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
56 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
53 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 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
6 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
6 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 
Jason Hattrick-SimpersDepartment of Materials Science and Engineering University of TorontoZweryfikowany adres z utoronto.ca
