1
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Manzhos S, Chen QG, Lee WY, Heejoo Y, Ihara M, Chueh CC. Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors. J Phys Chem Lett 2024; 15:6974-6985. [PMID: 38941557 PMCID: PMC11247485 DOI: 10.1021/acs.jpclett.4c01413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Synaptic transistors have been proposed to implement neuron activation functions of neural networks (NNs). While promising to enable compact, fast, inexpensive, and energy-efficient dedicated NN circuits, they also have limitations compared to digital NNs (realized as codes for digital processors), including shape choices of the activation function using particular types of transistor implementation, and instabilities due to noise and other factors present in analog circuits. We present a computational study of the effects of these factors on NN performance and find that, while accuracy competitive with traditional NNs can be realized for many applications, there is high sensitivity to the instability in the shape of the activation function, suggesting that, when highly accurate NNs are required, high-precision circuitry should be developed beyond what has been reported for synaptic transistors to date.
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Affiliation(s)
- Sergei Manzhos
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Qun Gao Chen
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan
| | - Wen-Ya Lee
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan
| | - Yoon Heejoo
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Manabu Ihara
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Chu-Chen Chueh
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
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2
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Xiong G, Ji H, Chen Y, Liu B, Wang Y, Long P, Zeng J, Tao J, Deng C. Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1153. [PMID: 38998758 PMCID: PMC11242931 DOI: 10.3390/nano14131153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/14/2024]
Abstract
In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.
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Affiliation(s)
- Gaoyang Xiong
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Haining Ji
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Yongxing Chen
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Bin Liu
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Yi Wang
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Peng Long
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Jinfang Zeng
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Jundong Tao
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
| | - Cong Deng
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
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3
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Gharakhanyan V, Wirth LJ, Garrido Torres JA, Eisenberg E, Wang T, Trinkle DR, Chatterjee S, Urban A. Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning. J Chem Phys 2024; 160:204112. [PMID: 38804486 DOI: 10.1063/5.0207033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.
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Affiliation(s)
- Vahe Gharakhanyan
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
- Columbia Electrochemical Energy Center, Columbia University, New York, New York 10027, USA
| | - Luke J Wirth
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Jose A Garrido Torres
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
| | - Ethan Eisenberg
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
| | - Ting Wang
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
| | - Dallas R Trinkle
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
| | | | - Alexander Urban
- Columbia Electrochemical Energy Center, Columbia University, New York, New York 10027, USA
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
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4
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Mukherjee M, Sahu H, Losego MD, Gutekunst WR, Ramprasad R. Informatics-Driven Design of Superhard B-C-O Compounds. ACS APPLIED MATERIALS & INTERFACES 2024; 16:10372-10379. [PMID: 38367252 PMCID: PMC10910474 DOI: 10.1021/acsami.3c18105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/19/2024]
Abstract
Materials containing B, C, and O, due to the advantages of forming strong covalent bonds, may lead to materials that are superhard, i.e., those with a Vicker's hardness larger than 40 GPa. However, the exploration of this vast chemical, compositional, and configurational space is nontrivial. Here, we leverage a combination of machine learning (ML) and first-principles calculations to enable and accelerate such a targeted search. The ML models first screen for potentially superhard B-C-O compositions from a large hypothetical B-C-O candidate space. Atomic-level structure search using density functional theory (DFT) within those identified compositions, followed by further detailed analyses, unravels on four potentially superhard B-C-O phases exhibiting thermodynamic, mechanical, and dynamic stability.
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Affiliation(s)
- Madhubanti Mukherjee
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Harikrishna Sahu
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Mark D. Losego
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Will R. Gutekunst
- School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - Rampi Ramprasad
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
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5
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Jung SG, Jung G, Cole JM. Gradient boosted and statistical feature selection workflow for materials property predictions. J Chem Phys 2023; 159:194106. [PMID: 37971034 DOI: 10.1063/5.0171540] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/13/2023] [Indexed: 11/19/2023] Open
Abstract
With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning models, in particular, is increasingly gaining momentum to accelerate the discovery of new materials. However, the black-box treatment of machine-learning methods suffers from a lack of model interpretability, as feature relevance and interactions can be overlooked or disregarded. In addition, naive approaches to model training often lead to irrelevant features being used which necessitates the need for various regularization techniques to achieve model generalization; this incurs a high computational cost. We present a feature-selection workflow that overcomes this problem by leveraging a gradient boosting framework and statistical feature analyses to identify a subset of features, in a recursive manner, which maximizes their relevance to the target variable or classes. We subsequently obtain minimal feature redundancy through multicollinearity reduction by performing feature correlation and hierarchical cluster analyses. The features are further refined using a wrapper method, which follows a greedy search approach by evaluating all possible feature combinations against the evaluation criterion. A case study on elastic material-property prediction and a case study on the classification of materials by their metallicity are used to illustrate the use of our proposed workflow; although it is highly general, as demonstrated through our wider subsequent prediction of various material properties. Our Bayesian-optimized machine-learning models generated results, without the use of regularization techniques, which are comparable to the state-of-the-art that are reported in the scientific literature.
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Affiliation(s)
- Son Gyo Jung
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, United Kingdom
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, United Kingdom
| | - Guwon Jung
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, United Kingdom
- Scientific Computing Department, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, United Kingdom
| | - Jacqueline M Cole
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, United Kingdom
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, United Kingdom
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6
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Ahmed A, Song W, Zhang Y, Haque MA, Liu X. Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis. MATERIALS (BASEL, SWITZERLAND) 2023; 16:4366. [PMID: 37374550 DOI: 10.3390/ma16124366] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 05/29/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023]
Abstract
Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model's predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R2 = 0.96 for training and R2 = 0.91 for testing phases) for predicting flexural strength with low error. In terms of compressive strength prediction, the employed BO-RF model performed very well, with R2 = 0.96 for train and R2 = 0.88 testing stages with minor errors. Moreover, the SHAP algorithm, permutation importance and leave-one-out importance score were used for sensitivity analysis to explain the prediction process and interpret the governing input variable parameters of the proposed HML models. Finally, the outcomes of this study might be applied to guide the future mix design of SCM specimens.
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Affiliation(s)
- Asif Ahmed
- Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
| | - Wei Song
- Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Yumeng Zhang
- Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
| | - M Aminul Haque
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Xian Liu
- Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
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7
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Mishra AK, Rajput S, Karamta M, Mukhopadhyay I. Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes. ACS OMEGA 2023; 8:16419-16427. [PMID: 37179618 PMCID: PMC10173313 DOI: 10.1021/acsomega.3c01400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Unlike conventional liquid electrolytes, solid-state electrolytes (SSEs) have gained increased attention in the domain of all-solid-state lithium-ion batteries (ASSBs) due to their safety features, higher energy/power density, better electrochemical stability, and a broader electrochemical window. SSEs, however, face several difficulties, such as poorer ionic conductivity, complicated interfaces, and unstable physical characteristics. Vast research is still needed to find compatible and appropriate SSEs with improved properties for ASSBs. Traditional trial-and-error procedures to find novel and sophisticated SSEs require vast resources and time. Machine learning (ML), which has emerged as an effective and trustworthy tool for screening new functional materials, was recently used to forecast new SSEs for ASSBs. In this study, we developed an ML-based architecture to predict ionic conductivity by utilizing the characteristics of activation energy, operating temperature, lattice parameters, and unit cell volume of various SSEs. Additionally, the feature set can identify distinct patterns in the data set that can be verified using a correlation map. Because they are more reliable, the ensemble-based predictor models can more precisely forecast ionic conductivity. The prediction can be strengthened even further, and the overfitting issue can be resolved by stacking numerous ensemble models. The data set was split into 70:30 ratios to train and test with eight predictor models. The maximum mean-squared error and mean absolute error in training and testing for the random forest regressor (RFR) model were obtained as 0.001 and 0.003, respectively.
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Affiliation(s)
- Atul Kumar Mishra
- Solar
Research and Development Center, Department of Solar Energy, Pandit Deendayal Energy University, Raisan, Gandhinagar 382007, Gujarat, India
| | - Snehal Rajput
- Department
of Computer Science Engineering, School of Technology, Pandit Deendayal Energy University, Raisan, Gandhinagar 382007, Gujarat, India
| | - Meera Karamta
- Department
of Electrical Engineering, School of Technology, Pandit Deendayal Energy University,
Raisan, Gandhinagar 382007, Gujarat, India
| | - Indrajit Mukhopadhyay
- Solar
Research and Development Center, Department of Solar Energy, Pandit Deendayal Energy University, Raisan, Gandhinagar 382007, Gujarat, India
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8
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Need for UAI–Anatomy of the Paradigm of Usable Artificial Intelligence for Domain-Specific AI Applicability. MULTIMODAL TECHNOLOGIES AND INTERACTION 2023. [DOI: 10.3390/mti7030027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Data-driven methods based on artificial intelligence (AI) are powerful yet flexible tools for gathering knowledge and automating complex tasks in many areas of science and practice. Despite the rapid development of the field, the existing potential of AI methods to solve recent industrial, corporate and social challenges has not yet been fully exploited. Research shows the insufficient practicality of AI in domain-specific contexts as one of the main application hurdles. Focusing on industrial demands, this publication introduces a new paradigm in terms of applicability of AI methods, called Usable AI (UAI). Aspects of easily accessible, domain-specific AI methods are derived, which address essential user-oriented AI services within the UAI paradigm: usability, suitability, integrability and interoperability. The relevance of UAI is clarified by describing challenges, hurdles and peculiarities of AI applications in the production area, whereby the following user roles have been abstracted: developers of cyber–physical production systems (CPPS), developers of processes and operators of processes. The analysis shows that target artifacts, motivation, knowledge horizon and challenges differ for the user roles. Therefore, UAI shall enable domain- and user-role-specific adaptation of affordances accompanied by adaptive support of vertical and horizontal integration across the domains and user roles.
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9
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Li Y, Zhang J, Zhang K, Zhao M, Hu K, Lin X. Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit. ACS APPLIED MATERIALS & INTERFACES 2022; 14:55517-55527. [PMID: 36472480 DOI: 10.1021/acsami.2c15396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R2) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The "ME Lattice Parameter" was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials.
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Affiliation(s)
- Yi Li
- School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen518055, P. R. China
- Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen518055, P.R. China
| | - Jingzi Zhang
- School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen518055, P. R. China
- Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen518055, P.R. China
| | - Ke Zhang
- School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen518055, P. R. China
- Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen518055, P.R. China
| | - Mengkun Zhao
- School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen518055, P. R. China
- Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen518055, P.R. China
| | - Kailong Hu
- School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen518055, P. R. China
- State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin150001, P. R. China
| | - Xi Lin
- School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen518055, P. R. China
- State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin150001, P. R. China
- Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen518055, P.R. China
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10
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Kiyabu S, Girard P, Siegel DJ. Discovery of Salt Hydrates for Thermal Energy Storage. J Am Chem Soc 2022; 144:21617-21627. [DOI: 10.1021/jacs.2c08993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Steven Kiyabu
- Mechanical Engineering DepartmentUniversity of Michigan, Ann Arbor, Michigan48109, United States
| | - Patrick Girard
- Mechanical Engineering DepartmentUniversity of Michigan, Ann Arbor, Michigan48109, United States
| | - Donald J. Siegel
- Mechanical Engineering DepartmentUniversity of Michigan, Ann Arbor, Michigan48109, United States
- Walker Department of Mechanical Engineering, Texas Materials Institute, and Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas78712-1591, United States
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11
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Xu H, Kim SY, Chen D, Monchoux J, Voisin T, Sun C, Li J. Materials Genomics Search for Possible Helium-Absorbing Nano-Phases in Fusion Structural Materials. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203555. [PMID: 36180389 PMCID: PMC9661827 DOI: 10.1002/advs.202203555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/07/2022] [Indexed: 06/16/2023]
Abstract
Civilian fusion demands structural materials that can withstand the harsh environments imposed inside fusion plasma reactors. The structural materials often transmute under 14.1 MeV fast neutrons, producing helium (He), which embrittles the grain boundary (GB) network. Here, it is shown that neutron-friendly and mechanically strong nano-phases with atomic-scale free volume can have low He-embedding energy E emb ${\mathcal{E}}_{\mathrm{emb}}$ and >10 at.% He-absorbing capacity, and can be especially advantageous for soaking up He on top of resisting radiation damage and creep, provided they have thermodynamic compatibility with the matrix phase, satisfactory equilibrium wetting angle, as well as a high enough melting point. The preliminary experimental demonstration proves that E emb ${\mathcal{E}}_{\mathrm{emb}}$ is a good ab initio predictor of He shielding potency in nano-heterophase materials, and thus, E emb ${\mathcal{E}}_{\mathrm{emb}}$ is used as a key feature for computational screening. In this context, a list of viable compounds expected to be good He-absorbing nano-phases is presented, taking into account E emb ${\mathcal{E}}_{\mathrm{emb}}$ , the neutron absorption and activation cross-sections, the elastic moduli, melting temperature, the thermodynamic compatibility, and the equilbrium wetting angle of the nano-phases with the Fe matrix as an example.
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Affiliation(s)
- Haowei Xu
- Department of Nuclear Science and EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - So Yeon Kim
- Department of Materials Science and EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Di Chen
- Department of Physics and Texas Center for SuperconductivityUniversity of HoustonHoustonTX77204USA
| | - Jean‐Phillippe Monchoux
- Centre for Materials Elaboration and Structural StudiesUniversity of ToulouseFrench National Centre for Scientific ResearchToulouse31055France
| | - Thomas Voisin
- Materials Science DivisionLawrence Livermore National LaboratoryLivermoreCA94550USA
| | - Cheng Sun
- Characterization and Advanced PIE DivisionIdaho National LaboratoryIdaho FallsID83415USA
| | - Ju Li
- Department of Nuclear Science and EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
- Department of Materials Science and EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
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12
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Grosso BF, Spaldin NA, Tehrani AM. Physics-Guided Descriptors for Prediction of Structural Polymorphs. J Phys Chem Lett 2022; 13:7342-7349. [PMID: 35921428 PMCID: PMC9376952 DOI: 10.1021/acs.jpclett.2c01876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
We develop a method combining machine learning (ML) and density functional theory (DFT) to predict low-energy polymorphs by introducing physics-guided descriptors based on structural distortion modes. We systematically generate crystal structures utilizing the distortion modes and compute their energies with single-point DFT calculations. We then train a ML model to identify low-energy configurations on the material's high-dimensional potential energy surface. Here, we use BiFeO3 as a case study and explore its phase space by tuning the amplitudes of linear combinations of a finite set of distinct distortion modes. Our procedure is validated by rediscovering several known metastable phases of BiFeO3 with complex crystal structures, and its efficiency is proved by identifying 21 new low-energy polymorphs. This approach proposes a new avenue toward accelerating the prediction of low-energy polymorphs in solid-state materials.
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13
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Jha D, Gupta V, Liao WK, Choudhary A, Agrawal A. Moving closer to experimental level materials property prediction using AI. Sci Rep 2022; 12:11953. [PMID: 35831344 PMCID: PMC9279333 DOI: 10.1038/s41598-022-15816-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of \documentclass[12pt]{minimal}
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\begin{document}$$>0.076$$\end{document}>0.076 eV/atom) for the first time.
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Affiliation(s)
- Dipendra Jha
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Vishu Gupta
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Wei-Keng Liao
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Alok Choudhary
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Ankit Agrawal
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA.
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14
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Xie T, France-Lanord A, Wang Y, Lopez J, Stolberg MA, Hill M, Leverick GM, Gomez-Bombarelli R, Johnson JA, Shao-Horn Y, Grossman JC. Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties. Nat Commun 2022; 13:3415. [PMID: 35701416 PMCID: PMC9197847 DOI: 10.1038/s41467-022-30994-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 03/02/2022] [Indexed: 12/03/2022] Open
Abstract
Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials. Screening polymer electrolytes for batteries is extremely expensive due to the complex structures and slow dynamics. Here the authors develop a machine learning scheme to accelerate the screening and explore a space much larger than past studies.
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Affiliation(s)
- Tian Xie
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. .,Computer Science and Artificial Intelligence Laboratory, 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.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yanming Wang
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jeffrey Lopez
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael A Stolberg
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Megan Hill
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Graham Michael Leverick
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Rafael Gomez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jeremiah A Johnson
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yang Shao-Horn
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,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. .,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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15
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Lin X, Jiang H, Wang L, Ren Y, Ma W, Zhan S. 3D-structure-attention graph neural network for crystals and materials. Mol Phys 2022. [DOI: 10.1080/00268976.2022.2077258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Xuanjie Lin
- Key Laboratory of Big Data Knowledge Engineering, Ministry of Education, Hefei, China
- School of Computer and Information Engineering, Hefei University of Technology, Hefei, People’s Republic of China
| | - Hantong Jiang
- Key Laboratory of Big Data Knowledge Engineering, Ministry of Education, Hefei, China
- School of Computer and Information Engineering, Hefei University of Technology, Hefei, People’s Republic of China
| | - Liquan Wang
- Key Laboratory of Big Data Knowledge Engineering, Ministry of Education, Hefei, China
- School of Computer and Information Engineering, Hefei University of Technology, Hefei, People’s Republic of China
| | - Yongsheng Ren
- National Engineering Laboratory of Vacuum Metallurgy, Kunming, People’s Republic of China
- Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, China
| | - Wenhui Ma
- Pùer University, Puer, People’s Republic of China
| | - Shu Zhan
- Key Laboratory of Big Data Knowledge Engineering, Ministry of Education, Hefei, China
- School of Computer and Information Engineering, Hefei University of Technology, Hefei, People’s Republic of China
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16
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Allen AEA, Tkatchenko A. Machine learning of material properties: Predictive and interpretable multilinear models. SCIENCE ADVANCES 2022; 8:eabm7185. [PMID: 35522750 PMCID: PMC9075804 DOI: 10.1126/sciadv.abm7185] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques can be used with black box solutions, or alternatively, models can be created that are directly interpretable. We revisit material datasets used in several works and demonstrate that simple linear combinations of nonlinear basis functions can be created, which have comparable accuracy to the kernel and neural network approaches originally used. Linear solutions can accurately predict the bandgap and formation energy of transparent conducting oxides, the spin states for transition metal complexes, and the formation energy for elpasolite structures. We demonstrate how linear solutions can provide interpretable predictive models and highlight the new insights that can be found when a model can be directly understood from its coefficients and functional form. Furthermore, we discuss how to recognize when intrinsically interpretable solutions may be the best route to interpretability.
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17
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A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys. Sci Data 2022; 9:188. [PMID: 35474075 PMCID: PMC9042935 DOI: 10.1038/s41597-022-01283-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/28/2022] [Indexed: 11/30/2022] Open
Abstract
The present work depicts a compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys and their respective microstructural features. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation. Each entry is associated with a high-quality experimental work containing a complete description of the processing route and testing setup. Furthermore, we incorporated flags to the dataset indicating (a) the use of high-resolution techniques for microstructural analysis and (b) the observation of non-linear elastic responses during mechanical testing. Oxygen content and average grain size are presented whenever available. The selected features can help material scientists to adjust the data to their needs concerning materials selection and discovery. Most alloys in the dataset were produced via an ingot metallurgy route, followed by solubilization and water quench (≈58%), which is considered a standard condition for β-Ti alloys. The database is hosted and maintained up to date in an open platform. For completeness, a few graphical representations of the dataset are included. Measurement(s) | elastic modulus • mechanical strength (yield) • deformation at rupture • Vickers hardness • oxygen content • grain size • mechanical strength (ultimate) | Technology Type(s) | longitudinal and transversal pulse-echo ultrasound, nanoidentation, mechanical testing • mechanical testing (tensile), mechanical testing (compression) • Vickers hardness testing • inert gas fusion chemical analysis • linear intercept method | Sample Characteristic - Organism | metallic alloys | Sample Characteristic - Environment | room temperature |
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18
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Twyman N, Walsh A, Buonassisi T. Environmental Stability of Crystals: A Greedy Screening. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2022; 34:2545-2552. [PMID: 35431438 PMCID: PMC9008530 DOI: 10.1021/acs.chemmater.1c02644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Discovering materials that are environmentally stable and also exhibit the necessary collection of properties required for a particular application is a perennial challenge in materials science. Herein, we present an algorithm to rapidly screen materials for their thermodynamic stability in a given environment, using a greedy approach. The performance was tested against the standard energy above the hull stability metric for inert conditions. Using data of 126 320 crystals, the greedy algorithm was shown to estimate the driving force for decomposition with a mean absolute error of 39.5 meV/atom, giving it sufficient resolution to identify stable materials. To demonstrate the utility outside of a vacuum, the in-oxygen stability of 39 654 materials was tested. The enthalpy of oxidation was found to be largely exothermic. Further analysis showed that 1438 of these materials fall into the range required for self-passivation based on the Pilling-Bedworth ratio.
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Affiliation(s)
- Nicholas
M. Twyman
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Photovaltaic
Research Laboratory, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Aron Walsh
- Department
of Materials, Imperial College London, London SW7 2AZ, United Kingdom
- Department
of Materials Science and Engineering, Yonsei
University, Seoul 03722, Korea
| | - Tonio Buonassisi
- Photovaltaic
Research Laboratory, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Singapore-MIT
Alliance for Research and Technology, Singapore 138602, Singapore
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19
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Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings. Nat Commun 2022; 13:949. [PMID: 35177607 PMCID: PMC8854636 DOI: 10.1038/s41467-022-28543-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/26/2022] [Indexed: 12/23/2022] Open
Abstract
Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec's ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.
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20
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Tavazza F, DeCost B, Choudhary K. Uncertainty Prediction for Machine Learning Models of Material Properties. ACS OMEGA 2021; 6:32431-32440. [PMID: 34901594 PMCID: PMC8655759 DOI: 10.1021/acsomega.1c03752] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 11/05/2021] [Indexed: 06/14/2023]
Abstract
Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are not as frequently available. In this work, we compare three different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the quantile loss function, machine learning the prediction intervals directly, and using Gaussian processes. We identify each approach's advantages and disadvantages and end up slightly favoring the modeling of the individual uncertainties directly, as it is the easiest to fit and, in most of the cases, minimizes over- and underestimation of the predicted errors. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through the JARVIS-tools package.
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21
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Dogan NO, Bozuyuk U, Erkoc P, Karacakol AC, Cingoz A, Seker-Polat F, Nazeer MA, Sitti M, Bagci-Onder T, Kizilel S. Parameters Influencing Gene Delivery Efficiency of PEGylated Chitosan Nanoparticles: Experimental and Modeling Approach. ADVANCED NANOBIOMED RESEARCH 2021. [DOI: 10.1002/anbr.202100033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Nihal Olcay Dogan
- Chemical and Biological Engineering Koc University Istanbul 34450 Turkey
- Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart 70569 Germany
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
| | - Ugur Bozuyuk
- Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart 70569 Germany
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
| | - Pelin Erkoc
- Institute of Pharmaceutical Biology Goethe University Frankfurt Frankfurt 60438 Germany
| | - Alp Can Karacakol
- Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart 70569 Germany
- Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Ahmet Cingoz
- School of Medicine Koc University Istanbul 34450 Turkey
| | | | | | - Metin Sitti
- Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart 70569 Germany
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
- School of Medicine Koc University Istanbul 34450 Turkey
| | | | - Seda Kizilel
- Chemical and Biological Engineering Koc University Istanbul 34450 Turkey
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22
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Wang Q, Dumond JJ, Teo J, Low HY. Superhydrophobic Polymer Topography Design Assisted by Machine Learning Algorithms. ACS APPLIED MATERIALS & INTERFACES 2021; 13:30155-30164. [PMID: 34128635 DOI: 10.1021/acsami.1c04473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Superhydrophobic surfaces have been largely achieved through various surface topographies. Both empirical and numerical simulations have been reported to help understand and design superhydrophobic surfaces. Many such successful surfaces have also been achieved using bioinspired and biomimetic designs. Despite this, identifying the right surface texture to meet the requirements of specific applications is not a straightforward task. Here, we report a hybrid approach that includes experimental methods, numerical simulations, and machine learning (ML) algorithms to create design maps for superhydrophobic polymer topographies. Two design objectives to investigate superhydrophobic properties were the maximum water contact angle (WCA) and Laplace pressure. The design parameters were the geometries of an isotropic pillar structure in micrometer and sub-micrometer length scales. The finite element method (FEM) was validated by the experimental data and employed to generate a labeled dataset for ML training. Artificial neural network (ANN) models were then trained on the labeled database for the topographic parameters (width W, height H, and pitch P) with the corresponding WCA and Laplace pressure. The ANN models yielded a series of nonlinear relationships between the topographic design parameters and the WCA and Laplace pressure and substantial differences between the micrometer and sub-micrometer length scales. Design maps that span the topography design parameters provide optimal design or tradeoff parameters. This research demonstrates the potential of ANN as a rapid design tool for surface topography exploration.
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Affiliation(s)
- Qiang Wang
- Digital Manufacturing and Design Centre (DManD), Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore
| | - Jarrett J Dumond
- NILT US Inc., 95 Brown Rd Ste 246, m/s 1024, Ithaca, New York 14850, United States
| | - Jarren Teo
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue, West Waterloo, Ontario N2L 3G1, Canada
| | - Hong Yee Low
- Digital Manufacturing and Design (DManD), Engineering Product Development Pillar, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
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23
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Rosenberger D, Smith JS, Garcia AE. Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison. J Phys Chem B 2021; 125:3598-3612. [DOI: 10.1021/acs.jpcb.0c10401] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- David Rosenberger
- Los Alamos National Laboratory, Theoretical Division, Chemistry and Physics of Materials Group, Los Alamos, 87545 New Mexico, United States
- Los Alamos National Laboratory, Theoretical Division, Center for Nonlinear Studies, Los Alamos, 87545 New Mexico, United States
| | - Justin S. Smith
- Los Alamos National Laboratory, Theoretical Division, Chemistry and Physics of Materials Group, Los Alamos, 87545 New Mexico, United States
| | - Angel E. Garcia
- Los Alamos National Laboratory, Theoretical Division, Center for Nonlinear Studies, Los Alamos, 87545 New Mexico, United States
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24
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Mehnaz, Yang LH, Da B, Ding ZJ. Ensemble machine learning methods: predicting electron stopping powers from a small experimental database. Phys Chem Chem Phys 2021; 23:6062-6074. [PMID: 33683251 DOI: 10.1039/d0cp06521h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Electron stopping power (SP) is of great importance in theoretical and applied research areas specifically for Monte Carlo simulation studies in many microanalysis and surface analysis techniques, radiation dosimetry, and the design of particle detectors. However, experimental data are available for a dozen elemental materials only. On the other hand, the Bethe analytical expression of the SP is applicable at high energies only whereas no generally accepted formula exists at lower energies. We employed ensemble machine learning (ML) methods with the available experimental database for the prediction of SPs of electrons with energies from 100 keV down to 1 eV, in elements over the entire periodic table. With a small training database for electron SPs, we applied various algorithms individually as well as their ensembles, which have the credibility to enhance the prediction accuracy in the case of a small training database. Based on the model's performance evaluation tests, we concluded that the stacked generalization is more accurate than the individual algorithms. Using this method, we were able to predict the electron SPs for 54 elements (in total) including 12 elements that were present in the training database as well as for 42 elements beyond the training database over a wide energy range (1 eV to 100 keV). Compared to other theoretical approaches, the ML predicted SPs show very good agreement with the available experimental data at all energies. Moreover, unlike other theoretical approaches, the ML model does not need dielectric function data and other physical parameters which involve complex calculations. Using our ML model, we have predicted SPs for a further 14 elements for which no theoretical SPs are available because of the lack of good dielectric function data.
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Affiliation(s)
- Mehnaz
- Hefei National Laboratory for Physical Sciences at Microscale and Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.
| | - L H Yang
- Hefei National Laboratory for Physical Sciences at Microscale and Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.
| | - B Da
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
| | - Z J Ding
- Hefei National Laboratory for Physical Sciences at Microscale and Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.
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25
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Salvador CAF, Zornio BF, Miranda CR. Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations. ACS APPLIED MATERIALS & INTERFACES 2020; 12:56850-56861. [PMID: 33296178 DOI: 10.1021/acsami.0c18506] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The discovery of low-modulus Ti alloys for biomedical applications is challenging due to a vast number of compositions and available solute contents. In this work, machine learning (ML) methods are employed for the prediction of the bulk modulus (K) and the shear modulus (G) of optimized ternary alloys. As a starting point, the elasticity data of more than 1800 compounds from the Materials Project fed linear models, random forest regressors, and artificial neural networks (NN), with the aims of training predictive models for K and G based on compositional features. The models were then used to predict the resultant Young modulus (E) for all possible compositions in the Ti-Nb-Zr system, with variations in the composition of 2 at. %. Random forest (RF) predictions of E deviate from the NN predictions by less than 4 GPa, which is within the expected variance from the ML training phase. RF regressors seem to generate the most reliable models, given the selected target variables and descriptors. Optimal compositions identified by the ML models were later investigated with the aid of special quasi-random structures (SQSs) and density functional theory (DFT). According to a combined analysis, alloys with 22 Zr (at. %) are promising structural materials to the biomedical field, given their low elastic modulus and elevated beta-phase stability. In alloys with Nb content higher than 14.8 (at. %), the beta phase has lower energy than omega, which may be enough to avoid the formation of omega, a high-modulus phase, during manufacturing.
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Affiliation(s)
- Camilo A F Salvador
- Instituto de Física, DFMT, Universidade de São Paulo, CP 66318, 05315-970 São Paulo, SP, Brazil
| | - Bruno F Zornio
- Instituto de Física, DFMT, Universidade de São Paulo, CP 66318, 05315-970 São Paulo, SP, Brazil
| | - Caetano R Miranda
- Instituto de Física, DFMT, Universidade de São Paulo, CP 66318, 05315-970 São Paulo, SP, Brazil
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26
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Sizochenko N, Hofmann M. Predictive Modeling of Critical Temperatures in Superconducting Materials. Molecules 2020; 26:molecules26010008. [PMID: 33375023 PMCID: PMC7792800 DOI: 10.3390/molecules26010008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 12/19/2020] [Accepted: 12/21/2020] [Indexed: 01/03/2023] Open
Abstract
In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests).
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Affiliation(s)
- Natalia Sizochenko
- Department of Informatics, Blanchardstown Campus, Technological University Dublin, 15 YV78 Dublin, Ireland;
- Department of Informatics, Postdoctoral Institute for Computational Studies, Enfield, NH 03748, USA
- Correspondence:
| | - Markus Hofmann
- Department of Informatics, Blanchardstown Campus, Technological University Dublin, 15 YV78 Dublin, Ireland;
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27
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Chong S, Lee S, Kim B, Kim J. Applications of machine learning in metal-organic frameworks. Coord Chem Rev 2020. [DOI: 10.1016/j.ccr.2020.213487] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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28
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Devereux C, Smith JS, Huddleston KK, Barros K, Zubatyuk R, Isayev O, Roitberg AE. Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens. J Chem Theory Comput 2020; 16:4192-4202. [PMID: 32543858 DOI: 10.1021/acs.jctc.0c00121] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here, we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, and S) make up ∼90% of drug-like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and nonbonded interactions. ANI-2x is shown to accurately predict molecular energies compared to density functional theory with a ∼106 factor speedup and a negligible slowdown compared to ANI-1x and shows subchemical accuracy across most of the COMP6 benchmark. The resulting model is a valuable tool for drug development which can potentially replace both quantum calculations and classical force fields for a myriad of applications.
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Affiliation(s)
- Christian Devereux
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Justin S Smith
- Center for Non-Linear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Kate K Huddleston
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Roman Zubatyuk
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Adrian E Roitberg
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
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29
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 319] [Impact Index Per Article: 79.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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Chen X, Chen D, Weng M, Jiang Y, Wei GW, Pan F. Topology-Based Machine Learning Strategy for Cluster Structure Prediction. J Phys Chem Lett 2020; 11:4392-4401. [PMID: 32320253 PMCID: PMC7351018 DOI: 10.1021/acs.jpclett.0c00974] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In cluster physics, the determination of the ground-state structure of medium-sized and large-sized clusters is a challenge due to the number of local minimal values on the potential energy surface growing exponentially with cluster size. Although machine learning approaches have had much success in materials sciences, their applications in clusters are often hindered by the geometric complexity clusters. Persistent homology provides a new topological strategy to simplify geometric complexity while retaining important chemical and physical information without having to "downgrade" the original data. We further propose persistent pairwise independence (PPI) to enhance the predictive power of persistent homology. We construct topology-based machine learning models to reveal hidden structure-energy relationships in lithium (Li) clusters. We integrate the topology-based machine learning models, a particle swarm optimization algorithm, and density functional theory calculations to accelerate the search of the globally stable structure of clusters.
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Affiliation(s)
- Xin Chen
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Dong Chen
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Mouyi Weng
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Yi Jiang
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Feng Pan
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
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31
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Ghilan A, Chiriac AP, Nita LE, Rusu AG, Neamtu I, Chiriac VM. Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges. JOURNAL OF POLYMERS AND THE ENVIRONMENT 2020; 28:1345-1367. [PMID: 32435165 PMCID: PMC7224028 DOI: 10.1007/s10924-020-01722-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Affiliation(s)
- Alina Ghilan
- “Petru Poni” Institute of Macromolecular Chemistry, Laboratory of Inorganic Polymers, 41-A Grigore Ghica Voda Alley, Iasi, 700487 Romania
| | - Aurica P. Chiriac
- “Petru Poni” Institute of Macromolecular Chemistry, Laboratory of Inorganic Polymers, 41-A Grigore Ghica Voda Alley, Iasi, 700487 Romania
| | - Loredana E. Nita
- “Petru Poni” Institute of Macromolecular Chemistry, Laboratory of Inorganic Polymers, 41-A Grigore Ghica Voda Alley, Iasi, 700487 Romania
| | - Alina G. Rusu
- “Petru Poni” Institute of Macromolecular Chemistry, Laboratory of Inorganic Polymers, 41-A Grigore Ghica Voda Alley, Iasi, 700487 Romania
| | - Iordana Neamtu
- “Petru Poni” Institute of Macromolecular Chemistry, Laboratory of Inorganic Polymers, 41-A Grigore Ghica Voda Alley, Iasi, 700487 Romania
| | - Vlad Mihai Chiriac
- “Gh. Asachi” Technical University, Faculty of Electronics, Telecommunications and Information Technology, Bd. Carol I, 11A, Iasi, 700506 Romania
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32
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Li Z, Achenie LEK, Xin H. An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts. ACS Catal 2020. [DOI: 10.1021/acscatal.9b05248] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Zheng Li
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Luke E. K. Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Hongliang Xin
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
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33
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Training data augmentation: An empirical study using generative adversarial net-based approach with normalizing flow models for materials informatics. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105932] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Jha D, Choudhary K, Tavazza F, Liao WK, Choudhary A, Campbell C, Agrawal A. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat Commun 2019; 10:5316. [PMID: 31757948 PMCID: PMC6874674 DOI: 10.1038/s41467-019-13297-w] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 10/24/2019] [Indexed: 01/11/2023] Open
Abstract
The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of \documentclass[12pt]{minimal}
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\begin{document}$$1,963$$\end{document}1,963 observations, the proposed approach yields a mean absolute error (MAE) of \documentclass[12pt]{minimal}
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\begin{document}$$0.06$$\end{document}0.06 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself. Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions with high accuracy.
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Affiliation(s)
- Dipendra Jha
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Kamal Choudhary
- Thermodynamics and Kinetics Group, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Francesca Tavazza
- Thermodynamics and Kinetics Group, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Wei-Keng Liao
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Alok Choudhary
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Carelyn Campbell
- Thermodynamics and Kinetics Group, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Ankit Agrawal
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA.
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35
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Barnard AS, Motevalli B, Parker AJ, Fischer JM, Feigl CA, Opletal G. Nanoinformatics, and the big challenges for the science of small things. NANOSCALE 2019; 11:19190-19201. [PMID: 31397835 DOI: 10.1039/c9nr05912a] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The combination of computational chemistry and computational materials science with machine learning and artificial intelligence provides a powerful way of relating structural features of nanomaterials with functional properties. However, combining these fundamentally different scientific approaches is not as straightforward as it seems. Machine learning methods were developed for large data sets with small numbers of consistent features. Typically nanomaterials data sets are small, with high dimensionality and high variance in the feature space, and suffer from numerous destructive biases. None of the established data science or machine learning methods in widespread use today were devised with (nano)materials data sets in mind, but there are ways to overcome these challenges and use them reliably. In this review we will discuss domain-specific constraints on data-driven nanomaterials design, and explore the differences between nanomaterials simulation and nanoinformatics that can be leveraged for greater impact.
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Affiliation(s)
- A S Barnard
- CSIRO Data61, Docklands, Victoria, Australia.
| | - B Motevalli
- CSIRO Data61, Docklands, Victoria, Australia.
| | - A J Parker
- CSIRO Data61, Docklands, Victoria, Australia.
| | - J M Fischer
- CSIRO Data61, Docklands, Victoria, Australia.
| | - C A Feigl
- CSIRO Data61, Docklands, Victoria, Australia.
| | - G Opletal
- CSIRO Data61, Docklands, Victoria, Australia.
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36
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Hu YJ, Zhao G, Zhang B, Yang C, Zhang M, Liu ZK, Qian X, Qi L. Local electronic descriptors for solute-defect interactions in bcc refractory metals. Nat Commun 2019; 10:4484. [PMID: 31578329 PMCID: PMC6775119 DOI: 10.1038/s41467-019-12452-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Accepted: 09/09/2019] [Indexed: 11/09/2022] Open
Abstract
The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. Here we present a general linear correlation between two descriptors of local electronic structures and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic descriptor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. For a particular pair of solute-matrix elements, this linear correlation is valid independent of types of defects and the locations of substitutional sites. These results provide the possibility to apply local electronic descriptors for quantitative and efficient predictions on the solute-defect interactions and defect properties in alloys.
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Affiliation(s)
- Yong-Jie Hu
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ge Zhao
- Department of Statistics, Pennsylvania State University, State College, PA, 16802, USA
| | - Baiyu Zhang
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Chaoming Yang
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Mingfei Zhang
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Zi-Kui Liu
- Department of Materials Science and Engineering, Pennsylvania State University, State College, PA, 16802, USA
| | - Xiaofeng Qian
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Liang Qi
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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37
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Fraux G, Chibani S, Coudert FX. Modelling of framework materials at multiple scales: current practices and open questions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180220. [PMID: 31130101 PMCID: PMC6562347 DOI: 10.1098/rsta.2018.0220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties. This article is part of the theme issue 'Mineralomimesis: natural and synthetic frameworks in science and technology'.
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Jie J, Hu Z, Qian G, Weng M, Li S, Li S, Hu M, Chen D, Xiao W, Zheng J, Wang LW, Pan F. Discovering unusual structures from exception using big data and machine learning techniques. Sci Bull (Beijing) 2019; 64:612-616. [PMID: 36659629 DOI: 10.1016/j.scib.2019.04.015] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 03/22/2019] [Accepted: 04/01/2019] [Indexed: 01/21/2023]
Abstract
Recently, machine learning (ML) has become a widely used technique in materials science study. Most work focuses on predicting the rule and overall trend by building a machine learning model. However, new insights are often learnt from exceptions against the overall trend. In this work, we demonstrate that how unusual structures are discovered from exceptions when machine learning is used to get the relationship between atomic and electronic structures based on big data from high-throughput calculation database. For example, after training an ML model for the relationship between atomic and electronic structures of crystals, we find AgO2F, an unusual structure with both Ag3+ and O22-, from structures whose band gap deviates much from the prediction made by our model. A further investigation on this structure might shed light into the research on anionic redox in transition metal oxides of Li-ion batteries.
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Affiliation(s)
- Jianshu Jie
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Zongxiang Hu
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Guoyu Qian
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Mouyi Weng
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Shunning Li
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Shucheng Li
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Mingyu Hu
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Dong Chen
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Weiji Xiao
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Jiaxin Zheng
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Lin-Wang Wang
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley 94720, USA.
| | - Feng Pan
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
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39
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Mansouri Tehrani A, Brgoch J. Hard and superhard materials: A computational perspective. J SOLID STATE CHEM 2019. [DOI: 10.1016/j.jssc.2018.10.048] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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40
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Vasudevan RK, Choudhary K, Mehta A, Smith R, Kusne G, Tavazza F, Vlcek L, Ziatdinov M, Kalinin SV, Hattrick-Simpers J. Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics. MRS COMMUNICATIONS 2019; 9:10.1557/mrc.2019.95. [PMID: 32166045 PMCID: PMC7067066 DOI: 10.1557/mrc.2019.95] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 07/03/2019] [Indexed: 05/14/2023]
Abstract
The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.
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Affiliation(s)
- Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Kamal Choudhary
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Apurva Mehta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025
| | - Ryan Smith
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Gilad Kusne
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Francesca Tavazza
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Lukas Vlcek
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Jason Hattrick-Simpers
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
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Choudhary K, Bercx M, Jiang J, Pachter R, Lamoen D, Tavazza F. Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2019; 31:10.1021/acs.chemmater.9b02166. [PMID: 32165788 PMCID: PMC7067045 DOI: 10.1021/acs.chemmater.9b02166] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand. However, the lack of suitable materials hinders further progress of this technology. Here, we present the largest inorganic solar-cell material search to date using density functional theory (DFT) and machine-learning approaches. We calculated the spectroscopic limited maximum efficiency (SLME) using Tran-Blaha modified Becke-Johnson potential for 5097 non-metallic materials and identified 1997 candidates with an SLME higher than 10%, including 934 candidates with suitable convex-hull stability and effective carrier mass. Screening for 2D-layered cases, we found 58 potential materials and performed G0W0 calculations on a subset to estimate the prediction-uncertainty. As the above DFT methods are still computationally expensive, we developed a high accuracy machine learning model to pre-screen efficient materials and applied it to over a million materials. Our results provide a general framework and universal strategy for the design of high-efficiency solar cell materials. The data and tools are publicly distributed at: https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.ctcms.nist.gov/jarvisml/, https://jarvis.nist.gov/ and https://github.com/usnistgov/jarvis.
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Affiliation(s)
- Kamal Choudhary
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Marnik Bercx
- EMAT, Department of Physics, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Jie Jiang
- Materials Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433, USA
| | - Ruth Pachter
- Materials Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433, USA
| | - Dirk Lamoen
- EMAT, Department of Physics, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Francesca Tavazza
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
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42
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ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition. Sci Rep 2018; 8:17593. [PMID: 30514926 PMCID: PMC6279928 DOI: 10.1038/s41598-018-35934-y] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/06/2018] [Indexed: 01/13/2023] Open
Abstract
Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds.
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43
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Ahmad Z, Xie T, Maheshwari C, Grossman JC, Viswanathan V. Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes. ACS CENTRAL SCIENCE 2018; 4:996-1006. [PMID: 30159396 PMCID: PMC6107869 DOI: 10.1021/acscentsci.8b00229] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Indexed: 05/10/2023]
Abstract
Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.
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Affiliation(s)
- Zeeshan Ahmad
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Tian Xie
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chinmay Maheshwari
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Jeffrey C. Grossman
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Venkatasubramanian Viswanathan
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- E-mail:
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Mansouri Tehrani A, Oliynyk AO, Parry M, Rizvi Z, Couper S, Lin F, Miyagi L, Sparks TD, Brgoch J. Machine Learning Directed Search for Ultraincompressible, Superhard Materials. J Am Chem Soc 2018; 140:9844-9853. [DOI: 10.1021/jacs.8b02717] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Anton O. Oliynyk
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Marcus Parry
- Department of Materials Science and Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zeshan Rizvi
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Samantha Couper
- Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah 84112, United States
| | - Feng Lin
- Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah 84112, United States
| | - Lowell Miyagi
- Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah 84112, United States
| | - Taylor D. Sparks
- Department of Materials Science and Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Jakoah Brgoch
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
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45
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Li X, Curtis FS, Rose T, Schober C, Vazquez-Mayagoitia A, Reuter K, Oberhofer H, Marom N. Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation. J Chem Phys 2018; 148:241701. [PMID: 29960303 DOI: 10.1063/1.5014038] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.
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Affiliation(s)
- Xiayue Li
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Farren S Curtis
- Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Timothy Rose
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Christoph Schober
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universiät München, Lichtenbergstr. 4, D-85747 Garching, Germany
| | | | - Karsten Reuter
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universiät München, Lichtenbergstr. 4, D-85747 Garching, Germany
| | - Harald Oberhofer
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universiät München, Lichtenbergstr. 4, D-85747 Garching, Germany
| | - Noa Marom
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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46
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Li Z, Omidvar N, Chin WS, Robb E, Morris A, Achenie L, Xin H. Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations. J Phys Chem A 2018; 122:4571-4578. [DOI: 10.1021/acs.jpca.8b02842] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Zheng Li
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Noushin Omidvar
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Wei Shan Chin
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Esther Robb
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Amanda Morris
- Department of Chemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Luke Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Hongliang Xin
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
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47
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Xie T, Grossman JC. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. PHYSICAL REVIEW LETTERS 2018; 120:145301. [PMID: 29694125 DOI: 10.1103/physrevlett.120.145301] [Citation(s) in RCA: 441] [Impact Index Per Article: 73.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/15/2017] [Indexed: 05/21/2023]
Abstract
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10^{4} data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
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Affiliation(s)
- Tian Xie
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Jeffrey C Grossman
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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48
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Tasnádi F, Wang F, Odén M, Abrikosov IA. Thermal expansion of quaternary nitride coatings. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2018; 30:135901. [PMID: 29460845 DOI: 10.1088/1361-648x/aab0b8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The thermal expansion coefficient of technologically relevant multicomponent cubic nitride alloys are predicted using the Debye model with ab initio elastic constants calculated at 0 K and an isotropic approximation for the Grüneisen parameter. Our method is benchmarked against measured thermal expansion of TiN and Ti(1-x)Al x N as well as against results of molecular dynamics simulations. We show that the thermal expansion coefficients of Ti(1-x-y)X y Al x N (X = Zr, Hf, Nb, V, Ta) solid solutions monotonously increase with the amount of alloying element X at all temperatures except for Zr and Hf, for which they instead decrease for [Formula: see text].
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Affiliation(s)
- Ferenc Tasnádi
- Department of Physics, Chemistry and Biology (IFM), Linköping University, SE-581 83 Linköping, Sweden. Materials Modeling and Development Laboratory, National University of Science and Technology 'MISIS', 119049 Moscow, Russia
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49
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Jalem R, Nakayama M, Noda Y, Le T, Takeuchi I, Tateyama Y, Yamazaki H. A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2018; 19:231-242. [PMID: 29707064 PMCID: PMC5917445 DOI: 10.1080/14686996.2018.1439253] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/07/2018] [Accepted: 02/07/2018] [Indexed: 06/01/2023]
Abstract
Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density (d), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction.
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Affiliation(s)
- Randy Jalem
- Japan Science and Technology Agency (JST), PRESTO, Saitama, Japan
- National Institute for Materials Science – Global Research Center for Environment and Energy Based on Nanomaterials Science (NIMS – GREEN), Tsukuba, Japan
- Center for Materials Research by Information Integration (CMI), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – ‘Materials Research by Information Integration’ Initiative (NIMS – Mii), Tsukuba, Japan
| | - Masanobu Nakayama
- National Institute for Materials Science – Global Research Center for Environment and Energy Based on Nanomaterials Science (NIMS – GREEN), Tsukuba, Japan
- Center for Materials Research by Information Integration (CMI), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – ‘Materials Research by Information Integration’ Initiative (NIMS – Mii), Tsukuba, Japan
- Frontier Research Institute for Materials Science, Nagoya Institute of Technology, Nagoya, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Kyoto, Japan
| | - Yusuke Noda
- Center for Materials Research by Information Integration (CMI), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – ‘Materials Research by Information Integration’ Initiative (NIMS – Mii), Tsukuba, Japan
| | - Tam Le
- Center for Materials Research by Information Integration (CMI), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – ‘Materials Research by Information Integration’ Initiative (NIMS – Mii), Tsukuba, Japan
| | - Ichiro Takeuchi
- Center for Materials Research by Information Integration (CMI), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – ‘Materials Research by Information Integration’ Initiative (NIMS – Mii), Tsukuba, Japan
- Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Yoshitaka Tateyama
- National Institute for Materials Science – Global Research Center for Environment and Energy Based on Nanomaterials Science (NIMS – GREEN), Tsukuba, Japan
- Center for Materials Research by Information Integration (CMI), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – ‘Materials Research by Information Integration’ Initiative (NIMS – Mii), Tsukuba, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Kyoto, Japan
| | - Hisatsugu Yamazaki
- Battery Material Engineering & Research Division, Toyota Motor Corporation, Susono, Japan
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50
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Musil F, De S, Yang J, Campbell JE, Day GM, Ceriotti M. Machine learning for the structure-energy-property landscapes of molecular crystals. Chem Sci 2018; 9:1289-1300. [PMID: 29675175 PMCID: PMC5887104 DOI: 10.1039/c7sc04665k] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 12/11/2017] [Indexed: 12/18/2022] Open
Abstract
Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol-1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure-property relations in molecular crystal engineering.
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Affiliation(s)
- Félix Musil
- National Center for Computational Design and Discovery of Novel Materials (MARVEL) , Laboratory of Computational Science and Modelling , Institute of Materials , Ecole Polytechnique Federale de Lausanne , Lausanne , Switzerland . ;
| | - Sandip De
- National Center for Computational Design and Discovery of Novel Materials (MARVEL) , Laboratory of Computational Science and Modelling , Institute of Materials , Ecole Polytechnique Federale de Lausanne , Lausanne , Switzerland . ;
| | - Jack Yang
- School of Chemistry , University of Southampton , Highfield , Southampton , UK
| | - Joshua E Campbell
- School of Chemistry , University of Southampton , Highfield , Southampton , UK
| | - Graeme M Day
- School of Chemistry , University of Southampton , Highfield , Southampton , UK
| | - Michele Ceriotti
- National Center for Computational Design and Discovery of Novel Materials (MARVEL) , Laboratory of Computational Science and Modelling , Institute of Materials , Ecole Polytechnique Federale de Lausanne , Lausanne , Switzerland . ;
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