| Machine learning prediction on the fractional free volume of polymer membranes L Tao, J He, T Arbaugh, JR McCutcheon, Y Li Journal of Membrane Science 665, 121131, 2023 | 41 | 2023 |
| Molecular mechanisms of thickness-dependent water desalination in polyamide reverse-osmosis membranes J He, T Arbaugh, D Nguyen, W Xian, EMV Hoek, JR McCutcheon, Y Li Journal of Membrane Science 674, 121498, 2023 | 27 | 2023 |
| Molecular self-assembled monolayers anomalously enhance thermal conductance across polymer–semiconductor interfaces J He, L Tao, W Xian, T Arbaugh, Y Li Nanoscale 14 (47), 17681-17693, 2022 | 7 | 2022 |
| Unified machine learning protocol for copolymer structure-property predictions L Tao, T Arbaugh, J Byrnes, V Varshney, Y Li STAR protocols 3 (4), 101875, 2022 | 4 | 2022 |
| Computationally efficient machine-learned model for GST phase change materials via direct and indirect learning OR Dunton, T Arbaugh, FW Starr The Journal of Chemical Physics 162 (3), 2025 | 1 | 2025 |
| A machine learning interatomic potential for Ge-Te alloys T Arbaugh, O Dunton, F Starr APS March Meeting Abstracts 2024, D18. 008, 2024 | | 2024 |
| Efficient Modelling of Ge15Te85 using Active Learning Methods T Arbaugh, F Starr APS March Meeting Abstracts 2023, D02. 012, 2023 | | 2023 |
