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RESEARCH PAPER · npj Computational Materials · 2022

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

Sungwoo Kang, Wonseok Jeong, Chang‐Ho Hong, Seungwoo Hwang, Youngchae Yoon, Seungwu Han

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Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentialsOriginalData-driven for accelerated design strategy of photocatalytic degradation activity prediction of doped TiO2 photocatalyst38Machine learning versus human learning in predicting glass-forming ability of metallic glasses56Ab initio construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials9A Cation-Driven Approach toward Deep-Ultraviolet Nonlinear Optical Materials17Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag10Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Spiral growth of multicomponent crystals: theoretical aspects4Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials29Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials21Applications and training sets of machine learning potentials17Evolutionary Machine Learning in Science and Engineering0Accelerating search for the polar phase stability of ferroelectric oxide by machine learning4Electrochemical Degradation of Pt3Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials22Transferable empirical pseudopotenials from machine learning13Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning19Local Structures of Ex-Solved Nanoparticles Identified by Machine-Learned Potentials9Intelligent Structure Searching and Designs for Nanoclusters: Effective Units in Atomic Manufacturing14Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies33Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets11Accurate Crystal Structure Prediction of New 2D Hybrid Organic–Inorganic Perovskites23Crystal structure generation with autoregressive large language modeling157How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm12Machine learning-driven global optimization reveals nanometre-scale mixed phases of borophene on Ag(100)6Prediction of novel gallium–sulfur compositions under pressure0MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration78Crystal Structure Prediction of Cs–Te with Supervised Machine Learning2Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation1Discovery of Crystalline Inorganic Solids in the Digital Age4Efficient Configuration Sampling for Hybrid Functional DFT Calculations to Train Machine‐Learning Potentials: Application to Atmospheric Chemistry3Neural network-driven molecular insights into alkaline wet etching of GaN: toward atomistic precision in nanostructure fabrication3A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction12Computational exploration of novel polar oxides: From composition selection to crystal structure prediction1Machine Learning-Assisted Crystal Structure Prediction of Solid-State Electrolytes Reveals Superior Ionic Conductivity in Metastable Edge-Sharing Phases8Discovery of oxide Li-conducting electrolytes with corner-sharing frameworks via topology-constrained crystal structure prediction1A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials0Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges7Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science12Machine‐Learning‐Assisted Elucidation of Ion Transport Mechanism in Heterogeneous Interphases Formed at the Electrolyte–Electrode Interfaces0

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38 direct citing papers saved.

  1. Data-driven for accelerated design strategy of photocatalytic degradation activity prediction of doped TiO2 photocatalyst

    Journal of Water Process Engineering · 2022-09-09

    Qing Liu, Kewei Pan, Ying Lu, Wei Wei, Shixing Wang, Wenjia Du, Zhao Ding, Yang Min Zhou

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  2. Machine learning versus human learning in predicting glass-forming ability of metallic glasses

    Acta Materialia · 2022-11-02

    Guannan Liu, Sungwoo Sohn, Sebastian Alexander Kube, Arindam Raj, Andrew Mertz, Aya Nawano, Anna Gilbert, Mark D. Shattuck, Corey S. O’Hern, Jan Schroers

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  3. Ab initio construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials

    Physical Review Materials · 2022-11-21

    Kyeongpung Lee, Yutack Park, Seungwu Han

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  4. A Cation-Driven Approach toward Deep-Ultraviolet Nonlinear Optical Materials

    Research · 2023-01-01

    Cong Hu, Meng Cheng, Wenqi Jin, Jian Wei Han, Zhihua Yang, Shilie Pan

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  5. Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag

    Physical Chemistry Chemical Physics · 2023-01-01

    Aidan Thorn, Daviti Gochitashvili, Saba Kharabadze, Aleksey N. Kolmogorov

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  6. Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

    Journal of Materials Chemistry A · 2023-01-01

    Swarn Jha, Matthew Yen, Yazmin Soto Salinas, Evan Palmer, John Anzel Villafuerte, Hong Yu Liang

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  7. Spiral growth of multicomponent crystals: theoretical aspects

    Frontiers in Chemistry · 2023-05-12

    Alexey V. Redkov

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  8. Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials

    Journal of the American Chemical Society · 2023-08-11

    Seungwoo Hwang, Jisu Jung, Chang‐Ho Hong, Wonseok Jeong, Sungwoo Kang, Seungwu Han

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  9. Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials

    iScience · 2023-09-21

    Gui Chen, Zhihao Zhang, Zongyi Li, Chen Luo, Jiang Xia, Xing Yang Wu, Junhao Chu

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  10. Applications and training sets of machine learning potentials

    Science and Technology of Advanced Materials Methods · 2023-10-12

    Chang‐Ho Hong, Jaehoon Kim, Jaesun Kim, Jisu Jung, Suyeon Ju, Jeong Min Choi, Seungwu Han

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  11. Evolutionary Machine Learning in Science and Engineering

    Genetic and evolutionary computation · 2023-11-01

    Jianjun Hu, Yuqi Song, Sadman Sadeed Omee, Lai Wei, Rongzhi Dong, Siddharth Gianey

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  12. Accelerating search for the polar phase stability of ferroelectric oxide by machine learning

    Materials & Design · 2023-11-25

    Md Mokhlesur Rahman, Shazia Janwari, Minsu Choi, Umesh Vasudeo Waghmare, Jaichan Lee

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  13. Electrochemical Degradation of Pt3Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials

    ACS Catalysis · 2023-11-30

    Jisu Jung, Suyeon Ju, Purun-hanul Kim, Deokgi Hong, Wonseok Jeong, Jinhee Lee, Seungwu Han, Sungwoo Kang

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  14. Transferable empirical pseudopotenials from machine learning

    Physical review. B./Physical review. B · 2024-01-31

    Rokyeon Kim, Young‐Woo Son

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  15. Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning

    Journal of the American Chemical Society · 2024-03-28

    Shin Kiyohara, Yoyo Hinuma, Fumiyasu Oba

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  16. Local Structures of Ex-Solved Nanoparticles Identified by Machine-Learned Potentials

    Nano Letters · 2024-04-01

    Sungwoo Kang, Jun Kyu Kim, Hyun‐Ah Kim, You‐Hwan Son, Jaehee Chang, Jinwoo Kim, Dongwook Kim, Jong-Min Lee, Hyuk Jae Kwon

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  17. Intelligent Structure Searching and Designs for Nanoclusters: Effective Units in Atomic Manufacturing

    Advanced Intelligent Systems · 2024-04-26

    Junfeng Gao, Luneng Zhao, Yuan Chang, Yanxue Zhang, Shi Yin Qiu, Yuanyuan Zhao, Hongsheng Liu, Jijun Zhao

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  18. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

    Nature Computational Science · 2024-05-23

    Jiahao Xie, Yansong Zhou, Muhammad Faizan, Zewei Li, Tianshu Li, Yuhao Fu, Xinjiang Wang, Lijun Zhang

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  19. Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets

    Nature Communications · 2024-08-08

    Yingxue Sun, Yuanyi Zhao, Xinjian Xie, Hongjiao Li, Wenqian Feng

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  20. Accurate Crystal Structure Prediction of New 2D Hybrid Organic–Inorganic Perovskites

    Journal of the American Chemical Society · 2024-09-30

    Nima Karimitari, William J. Baldwin, Evan W. Muller, Zachary J. L. Bare, William Joshua Kennedy, Gábor Cśanyi, Christopher A. Sutton

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  21. Crystal structure generation with autoregressive large language modeling

    Nature Communications · 2024-12-06

    Luis M. Antunes, Keith T. Butler, Ricardo Grau‐Crespo

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  22. How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm

    The Journal of Chemical Physics · 2024-12-23

    Sungwoo Kang

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  23. Machine learning-driven global optimization reveals nanometre-scale mixed phases of borophene on Ag(100)

    Chemical Science · 2025-01-01

    Yunlei Wang, Haifeng Lv, Xiaojun Wu

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  24. Prediction of novel gallium–sulfur compositions under pressure

    Physical Chemistry Chemical Physics · 2025-01-01

    Reza Shahsavari, Sylvain Pitié, Somayeh Faraji, Lewis J. Conway, Chris J. Pickard, S. Javad Hashemifar, Alireza Shahidi, Gilles Frapper

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  25. MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration

    Progress in Materials Science · 2025-01-20

    Vimukthi Dananjaya, Nethmi Hansika, Sathish Marimuthu, Venkata S. Chevali, Yogendra Kumar Mishra, Andrews Nirmala Grace, Nisa V. Salim, Chamil Abeykoon

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  26. Crystal Structure Prediction of Cs–Te with Supervised Machine Learning

    Advanced Theory and Simulations · 2025-01-24

    Holger‐Dietrich Saßnick, Caterina Cocchi

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  27. Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation

    Journal of Chemical Information and Modeling · 2025-04-14

    Wuling Zhao, Minxia Zhou, Jialin Shao, Jingzheng Ren, Yusha Hu, Yulin Han, Yi Man

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  28. Discovery of Crystalline Inorganic Solids in the Digital Age

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    Dmytro Antypov, Andrij Vasylenko, Christopher M. Collins, Luke M. Daniels, George R. Darling, Matthew Stephen Dyer, John B. Claridge, Matthew J. Rosseinsky

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  29. Efficient Configuration Sampling for Hybrid Functional DFT Calculations to Train Machine‐Learning Potentials: Application to Atmospheric Chemistry

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  30. Neural network-driven molecular insights into alkaline wet etching of GaN: toward atomistic precision in nanostructure fabrication

    npj Computational Materials · 2025-10-20

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  31. A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction

    Crystals · 2025-10-28

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  32. Computational exploration of novel polar oxides: From composition selection to crystal structure prediction

    Physical Review Materials · 2025-11-18

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  33. Machine Learning-Assisted Crystal Structure Prediction of Solid-State Electrolytes Reveals Superior Ionic Conductivity in Metastable Edge-Sharing Phases

    Journal of the American Chemical Society · 2025-12-12

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  34. Discovery of oxide Li-conducting electrolytes with corner-sharing frameworks via topology-constrained crystal structure prediction

    Journal of Materials Chemistry A · 2026-01-01

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  35. A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials

    High Entropy Alloys & Materials · 2026-03-01

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  36. Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

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  37. Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science

    Chemical Reviews · 2026-04-08

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  38. Machine‐Learning‐Assisted Elucidation of Ion Transport Mechanism in Heterogeneous Interphases Formed at the Electrolyte–Electrode Interfaces

    Advanced Energy Materials · 2026-07-24

    Wonseok Jeong, Nicole Adelstein, Suyue Yuan, Steven Jay Jue, Spencer Kirkman, Liwen F. Wan

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