1
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Li H, Guo W, Guo Y. Impart of Heterogeneous Charge Polarization and Distribution on Friction at Water-Graphene Interfaces: a Density-Functional-Theory based Machine Learning Study. J Phys Chem Lett 2024; 15:6585-6591. [PMID: 38885449 DOI: 10.1021/acs.jpclett.4c01274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Accurately characterizing friction behaviors at water-solid interfaces remains a challenge because of the dynamic nature of water molecules and temporal variations in solid surface charges. By using a density-functional-theory (DFT) based machine learning (ML) technique and long-time ML-parametrized molecular dynamics simulations, we have systematically investigated water-induced charge polarization and redistribution on graphene, as well as its impact on friction at water-graphene interfaces. Heterogeneous charge polarization and distribution are observed for water-covered graphene accompanied by the formation of electric double layers (EDLs). The introduction of defects into graphene significantly enhances the heterogeneity in charge polarization and distribution. Compared to pristine graphene, defected graphene exhibits reduced friction at water-graphene interfaces due to stronger charge heterogeneity, resulting in lower surface charge density and the inverse relationship between slip length and surface charge density for EDLs. Our results highlight the pivotal roles of defects and charge heterogeneity in reducing friction at water-graphene interfaces.
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Affiliation(s)
- Hao Li
- State Key Laboratory of Mechanics and Control for Aerospace Structures, MOE Key Laboratory for Intelligent Nano Materials and Devices, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Wanlin Guo
- State Key Laboratory of Mechanics and Control for Aerospace Structures, MOE Key Laboratory for Intelligent Nano Materials and Devices, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Yufeng Guo
- State Key Laboratory of Mechanics and Control for Aerospace Structures, MOE Key Laboratory for Intelligent Nano Materials and Devices, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
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2
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Arab F, Nazari F, Illas F. Artificial Neural Network-Derived Unified Six-Dimensional Potential Energy Surface for Tetra Atomic Isomers of the Biogenic [H, C, N, O] System. J Chem Theory Comput 2023; 19:1186-1196. [PMID: 36735891 PMCID: PMC9979606 DOI: 10.1021/acs.jctc.2c00915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Recognition of different structural patterns in different potential energy surface regions, such as in isomerizing quasilinear tetra atomic molecules, is important for understanding the details of underlying physics and chemistry. In this respect, using three variants of artificial neural networks (ANNs), we investigated the six-dimensional (6-D) singlet potential energy surfaces (PES) of tetra atomic isomers of the biogenic [H, C, N, O] system. At first, we constructed a separate ANN potential for each of the studied isomers. In the next step, a comparative assessment of the separate ANN models led to the setting up of a unified 6-D singlet PES equally and accurately describing all studied isomers. The constructed unified model yields relative energies comparable to those obtained either from the gold standard CCSD(T) method or from separate ANNs for each of the studied isomers. The accuracy of the unified singlet PES is on the order of 10-4 Hartrees (0.1 kcal/mol). The developed PES in this work captures the main features of nonlinear and quasilinear tetra atomic isomers of this biogenic system.
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Affiliation(s)
- Fatemeh Arab
- Department
of Chemistry, Institute for Advanced Studies
in Basic Sciences, Zanjan45137-66731, Iran
| | - Fariba Nazari
- Department
of Chemistry, Institute for Advanced Studies
in Basic Sciences, Zanjan45137-66731, Iran,Center
of Climate Change and Global Warming, Institute
for Advanced Studies in Basic Sciences, Zanjan45137-66731, Iran,
| | - Francesc Illas
- Departament
de Ciència de Materials i Química Física &
Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, C/Martí i Franquès 1, 08028Barcelona, Spain,
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3
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Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J. Deep Learning in Diverse Intelligent Sensor Based Systems. SENSORS (BASEL, SWITZERLAND) 2022; 23:62. [PMID: 36616657 PMCID: PMC9823653 DOI: 10.3390/s23010062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/06/2022] [Accepted: 12/14/2022] [Indexed: 05/27/2023]
Abstract
Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.
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Affiliation(s)
- Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Min Wang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Xuefei Yin
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Jue Zhang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jiankun Hu
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
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4
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Gong S, Wang S, Xie T, Chae WH, Liu R, Shao-Horn Y, Grossman JC. Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning. JACS AU 2022; 2:1964-1977. [PMID: 36186569 PMCID: PMC9516701 DOI: 10.1021/jacsau.2c00235] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.
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Affiliation(s)
- Sheng Gong
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Shuo Wang
- Department
of Materials Science and Engineering, University
of Maryland, College
Park, Maryland 20742, United States
| | - Tian Xie
- Computer
Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Woo Hyun Chae
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Runze Liu
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Yang Shao-Horn
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jeffrey C. Grossman
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
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5
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Affiliation(s)
- Josiah Roberts
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, USA
| | - Eva Zurek
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, USA
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6
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Hughes DS, Bingham AL, Hursthouse MB, Threlfall TL, Bond AD. The extensive solid-form landscape of sulfathiazole: hydrogen-bond topology and node shape. CrystEngComm 2022. [DOI: 10.1039/d2ce00964a] [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
Patterns of hydrogen bonds are described in a set of 101 crystal structures containing sulfathiazole. Topological analysis of the hydrogen-bond nets is augmented by comparison of the shapes of the nodes extracted from each net.
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Affiliation(s)
- David S. Hughes
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Ann L. Bingham
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
- CHEP, Faculty of Social Sciences, University of Southampton, SO17 1BJ, UK
| | - Michael B. Hursthouse
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Terry L. Threlfall
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Andrew D. Bond
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
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7
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Gong S, Wang S, Zhu T, Chen X, Yang Z, Buehler MJ, Shao-Horn Y, Grossman JC. Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning. JACS AU 2021; 1:1904-1914. [PMID: 34841409 PMCID: PMC8611661 DOI: 10.1021/jacsau.1c00260] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Indexed: 06/13/2023]
Abstract
Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li+ and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.
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Affiliation(s)
- Sheng Gong
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Shuo Wang
- Department
of Materials Science and Engineering, University
of Maryland, College
Park, Maryland 20742, United States
| | - Taishan Zhu
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xi Chen
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Zhenze Yang
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Markus J. Buehler
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge Massachusetts 02139, United States
| | - Yang Shao-Horn
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, Cambridge Massachusetts 02139, United States
| | - Jeffrey C. Grossman
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
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8
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An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon. J Electroanal Chem (Lausanne) 2021. [DOI: 10.1016/j.jelechem.2021.115684] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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9
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Huang L, Ling C. Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties. J Chem Inf Model 2021; 61:4200-4209. [PMID: 34435765 DOI: 10.1021/acs.jcim.1c00434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning is emerging as a new paradigm to rationalize chemical properties for deepening our understanding of chemistry and providing instructive clues on better materials performance. While the complex architecture of machine learning contributes to unprecedented capability in this task, it prevents easy interpretation, leading to extensive criticisms on the lack of physical foundations for the black-box like models. Here, we demonstrate a transfer learning strategy that leverages fundamental principles of chemistry to offer adequate physical insights for the interpretation. Through interpreting the models for the formation energies of inorganic compounds, the proposed strategy revealed the deficiency of deep neural network in handling interelemental patterns and proved the more proper abstraction of recurrent neural network with attention mechanism, which led to predicting the elegant form of periodic table with high precision. The success demonstrates a new solution toward models with full transparency in materials informatics.
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Affiliation(s)
- Liyuan Huang
- Toyota Research Institute of North America, 1555 Woodridge Avenue, Ann Arbor, Michigan United States, 48105
| | - Chen Ling
- Toyota Research Institute of North America, 1555 Woodridge Avenue, Ann Arbor, Michigan United States, 48105
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10
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Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian Process Regression for Materials and Molecules. Chem Rev 2021; 121:10073-10141. [PMID: 34398616 PMCID: PMC8391963 DOI: 10.1021/acs.chemrev.1c00022] [Citation(s) in RCA: 245] [Impact Index Per Article: 81.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Indexed: 12/18/2022]
Abstract
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.
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Affiliation(s)
- Volker L. Deringer
- Department
of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom
| | - Albert P. Bartók
- Department
of Physics and Warwick Centre for Predictive Modelling, School of
Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Noam Bernstein
- Center
for Computational Materials Science, U.S.
Naval Research Laboratory, Washington D.C. 20375, United States
| | - David M. Wilkins
- Atomistic
Simulation Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
| | - Michele Ceriotti
- Laboratory
of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale
de Lausanne, Lausanne, Switzerland
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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11
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Yoon J, Ulissi ZW. Differentiable Optimization for the Prediction of Ground State Structures (DOGSS). PHYSICAL REVIEW LETTERS 2020; 125:173001. [PMID: 33156640 DOI: 10.1103/physrevlett.125.173001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 09/15/2020] [Indexed: 06/11/2023]
Abstract
Ground state or relaxed inorganic structures are the starting point for most computational materials science or surface science analyses. Many of these structure relaxations represent systematic changes to the structure, but there are currently no general methods to improve the initial structure guess based on past calculations. Here we present a method to directly predict the ground state configuration using differentiable optimization and graph neural networks to learn the properties of a simple harmonic force field that approximates the ground state structure and properties. We demonstrate this flexible open source tool for improving the initial configurations for large datasets of inorganic multicomponent surface relaxations across 32 elements and the relaxation of adsorbates (H and CO) on these surfaces. Using these improved initial configurations reduces the expensive adsorbate-covered surface relaxations by approximately 50% and is complementary to other approaches to accelerate the relaxation process.
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Affiliation(s)
- Junwoong Yoon
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Zachary W Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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12
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Jablonka K, Ongari D, Moosavi SM, Smit B. Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. Chem Rev 2020; 120:8066-8129. [PMID: 32520531 PMCID: PMC7453404 DOI: 10.1021/acs.chemrev.0c00004] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Indexed: 12/16/2022]
Abstract
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
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Affiliation(s)
- Kevin
Maik Jablonka
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Daniele Ongari
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Seyed Mohamad Moosavi
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Berend Smit
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
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13
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Shao Y, Hellström M, Mitev PD, Knijff L, Zhang C. PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials. J Chem Inf Model 2020; 60:1184-1193. [DOI: 10.1021/acs.jcim.9b00994] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Yunqi Shao
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
| | - Matti Hellström
- Software for Chemistry and Materials B.V., De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
| | - Pavlin D. Mitev
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
| | - Lisanne Knijff
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
| | - Chao Zhang
- Department of Chemistry-Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, P.O. Box 538, 75121 Uppsala, Sweden
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14
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Zimmermann NER, Jain A. Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity. RSC Adv 2020; 10:6063-6081. [PMID: 35497431 PMCID: PMC9049235 DOI: 10.1039/c9ra07755c] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 01/20/2020] [Indexed: 12/12/2022] Open
Abstract
Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure.
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Affiliation(s)
| | - Anubhav Jain
- Energy Technology Area
- Lawrence Berkeley National Laboratory
- Berkeley
- USA
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15
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Janet JP, Duan C, Yang T, Nandy A, Kulik HJ. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chem Sci 2019; 10:7913-7922. [PMID: 31588334 PMCID: PMC6764470 DOI: 10.1039/c9sc02298h] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 07/11/2019] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model's domain of applicability. Established uncertainty metrics for neural network models are either costly to obtain (e.g., ensemble models) or rely on feature engineering (e.g., feature space distances), and each has limitations in estimating prediction errors for chemical space exploration. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry. The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning.
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Affiliation(s)
- Jon Paul Janet
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
| | - Chenru Duan
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
- Department of Chemistry , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA
| | - Tzuhsiung Yang
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
| | - Aditya Nandy
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
- Department of Chemistry , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA
| | - Heather J Kulik
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , MA 02139 , USA . ; Tel: +1-617-253-4584
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16
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Kulik HJ. Making machine learning a useful tool in the accelerated discovery of transition metal complexes. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1439] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Heather J. Kulik
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
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17
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Xie T, France-Lanord A, Wang Y, Shao-Horn Y, Grossman JC. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials. Nat Commun 2019; 10:2667. [PMID: 31209223 PMCID: PMC6573035 DOI: 10.1038/s41467-019-10663-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 05/17/2019] [Indexed: 01/08/2023] Open
Abstract
Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems. Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations.
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Affiliation(s)
- Tian Xie
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Arthur France-Lanord
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yanming Wang
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yang Shao-Horn
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jeffrey C Grossman
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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18
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Abstract
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and follows this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry.
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Affiliation(s)
- Adam C Mater
- ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry , Australian National University , Canberra , Australian Capital Territory 2601 , Australia
| | - Michelle L Coote
- ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry , Australian National University , Canberra , Australian Capital Territory 2601 , Australia
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