| Matminer: An open source toolkit for materials data mining L Ward, A Dunn, A Faghaninia, NER Zimmermann, S Bajaj, Q Wang, ... Computational Materials Science 152, 60-69, 2018 | 614 | 2018 |
| CIDER: An expressive, nonlocal feature set for machine learning density functionals with exact constraints K Bystrom, B Kozinsky Journal of Chemical Theory and Computation 18 (4), 2180-2192, 2022 | 18 | 2022 |
| Pawpyseed: Perturbation-extrapolation band shifting corrections for point defect calculations K Bystrom, D Broberg, S Dwaraknath, KA Persson, M Asta arXiv preprint arXiv:1904.11572, 2019 | 17 | 2019 |
| High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications D Broberg, K Bystrom, S Srivastava, D Dahliah, BAD Williamson, ... npj Computational Materials 9 (1), 72, 2023 | 14 | 2023 |
| Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set CJ Owen, SB Torrisi, Y Xie, S Batzner, K Bystrom, J Coulter, A Musaelian, ... arXiv preprint arXiv:2302.12993, 2023 | 5 | 2023 |
| Nonlocal machine-learned exchange functional for molecules and solids K Bystrom, B Kozinsky arXiv preprint arXiv:2303.00682, 2023 | 2 | 2023 |
| Addressing the Band Gap Problem with a Machine-Learned Exchange Functional K Bystrom, S Falletta, B Kozinsky arXiv preprint arXiv:2403.17002, 2024 | | 2024 |
| Machine Learning of Density Functionals for Accurate, Large-Scale Materials Simulations K Bystrom, S Falletta, B Kozinsky Bulletin of the American Physical Society, 2024 | | 2024 |
| Chemical Transferability and Accuracy of Ionic Liquid Simulations with Machine Learning Interatomic Potentials ZAH Goodwin, MB Wenny, JH Yang, A Cepellotti, K Bystrom, ... arXiv preprint arXiv:2403.01980, 2024 | | 2024 |
| Understanding Metal Ion Interactions in Solvents Using First-Principles and Machine Learning Interatomic Potentials J Yang, K Bystrom, B Kozinsky APS March Meeting Abstracts 2023, D17. 010, 2023 | | 2023 |
| Efficient Implementation of Machine Learning-Based Nonlocal Functionals for Molecules and Solids K Bystrom, B Kozinsky APS March Meeting Abstracts 2023, B17. 004, 2023 | | 2023 |
| Improving the Accuracy and Efficiency of Nonlocal Exchange Functionals via Machine Learning K Bystrom, B Kozinsky APS March Meeting Abstracts 2022, S47. 011, 2022 | | 2022 |
| Data-Driven Exchange-Correlation Functional Design for Transferability and Interpretability K Bystrom, B Kozinsky APS March Meeting Abstracts 2021, C19. 005, 2021 | | 2021 |
