen
- School of Physics, Southeast University , China
| | | | - Jinlan Wang
- School of Physics, Southeast University , China
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34
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Chatenet M, Pollet BG, Dekel DR, Dionigi F, Deseure J, Millet P, Braatz RD, Bazant MZ, Eikerling M, Staffell I, Balcombe P, Shao-Horn Y, Schäfer H. Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments. Chem Soc Rev 2022; 51:4583-4762. [PMID: 35575644 PMCID: PMC9332215 DOI: 10.1039/d0cs01079k] [Citation(s) in RCA: 167] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Indexed: 12/23/2022]
Abstract
Replacing fossil fuels with energy sources and carriers that are sustainable, environmentally benign, and affordable is amongst the most pressing challenges for future socio-economic development. To that goal, hydrogen is presumed to be the most promising energy carrier. Electrocatalytic water splitting, if driven by green electricity, would provide hydrogen with minimal CO2 footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering specifications and special features of the current industrial processes and those processes currently being tested in large-scale applications. Recently developed strategies are described for the optimisation and discovery of active and durable materials for electrodes that ever-increasingly harness first-principles calculations and machine learning. In addition, a technoeconomic analysis of water electrolysis is included that allows an assessment of the extent to which a large-scale implementation of water splitting can help to combat climate change. This review article is intended to cross-pollinate and strengthen efforts from fundamental understanding to technical implementation and to improve the 'junctions' between the field's physical chemists, materials scientists and engineers, as well as stimulate much-needed exchange among these groups on challenges encountered in the different domains.
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Affiliation(s)
- Marian Chatenet
- University Grenoble Alpes, University Savoie Mont Blanc, CNRS, Grenoble INP (Institute of Engineering and Management University Grenoble Alpes), LEPMI, 38000 Grenoble, France
| | - Bruno G Pollet
- Hydrogen Energy and Sonochemistry Research group, Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU) NO-7491, Trondheim, Norway
- Green Hydrogen Lab, Institute for Hydrogen Research (IHR), Université du Québec à Trois-Rivières (UQTR), 3351 Boulevard des Forges, Trois-Rivières, Québec G9A 5H7, Canada
| | - Dario R Dekel
- The Wolfson Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
- The Nancy & Stephen Grand Technion Energy Program (GTEP), Technion - Israel Institute of Technology, Haifa 3200003, Israel
| | - Fabio Dionigi
- Department of Chemistry, Chemical Engineering Division, Technical University Berlin, 10623, Berlin, Germany
| | - Jonathan Deseure
- University Grenoble Alpes, University Savoie Mont Blanc, CNRS, Grenoble INP (Institute of Engineering and Management University Grenoble Alpes), LEPMI, 38000 Grenoble, France
| | - Pierre Millet
- Paris-Saclay University, ICMMO (UMR 8182), 91400 Orsay, France
- Elogen, 8 avenue du Parana, 91940 Les Ulis, France
| | - Richard D Braatz
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Martin Z Bazant
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Michael Eikerling
- Chair of Theory and Computation of Energy Materials, Division of Materials Science and Engineering, RWTH Aachen University, Intzestraße 5, 52072 Aachen, Germany
- Institute of Energy and Climate Research, IEK-13: Modelling and Simulation of Materials in Energy Technology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Iain Staffell
- Centre for Environmental Policy, Imperial College London, London, UK
| | - Paul Balcombe
- Division of Chemical Engineering and Renewable Energy, School of Engineering and Material Science, Queen Mary University of London, London, UK
| | - Yang Shao-Horn
- Research Laboratory of Electronics and Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Helmut Schäfer
- Institute of Chemistry of New Materials, The Electrochemical Energy and Catalysis Group, University of Osnabrück, Barbarastrasse 7, 49076 Osnabrück, Germany.
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35
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Prabhu AM, Choksi TS. Data-driven methods to predict the stability metrics of catalytic nanoparticles. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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36
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Pahija E, Panaritis C, Gusarov S, Shadbahr J, Bensebaa F, Patience G, Boffito DC. Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Ergys Pahija
- Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Station Centre-Ville, Montréal, Québec H3C 3A7, Canada
| | - Christopher Panaritis
- Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Station Centre-Ville, Montréal, Québec H3C 3A7, Canada
| | - Sergey Gusarov
- Nanotechnology Research Center, National Research Council of Canada, 11421 Saskatchewan Drive, Edmonton, Alberta T6G 2M9, Canada
| | - Jalil Shadbahr
- Energy, Mining and Environment Research Centre, National Research Council Canada, Ottawa, Ontario K1A 0R6, Canada
| | - Farid Bensebaa
- Energy, Mining and Environment Research Centre, National Research Council Canada, Ottawa, Ontario K1A 0R6, Canada
| | - Gregory Patience
- Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Station Centre-Ville, Montréal, Québec H3C 3A7, Canada
| | - Daria Camilla Boffito
- Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Station Centre-Ville, Montréal, Québec H3C 3A7, Canada
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37
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Hiener DC, Hutchison GR. Pareto Optimization of Oligomer Polarizability and Dipole Moment Using a Genetic Algorithm. J Phys Chem A 2022; 126:2750-2760. [PMID: 35471827 DOI: 10.1021/acs.jpca.2c01266] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.
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Affiliation(s)
- Danielle C Hiener
- Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
| | - Geoffrey R Hutchison
- Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.,Department of Chemical and Petroleum Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, Pennsylvania 15261, United States
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38
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Abstract
We propose to relax geometries throughout chemical compound space (CCS) using alchemical perturbation density functional theory (APDFT). APDFT refers to perturbation theory involving changes in nuclear charges within approximate solutions to Schr\"odinger's equation. We give an analytical formula to calculate the mixed second order energy derivatives with respect to both, nuclear charges and nuclear positions (named "alchemical force"), within the restricted Hartree-Fock case.We have implemented and studied the formula for its use in geometry relaxation of various reference and target molecules.We have also analysed the convergence of the alchemical force perturbation series, as well as basis set effects.Interpolating alchemically predicted energies, forces, and Hessian to a Morse potential yields more accurate geometries and equilibrium energies than when performing a standard Newton Raphson step. Our numerical predictions for small molecules including BF, CO, N2, CH$_4$, NH$_3$, H$_2$O, and HF yield mean absolute errors of of equilibrium energies and bond lengths smaller than 10 mHa and 0.01 Bohr for 4$^\text{th}$ order APDFT predictions, respectively.Our alchemical geometry relaxation still preserves the combinatorial efficiency of APDFT: Based on a single coupled perturbed Hartree Fock derivative for benzene we provide numerical predictions of equilibrium energies and relaxed structures of all the 17 iso-electronic charge-netural BN-doped mutants with averaged absolute deviations of $\sim$27 mHa and $\sim$0.12 Bohr, respectively.
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39
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40
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Duan C, Nandy A, Kulik HJ. Machine Learning for the Discovery, Design, and Engineering of Materials. Annu Rev Chem Biomol Eng 2022; 13:405-429. [PMID: 35320698 DOI: 10.1146/annurev-chembioeng-092320-120230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , ,
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41
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Gebauer NWA, Gastegger M, Hessmann SSP, Müller KR, Schütt KT. Inverse design of 3d molecular structures with conditional generative neural networks. Nat Commun 2022; 13:973. [PMID: 35190542 PMCID: PMC8861047 DOI: 10.1038/s41467-022-28526-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/28/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractThe rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.
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42
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Affiliation(s)
- Leo H. Chiang
- Core R&D The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Birgit Braun
- Core R&D The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Zhenyu Wang
- Chemometrics, AI & Statistics The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Ivan Castillo
- Chemometrics, AI & Statistics The Dow Chemical Company Lake Jackson Texas 77566 USA
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43
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Eisenstein O. From the Felkin‐Anh Rule to the Grignard Reaction: an Almost Circular 50 Year Adventure in the World of Molecular Structures and Reaction Mechanisms with Computational Chemistry**. Isr J Chem 2022. [DOI: 10.1002/ijch.202100138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Odile Eisenstein
- ICGM, Univ. Montpellier, CNRS, ENSCM, Montpellier, 34095 France Department of Chemistry and Hylleraas Centre for Quantum Molecular Sciences University of Oslo Oslo 0315 Norway
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Chaikittisilp W, Yamauchi Y, Ariga K. Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials? ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107212. [PMID: 34637159 DOI: 10.1002/adma.202107212] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/05/2021] [Indexed: 05/27/2023]
Abstract
Materials science and chemistry have played a central and significant role in advancing society. With the shift toward sustainable living, it is anticipated that the development of functional materials will continue to be vital for sustaining life on our planet. In the recent decades, rapid progress has been made in materials science and chemistry owing to the advances in experimental, analytical, and computational methods, thereby producing several novel and useful materials. However, most problems in material development are highly complex. Here, the best strategy for the development of functional materials via the implementation of three key concepts is discussed: nanotechnology as a game changer, nanoarchitectonics as an integrator, and materials informatics as a super-accelerator. Discussions from conceptual viewpoints and example recent developments, chiefly focused on nanoporous materials, are presented. It is anticipated that coupling these three strategies together will open advanced routes for the swift design and exploratory search of functional materials truly useful for solving real-world problems. These novel strategies will result in the evolution of nanoporous functional materials.
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Affiliation(s)
- Watcharop Chaikittisilp
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Yusuke Yamauchi
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Australian Institute for Bioengineering and Nanotechnology (AIBN) and School of Chemical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Katsuhiko Ariga
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan
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Harper DR, Nandy A, Arunachalam N, Duan C, Janet JP, Kulik HJ. Representations and strategies for transferable machine learning Improve model performance in chemical discovery. J Chem Phys 2022; 156:074101. [DOI: 10.1063/5.0082964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Daniel R Harper
- Massachusetts Institute of Technology, United States of America
| | - Aditya Nandy
- Massachusetts Institute of Technology, United States of America
| | | | - Chenru Duan
- Massachusetts Institute of Technology, United States of America
| | | | - Heather J. Kulik
- Dept of Chemical Engineering, Massachusetts Institute of Technology, United States of America
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46
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Cui Q, Peng J, Xu C, Lan Z. Automatic Approach to Explore the Multireaction Mechanism for Medium-Sized Bimolecular Reactions via Collision Dynamics Simulations and Transition State Searches. J Chem Theory Comput 2022; 18:910-924. [DOI: 10.1021/acs.jctc.1c00795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qinghai Cui
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
- Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education; School of Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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Steiner M, Reiher M. Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis. Top Catal 2022; 65:6-39. [PMID: 35185305 PMCID: PMC8816766 DOI: 10.1007/s11244-021-01543-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 12/11/2022]
Abstract
Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.
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Affiliation(s)
- Miguel Steiner
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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Chen L, Zhang X, Chen A, Yao S, Hu X, Zhou Z. Targeted design of advanced electrocatalysts by machine learning. CHINESE JOURNAL OF CATALYSIS 2022. [DOI: 10.1016/s1872-2067(21)63852-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Trunschke A. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00275b] [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
Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.
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Affiliation(s)
- Annette Trunschke
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Department of Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
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Desmedt E, Woller T, Teunissen JL, De Vleeschouwer F, Alonso M. Fine-Tuning of Nonlinear Optical Contrasts of Hexaphyrin-Based Molecular Switches Using Inverse Design. Front Chem 2021; 9:786036. [PMID: 34926405 PMCID: PMC8677951 DOI: 10.3389/fchem.2021.786036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
In the search for new nonlinear optical (NLO) switching devices, expanded porphyrins have emerged as ideal candidates thanks to their tunable chemical and photophysical properties. Introducing meso-substituents to these macrocycles is a successful strategy to enhance the NLO contrasts. Despite its potential, the influence of meso-substitution on their structural and geometrical properties has been scarcely investigated. In this work, we pursue to grasp the underlying pivotal concepts for the fine-tuning of the NLO contrasts of hexaphyrin-based molecular switches, with a particular focus on the first hyperpolarizability related to the hyper-Rayleigh scattering (βHRS). Building further on these concepts, we also aim to develop a rational design protocol. Starting from the (un)substituted hexaphyrins with various π-conjugation topologies and redox states, structure-property relationships are established linking aromaticity, photophysical properties and βHRS responses. Ultimately, inverse molecular design using the best-first search algorithm is applied on the most favorable switches with the aim to further explore the combinatorial chemical compound space of meso-substituted hexaphyrins in search of high-contrast NLO switches. Two definitions of the figure-of-merit of the switch performance were used as target objectives in the optimization problem. Several meso-substitution patterns and their underlying characteristics are identified, uncovering molecular symmetry and the electronic nature of the substituents as the key players for fine-tuning the βHRS values and NLO contrasts of hexaphyrin-based switches.
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Affiliation(s)
- Eline Desmedt
- General Chemistry - Eenheid Algemene Chemie (ALGC), Department of Chemistry, Vrije Universiteit Brussel, Brussels, Belgium
| | - Tatiana Woller
- General Chemistry - Eenheid Algemene Chemie (ALGC), Department of Chemistry, Vrije Universiteit Brussel, Brussels, Belgium
| | - Jos L Teunissen
- General Chemistry - Eenheid Algemene Chemie (ALGC), Department of Chemistry, Vrije Universiteit Brussel, Brussels, Belgium
| | - Freija De Vleeschouwer
- General Chemistry - Eenheid Algemene Chemie (ALGC), Department of Chemistry, Vrije Universiteit Brussel, Brussels, Belgium
| | - Mercedes Alonso
- General Chemistry - Eenheid Algemene Chemie (ALGC), Department of Chemistry, Vrije Universiteit Brussel, Brussels, Belgium
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