Publications 2022 2021 2020 2019 2022 Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences 6. Folmsbee, D., Koes, D., Hutchison, G. Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences. ChemRxiv. 2022. Evaluating Fast Methods for Static Polarizabilities on Extended Conjugated Oligomers 5. Hiener, D., Folmsbee, D., Langkamp, L., Hutchison, G. Evaluating Fast Methods for Static Polarizabilities on Extended Conjugated Oligomers. Phys. Chem. Chem. Phys. 2022. 2021 Deep Learning Coordinate-Free Quantum Chemistry 4. Matlock, M., Hoffman, M., Dang, N., Folmsbee, D., Langkamp, L., Hutchison, G., Kumar, N., Sarullo, K., Swamidass, S. J. Deep Learning Coordinate-Free Quantum Chemistry. J. Phys. Chem. A. 2021. Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization 3. Folmsbee, D., Koes, D., Hutchison, G. Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization. J. Phys. Chem. A. 2021. 2020 Assessing Conformer Energies using Electronic Structure and Machine Learning Methods 2. Folmsbee, D., Hutchison, G. Assessing conformer energies using electronic structure and machine learning methods. Int J Quantum Chem. 2020. 2019 chemreps/chemreps: Molecular Machine Learning Representations 1. D. Folmsbee, S. Upadhyay, A. Dumi, D. Hiener, & D. Mulvey. (2019, July 12). chemreps/chemreps: Molecular Machine Learning Representations(Version 0.1.1). Zenodo. orcid.org/0000-0002-4094-233X
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