129 citations · 286 across the 5 of their papers we have counts for
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physics.chem-ph2020★ 15 cited
Analytical Gradients for Molecular-Orbital-Based Machine Learning
Sebastian J. R. Lee, Tamara Husch, Feizhi Ding +1
Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivati…
physics.chem-ph2020★ 9 cited
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces
Zhuoran Qiao, Feizhi Ding, Matthew Welborn +5
We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-co…