A critical examination of robustness and generalizability of machine learning prediction of materials properties K Li, B DeCost, K Choudhary, M Greenwood, J Hattrick-Simpers npj Computational Materials 9 (1), 55, 2023 | 55 | 2023 |
Exploiting redundancy in large materials datasets for efficient machine learning with less data K Li, D Persaud, K Choudhary, B DeCost, M Greenwood, ... Nature Communications 14 (1), 7283, 2023 | 51 | 2023 |
JARVIS-Leaderboard: a large scale benchmark of materials design methods K Choudhary, D Wines, K Li, KF Garrity, V Gupta, AH Romero, JT Krogel, ... npj Computational Materials 10 (1), 93, 2024 | 23* | 2024 |
Magnetochemical effects on phase stability and vacancy formation in fcc Fe-Ni alloys K Li, CC Fu, M Nastar, F Soisson, MY Lavrentiev Physical Review B 106 (02), 024106, 2022 | 18 | 2022 |
Ground-state properties and lattice-vibration effects of disordered Fe-Ni systems for phase stability predictions K Li, CC Fu Physical Review Materials 4 (2), 023606, 2020 | 18 | 2020 |
Combining DFT and CALPHAD for the development of on-lattice interaction models: The case of Fe-Ni system Y Wang*, K Li*, F Soisson*, CS Becquart* Physical Review Materials 4 (11), 113801, 2020 | 17 | 2020 |
Predicting magnetization of ferromagnetic binary Fe alloys from chemical short range order VT Tran, CC Fu, K Li Computational Materials Science 172, 109344, 2020 | 17 | 2020 |
Editors’ Choice—AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy R Zhang, R Black, D Sur, P Karimi, K Li, B DeCost, JR Scully, ... Journal of The Electrochemical Society 170 (8), 086502, 2023 | 16 | 2023 |
Effects of magnetic excitations and transitions on vacancy formation: Cases of fcc Fe and Ni compared to bcc Fe K Li, CC Fu, A Schneider Physical Review B 104 (10), 104406, 2021 | 13 | 2021 |
Predicting atomic diffusion in concentrated magnetic alloys: The case of paramagnetic Fe-Ni K Li, CC Fu, M Nastar, F Soisson Physical Review B 107 (9), 094103, 2023 | 11 | 2023 |
Synergistic effects of applied strain and cascade overlap on irradiation damage in BCC iron K Lai, K Li, H Wen, Q Guo, B Wang, Y Zheng Journal of Nuclear Materials 542, 152422, 2020 | 11 | 2020 |
Designing durable, sustainable, high-performance materials for clean energy infrastructure J Hattrick-Simpers, K Li, M Greenwood, R Black, J Witt, M Kozdras, ... Cell Reports Physical Science 4 (1), 101200, 2023 | 8 | 2023 |
Artificial intelligence for materials research at extremes B Maruyama, J Hattrick-Simpers, W Musinski, L Graham-Brady, K Li, ... MRS Bulletin 47 (11), 1154-1164, 2022 | 8 | 2022 |
Magnetic and atomic short range order in alloys I Mirebeau, V Pierron-Bohnes, C Decorse, E Rivière, CC Fu, K Li, ... Physical Review B 100 (22), 224406, 2019 | 8 | 2019 |
Probing out-of-distribution generalization in machine learning for materials K Li, AN Rubungo, X Lei, D Persaud, K Choudhary, B DeCost, AB Dieng, ... Communications Materials 6 (1), 9, 2025 | 5 | 2025 |
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials K Li, K Choudhary, B DeCost, M Greenwood, J Hattrick-Simpers Journal of Materials Chemistry A 12, 12412, 2024 | 5 | 2024 |
Magnetochemical coupling effects on thermodynamics, point-defect formation and diffusion in Fe-Ni alloys: a theoretical study K Li Université Paris-Saclay, 2021 | 4 | 2021 |
Bayesian assessment of commonly used equivalent circuit models for corrosion analysis in electrochemical impedance spectroscopy R Zhang, D Sur, K Li, J Witt, R Black, A Whittingham, JR Scully, ... npj Materials Degradation 8 (1), 120, 2024 | 2 | 2024 |
Towards accurate thermodynamics from random energy sampling T Schuler*, M Nastar, K Li* (co-corresponding), CC Fu Acta Materialia 276, 120074, 2024 | 2 | 2024 |
A call for caution in the era of AI-accelerated materials science K Li, E Kim, Y Fehlis, D Persaud, B DeCost, M Greenwood, ... Matter 6 (12), 4116-4117, 2023 | 2 | 2023 |