| Assessing conformer energies using electronic structure and machine learning methods D Folmsbee, G Hutchison International Journal of Quantum Chemistry 121 (1), e26381, 2021 | 59 | 2021 |
| Evaluation of thermochemical machine learning for potential energy curves and geometry optimization DL Folmsbee, DR Koes, GR Hutchison The Journal of Physical Chemistry A 125 (9), 1987-1993, 2021 | 9 | 2021 |
| Deep learning coordinate-free quantum chemistry MK Matlock, M Hoffman, NL Dang, DL Folmsbee, LA Langkamp, ... The Journal of Physical Chemistry A 125 (40), 8978-8986, 2021 | 7 | 2021 |
| Evaluating fast methods for static polarizabilities on extended conjugated oligomers DC Hiener, DL Folmsbee, LA Langkamp, GR Hutchison Physical Chemistry Chemical Physics 24 (38), 23173-23181, 2022 | 5 | 2022 |
| Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences DL Folmsbee, DR Koes, GR Hutchison Journal of Chemical Information and Modeling 63 (23), 7401-7411, 2023 | 2 | 2023 |
| Evaluating and Improving the Viability of Machine Learning to Solve Chemical Problems DL Folmsbee University of Pittsburgh, 2022 | | 2022 |
| Response to Reviews of" Assessing Conformer Energies" G Hutchison, D Folmsbee Authorea Preprints, 2020 | | 2020 |
| Efficient Bayesian sampling of molecular conformers: Understanding torsional correlations and entropy effects G Hutchison, LS Chan, GM Morris, D Folmsbee American Chemical Society SciMeetings 1 (1), 2020 | | 2020 |
| Assessing conformer energies: Machine learning vs conventional quantum chemistry D Folmsbee American Chemical Society SciMeetings 1 (1), 2017 | | 2017 |
