| 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 | 58 | 2023 |
| 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 |
| 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 |
| AMPERE: automated modular platform for expedited and reproducible electrochemical testing J Abed, Y Bai, D Persaud, J Kim, J Witt, J Hattrick-Simpers, EH Sargent Digital Discovery 3 (11), 2265-2274, 2024 | 1 | 2024 |
| Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’ D Persaud, L Ward, J Hattrick-Simpers Digital Discovery 3 (2), 281-286, 2024 | 1 | 2024 |
