| 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 | 28 | 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 | 23* | 2023 |
| 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 | 16 | 2022 |
| 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 | 16 | 2020 |
| 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 | 14* | 2024 |
| 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 | 14 | 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 | 14 | 2020 |
| 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 | 11 | 2021 |
| 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 | 10 | 2020 |
| 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 | 9 | 2023 |
| 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 |
| 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 | 7 | 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 | 6 | 2022 |
| Magnetochemical coupling effects on thermodynamics, point-defect formation and diffusion in Fe-Ni alloys: a theoretical study K Li arXiv preprint arXiv:2302.00186, 2023 | 4 | 2023 |
| 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), 2023 | 4 | 2023 |
| Towards accurate thermodynamics from random energy sampling T Schuler, M Nastar, K Li, CC Fu Acta Materialia, 120074, 2024 | | 2024 |
| Probing out-of-distribution generalization in machine learning for materials K Li, A Niyongabo Rubungo, X Lei, D Persaud, K Choudhary, B DeCost, ... arXiv e-prints, arXiv: 2406.06489, 2024 | | 2024 |
| Accurate predictions of keyhole depths using machine learning-aided simulations J Zhang, R Jiang, K Li, P Chen, X Shang, Z Liu, J Hattrick-Simpers, ... arXiv preprint arXiv:2402.16190, 2024 | | 2024 |
| A reproducibility study of atomistic line graph neural networks for materials property prediction K Li, B DeCost, K Choudhary, J Hattrick-Simpers Digital Discovery, 2024 | | 2024 |
| 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, 2024 | | 2024 |
Jason Hattrick-SimpersDepartment of Materials Science and Engineering University of TorontoVerified email at utoronto.ca
