103 citations · 183 across the 3 of their papers we have counts for
3 papers
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao +8
We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calcul…
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…
Energy benchmarks for water clusters and ice structures from an embedded many-body expansion
M. J. Gillan, D. Alfe, P. J. Bygrave +2
We show how an embedded many-body expansion (EMBE) can be used to calculate accurate \emph{ab initio} energies of water clusters and ice structures using wavefunction-based methods…