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RESEARCH PAPER · The Journal of Chemical Physics · 2018

Predicting the stability of ternary intermetallics with density functional theory and machine learning

Jonathan Schmidt, Liming Chen, Silvana Botti, Miguel A. L. Marques

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42 direct citing works

Predicting the stability of ternary intermetallics with density functional theory and machine learningOriginalGuest Editorial: Special Topic on Data-Enabled Theoretical Chemistry98Data-centric science for materials innovation42Database Screening of Ternary Chalcogenides for P-type Transparent Conductors55CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks14Recent advances and applications of machine learning in solid-state materials science2607In silico investigation of Cu(In,Ga)Se 2 -based solar cells3Discovering Intermetallics Through Synthesis, Computation, and Data‐Driven Analysis29AI Applications through the Whole Life Cycle of Material Discovery192Machine learning formation enthalpies of intermetallics22Machine learning for halide perovskite materials130Machine learning in materials design: Algorithm and application*62Design of Materials for Nuclear Energy Applications: First-Principles Calculations and Artificial Intelligence Methods8Predicting stable crystalline compounds using chemical similarity113Innovative Materials Science via Machine Learning194Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells90Deformation potential extraction and computationally efficient mobility calculations in silicon from first principles67Crystal graph attention networks for the prediction of stable materials153A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals51Application of machine learning for advanced material prediction and design123Machine-learning correction to density-functional crystal structure optimization15Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects41High-throughput identification of materials for silicon tandem solar cells0Transfer learning on large datasets for the accurate prediction of material properties36Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109A Two-Step Machine Learning Method for Predicting the Formation Energy of Ternary Compounds7Mapping superconductivity in high-pressure hydrides: The Superhydra project24Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques23Thermal equation of state of U6Fe from experiments and calculations3Machine-learning surrogate model for accelerating the search of stable ternary alloys5Accelerating the prediction of stable materials with machine learning104Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange52Improving Machine-Learning Models in Materials Science Through Large Datasets6Impact of Li disorder on the open cell voltage profile of LixTi6O13 anodes: A theoretical study5Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange18Multi-scale computational study of high-temperature corrosion and the design of corrosion-resistant alloys33Improving machine-learning models in materials science through large datasets115Atomic fingerprints for high throughput screening of 2D monolayers2Wide-ranging predictions of new stable compounds powered by recommendation engines12Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation13

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  1. Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

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  2. Data-centric science for materials innovation

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  3. Database Screening of Ternary Chalcogenides for P-type Transparent Conductors

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  4. CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks

    arXiv (Cornell University) · 2018-10-26

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  5. Recent advances and applications of machine learning in solid-state materials science

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  6. In silico investigation of Cu(In,Ga)Se 2 -based solar cells

    Physical Chemistry Chemical Physics · 2020-01-01

    Hossein Mirhosseini, Ramya Kormath Madam Raghupathy, Sudhir Kumar Sahoo, Hendrik Wiebeler, Manjusha Chugh, Thomas D. Kühne

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  7. Discovering Intermetallics Through Synthesis, Computation, and Data‐Driven Analysis

    Chemistry - A European Journal · 2020-03-18

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  8. AI Applications through the Whole Life Cycle of Material Discovery

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    Jiali Li, Kaizhuo Lim, Haitao Yang, Zekun Ren, S. Raghavan, Po‐Yen Chen, Tonio Buonassisi, Xiaonan Wang

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  9. Machine learning formation enthalpies of intermetallics

    Journal of Applied Physics · 2020-09-08

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  10. Machine learning for halide perovskite materials

    Nano Energy · 2020-09-12

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  11. Machine learning in materials design: Algorithm and application*

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    Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin

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  12. Design of Materials for Nuclear Energy Applications: First-Principles Calculations and Artificial Intelligence Methods

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  13. Predicting stable crystalline compounds using chemical similarity

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  14. Innovative Materials Science via Machine Learning

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  15. Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells

    Solar Energy · 2021-10-13

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  16. Deformation potential extraction and computationally efficient mobility calculations in silicon from first principles

    Physical review. B./Physical review. B · 2021-11-03

    Zhen Li, Patrizio Graziosi, Neophytos Neophytou

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  17. Crystal graph attention networks for the prediction of stable materials

    Science Advances · 2021-12-03

    Jonathan Schmidt, Love Pettersson, Claudio Verdozzi, Silvana Botti, Miguel A. L. Marques

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  18. A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals

    Scientific Data · 2022-03-02

    Jonathan Schmidt, Hai‐Chen Wang, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques

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  19. Application of machine learning for advanced material prediction and design

    EcoMat · 2022-03-07

    Cheuk Hei Chan, Mingzi Sun, Bolong Huang

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  20. Machine-learning correction to density-functional crystal structure optimization

    MRS Bulletin · 2022-04-27

    Robert Hussein, Jonathan Schmidt, Tomás Barros, Miguel A. L. Marques, Silvana Botti

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  21. Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects

    Catalysis Reviews · 2022-07-30

    Masilamani Karthikeyan, Durga Madhab Mahapatra, Abdul Syukor Abd Razak, Abdulaziz A.M. Abahussain, Baranitharan Ethiraj, Lakhveer Singh

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  22. High-throughput identification of materials for silicon tandem solar cells

    Sustainable Energy & Fuels · 2023-01-01

    Yan-Zhen Zhao, Zhikun Yao, Chang Liu, Wei Li Ren, Geoffroy Hautier, Lee A. Burton

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  23. Transfer learning on large datasets for the accurate prediction of material properties

    Digital Discovery · 2023-01-01

    Noah Hoffmann, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques

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  24. Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials

    Advanced Materials · 2023-03-22

    Jonathan Schmidt, Noah Hoffmann, Hai‐Chen Wang, Pedro Borlido, Pedro J. M. A. Carriço, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques

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  25. A Two-Step Machine Learning Method for Predicting the Formation Energy of Ternary Compounds

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    Varadarajan Rengaraj, Sebastian Jost, Franz Bethke, Christian Plessl, Hossein Mirhosseini, Andrea Walther, Thomas D. Kühne

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  26. Mapping superconductivity in high-pressure hydrides: The Superhydra project

    Physical Review Materials · 2023-05-31

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  27. Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques

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  28. Thermal equation of state of U6Fe from experiments and calculations

    Physical review. B./Physical review. B · 2023-08-28

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  29. Machine-learning surrogate model for accelerating the search of stable ternary alloys

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  30. Accelerating the prediction of stable materials with machine learning

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  33. Impact of Li disorder on the open cell voltage profile of LixTi6O13 anodes: A theoretical study

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  35. Multi-scale computational study of high-temperature corrosion and the design of corrosion-resistant alloys

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  36. Improving machine-learning models in materials science through large datasets

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  37. Atomic fingerprints for high throughput screening of 2D monolayers

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