1
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Fu W, Mo Y, Xiao Y, Liu C, Zhou F, Wang Y, Zhou J, Zhang YJ. Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential. J Chem Theory Comput 2024; 20:4533-4544. [PMID: 38828925 DOI: 10.1021/acs.jctc.3c01181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent graph neural network-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems.
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Affiliation(s)
- Weiqiang Fu
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yujie Mo
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yi Xiao
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Chang Liu
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Feng Zhou
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yang Wang
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Jielong Zhou
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yingsheng J Zhang
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
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2
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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3
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Rutskoy B, Ozerov G, Bezrukov D. The Role of Bond Functions in Describing Intermolecular Electron Correlation for Van der Waals Dimers: A Study of (CH 4) 2 and Ne 2. Int J Mol Sci 2024; 25:1472. [PMID: 38338750 PMCID: PMC10855067 DOI: 10.3390/ijms25031472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
We present a study of the intermolecular interactions in van der Waals complexes of methane and neon dimers within the framework of the CCSD method. This approach was implemented and applied to calculate and examine the behavior of the contracted two-particle reduced density matrix (2-RDM). It was demonstrated that the region near the minimum of the two-particle density matrix correlation part, corresponding to the primary bulk of the Coulomb hole contribution, exerts a significant influence on the dispersion interaction energetics of the studied systems. As a result, the bond functions approach was applied to improve the convergence performance for the intermolecular correlation energy results with respect to the size of the atomic basis. For this, substantial acceleration was achieved by introducing an auxiliary basis of bond functions centered on the minima of the 2-RDM. For both methane and neon dimers, this general conclusion was confirmed with a series of CCSD calculations for the 2-RDM and the correlation energies.
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Affiliation(s)
- Bogdan Rutskoy
- National Research Centre “Kurchatov Institute”, Moscow 123182, Russia;
- Institute of Nuclear Physics and Technology, National Research Nuclear University “MEPhI” (Moscow Engineering Physics Institute), Moscow 115409, Russia
- Chemistry Department, M.V. Lomonosov Moscow State University, Moscow 119991, Russia;
| | - Georgiy Ozerov
- Chemistry Department, M.V. Lomonosov Moscow State University, Moscow 119991, Russia;
| | - Dmitry Bezrukov
- Chemistry Department, M.V. Lomonosov Moscow State University, Moscow 119991, Russia;
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4
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Hasan MN, Ray M, Saha A. Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery. J Phys Chem B 2023; 127:9663-9684. [PMID: 37921534 DOI: 10.1021/acs.jpcb.3c04710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.
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Affiliation(s)
- Md Nazmul Hasan
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
| | - Manisha Ray
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Arjun Saha
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
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5
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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6
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Ashraf M, Ahmad MS, Inomata Y, Ullah N, Tahir MN, Kida T. Transition metal nanoparticles as nanocatalysts for Suzuki, Heck and Sonogashira cross-coupling reactions. Coord Chem Rev 2023. [DOI: 10.1016/j.ccr.2022.214928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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7
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Fan X, Zhong C, Liu J, Ding J, Deng Y, Han X, Zhang L, Hu W, Wilkinson DP, Zhang J. Opportunities of Flexible and Portable Electrochemical Devices for Energy Storage: Expanding the Spotlight onto Semi-solid/Solid Electrolytes. Chem Rev 2022; 122:17155-17239. [PMID: 36239919 DOI: 10.1021/acs.chemrev.2c00196] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The ever-increasing demand for flexible and portable electronics has stimulated research and development in building advanced electrochemical energy devices which are lightweight, ultrathin, small in size, bendable, foldable, knittable, wearable, and/or stretchable. In such flexible and portable devices, semi-solid/solid electrolytes besides anodes and cathodes are the necessary components determining the energy/power performances. By serving as the ion transport channels, such semi-solid/solid electrolytes may be beneficial to resolving the issues of leakage, electrode corrosion, and metal electrode dendrite growth. In this paper, the fundamentals of semi-solid/solid electrolytes (e.g., chemical composition, ionic conductivity, electrochemical window, mechanical strength, thermal stability, and other attractive features), the electrode-electrolyte interfacial properties, and their relationships with the performance of various energy devices (e.g., supercapacitors, secondary ion batteries, metal-sulfur batteries, and metal-air batteries) are comprehensively reviewed in terms of materials synthesis and/or characterization, functional mechanisms, and device assembling for performance validation. The most recent advancements in improving the performance of electrochemical energy devices are summarized with focuses on analyzing the existing technical challenges (e.g., solid electrolyte interphase formation, metal electrode dendrite growth, polysulfide shuttle issue, electrolyte instability in half-open battery structure) and the strategies for overcoming these challenges through modification of semi-solid/solid electrolyte materials. Several possible directions for future research and development are proposed for going beyond existing technological bottlenecks and achieving desirable flexible and portable electrochemical energy devices to fulfill their practical applications. It is expected that this review may provide the readers with a comprehensive cross-technology understanding of the semi-solid/solid electrolytes for facilitating their current and future researches on the flexible and portable electrochemical energy devices.
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Affiliation(s)
- Xiayue Fan
- Key Laboratory of Advanced Ceramics and Machining Technology (Ministry of Education), School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
| | - Cheng Zhong
- Key Laboratory of Advanced Ceramics and Machining Technology (Ministry of Education), School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou350207, China
| | - Jie Liu
- Key Laboratory of Advanced Ceramics and Machining Technology (Ministry of Education), School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
| | - Jia Ding
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
| | - Yida Deng
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
| | - Xiaopeng Han
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
| | - Lei Zhang
- Energy, Mining & Environment, National Research Council of Canada, Vancouver, British ColumbiaV6T 1W5, Canada
| | - Wenbin Hu
- Key Laboratory of Advanced Ceramics and Machining Technology (Ministry of Education), School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin300072, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou350207, China
| | - David P Wilkinson
- Department of Chemical and Biochemical Engineering, University of British Columbia, Vancouver, British ColumbiaV6T 1W5, Canada
| | - Jiujun Zhang
- Energy, Mining & Environment, National Research Council of Canada, Vancouver, British ColumbiaV6T 1W5, Canada
- Department of Chemical and Biochemical Engineering, University of British Columbia, Vancouver, British ColumbiaV6T 1W5, Canada
- Institute for Sustainable Energy, College of Sciences, Shanghai University, Shanghai, 200444, China
- College of Materials Science and Engineering, Fuzhou University, Fuzhou350108, China
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8
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Kraka E, Quintano M, La Force HW, Antonio JJ, Freindorf M. The Local Vibrational Mode Theory and Its Place in the Vibrational Spectroscopy Arena. J Phys Chem A 2022; 126:8781-8798. [DOI: 10.1021/acs.jpca.2c05962] [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)
- Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Mateus Quintano
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Hunter W. La Force
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Juliana J. Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
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9
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Batalović K, Radaković J, Paskaš Mamula B, Kuzmanović B, Medić Ilić M. Predicting the Heat of Hydride Formation by Graph Neural Network ‐ Exploring the Structure–Property Relation for Metal Hydrides. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Katarina Batalović
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Jana Radaković
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Bojana Paskaš Mamula
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Bojana Kuzmanović
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Mirjana Medić Ilić
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
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10
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Darmawan KK, Karagiannis TC, Hughes JG, Small DM, Hung A. Molecular modeling of lactoferrin for food and nutraceutical applications: insights from in silico techniques. Crit Rev Food Sci Nutr 2022; 63:9074-9097. [PMID: 35503258 DOI: 10.1080/10408398.2022.2067824] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Lactoferrin is a protein, primarily found in milk that has attracted the interest of the food industries due to its health properties. Nevertheless, the instability of lactoferrin has limited its commercial application. Recent studies have focused on encapsulation to enhance the stability of lactoferrin. However, the molecular insights underlying the changes of structural properties of lactoferrin and the interaction with protectants remain poorly understood. Computational approaches have proven useful in understanding the structural properties of molecules and the key binding with other constituents. In this review, comprehensive information on the structure and function of lactoferrin and the binding with various molecules for food purposes are reviewed, with a special emphasis on the use of molecular dynamics simulations. The results demonstrate the application of modeling and simulations to determine key residues of lactoferrin responsible for its stability and interactions with other biomolecular components under various conditions, which are also associated with its functional benefits. These have also been extended into the potential creation of enhanced lactoferrin for commercial purposes. This review provides valuable strategies in designing novel nutraceuticals for food science practitioners and those who have interests in acquiring familiarity with the application of computational modeling for food and health purposes.
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Affiliation(s)
- Kevion K Darmawan
- School of Science, STEM College, RMIT University, Melbourne, Australia
| | - Tom C Karagiannis
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Australia
| | - Jeff G Hughes
- School of Science, STEM College, RMIT University, Melbourne, Australia
| | - Darryl M Small
- School of Science, STEM College, RMIT University, Melbourne, Australia
| | - Andrew Hung
- School of Science, STEM College, RMIT University, Melbourne, Australia
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11
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Dattila F, Seemakurthi RR, Zhou Y, López N. Modeling Operando Electrochemical CO 2 Reduction. Chem Rev 2022; 122:11085-11130. [PMID: 35476402 DOI: 10.1021/acs.chemrev.1c00690] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO2 reduction (eCO2R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO2R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.
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Affiliation(s)
- Federico Dattila
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain
| | - Ranga Rohit Seemakurthi
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain
| | - Yecheng Zhou
- School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510006, P. R. China
| | - Núria López
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain
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12
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Chen BWJ, Wang B, Sullivan MB, Borgna A, Zhang J. Unraveling the Synergistic Effect of Re and Cs Promoters on Ethylene Epoxidation over Silver Catalysts with Machine Learning-Accelerated First-Principles Simulations. ACS Catal 2022. [DOI: 10.1021/acscatal.1c05419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Benjamin W. J. Chen
- Agency for Science, Technology and Research, Institute of High Performance Computing, 1 Fusionopolis Way, #16−16 Connexis, Singapore 138632, Singapore
| | - Bo Wang
- Agency for Science, Technology and Research, Institute of Chemical and Engineering Sciences, 1 Pesek Road, Jurong Island, Singapore 627833, Singapore
| | - Michael B. Sullivan
- Agency for Science, Technology and Research, Institute of High Performance Computing, 1 Fusionopolis Way, #16−16 Connexis, Singapore 138632, Singapore
| | - Armando Borgna
- Agency for Science, Technology and Research, Institute of Chemical and Engineering Sciences, 1 Pesek Road, Jurong Island, Singapore 627833, Singapore
| | - Jia Zhang
- Agency for Science, Technology and Research, Institute of High Performance Computing, 1 Fusionopolis Way, #16−16 Connexis, Singapore 138632, Singapore
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13
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Wu R, Matta M, Paulsen BD, Rivnay J. Operando Characterization of Organic Mixed Ionic/Electronic Conducting Materials. Chem Rev 2022; 122:4493-4551. [PMID: 35026108 DOI: 10.1021/acs.chemrev.1c00597] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Operando characterization plays an important role in revealing the structure-property relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct observation of dynamic changes during device operation and thus guiding the development of new materials. This review focuses on the application of different operando characterization techniques in the study of OMIECs, highlighting the time-dependent and bias-dependent structure, composition, and morphology information extracted from these techniques. We first illustrate the needs, requirements, and challenges of operando characterization then provide an overview of relevant experimental techniques, including spectroscopy, scattering, microbalance, microprobe, and electron microscopy. We also compare different in silico methods and discuss the interplay of these computational methods with experimental techniques. Finally, we provide an outlook on the future development of operando for OMIEC-based devices and look toward multimodal operando techniques for more comprehensive and accurate description of OMIECs.
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Affiliation(s)
- Ruiheng Wu
- Department of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Micaela Matta
- Department of Chemistry, University of Liverpool, Liverpool L69 7ZD, United Kingdom
| | - Bryan D Paulsen
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Jonathan Rivnay
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States.,Simpson Querrey Institute, Northwestern University, Chicago, Illinois 60611, United States
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14
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Barros EP, Ries B, Böselt L, Champion C, Riniker S. Recent developments in multiscale free energy simulations. Curr Opin Struct Biol 2021; 72:55-62. [PMID: 34534706 DOI: 10.1016/j.sbi.2021.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/06/2021] [Accepted: 08/16/2021] [Indexed: 11/26/2022]
Abstract
Physics-based free energy simulations enable the rigorous calculation of properties, such as conformational equilibria, solvation or binding free energies. While historically most applications have occurred at the atomistic level of resolution, a range of advances in the past years make it possible now to reliably cross the temporal, spatial and theory scales for the modeling of complex systems or the efficient prediction of results at the accuracy level of expensive quantum-mechanical calculations. In this mini-review, we discuss recent methodological advances as well as opportunities opened up by the introduction of machine learning approaches, which tackle the diverse challenges across the different scales, improve the accuracy and feasibility, and push the boundaries of multiscale free energy simulations.
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Affiliation(s)
- Emilia P Barros
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Benjamin Ries
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Candide Champion
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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15
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Chen MS, Morawietz T, Mori H, Markland TE, Artrith N. AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials. J Chem Phys 2021; 155:074801. [PMID: 34418919 DOI: 10.1063/5.0063880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.
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Affiliation(s)
- Michael S Chen
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Tobias Morawietz
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Hideki Mori
- Department of Mechanical Engineering, College of Industrial Technology, 1-27-1 Nishikoya, Amagasaki, Hyogo 661-0047, Japan
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Nongnuch Artrith
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
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Miksch AM, Morawietz T, Kästner J, Urban A, Artrith N. Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abfd96] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (a) data collection, (b) model selection, (c) training and validation, and (d) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies.
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Kingdon ADH, Alderwick LJ. Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis. Comput Struct Biotechnol J 2021; 19:3708-3719. [PMID: 34285773 PMCID: PMC8258792 DOI: 10.1016/j.csbj.2021.06.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 12/12/2022] Open
Abstract
Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.
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Key Words
- CV, collective variable
- Docking
- Drug discovery
- In silico
- LIE, Linear Interaction Energy
- MD, Molecular Dynamic
- MDR, multi-drug resistant
- MMPB(GB)SA, Molecular Mechanics with Poisson Boltzmann (or generalised Born) and Surface Area solvation
- Machine learning
- Mt, Mycobacterium tuberculosis
- Mycobacterium tuberculosis
- PTC, peptidyl transferase centre
- RMSD, root-mean square-deviation
- Tuberculosis, TB
- cMD, Classical Molecular Dynamic
- cryo-EM, cryogenic electron microscopy
- ns, nanosecond
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Affiliation(s)
- Alexander D H Kingdon
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Luke J Alderwick
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
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