129 citations · 286 across the 5 of their papers we have counts for
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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…
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…
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…
The Chronus Quantum (ChronusQ) Software Package
David B. Williams-Young, Alessio Petrone, Shichao Sun +12
The Chronus Quantum (ChronusQ) software package is an open source (under the GNU General Public License v2) software infrastructure which targets the solution of challenging proble…
Analytical Gradients for Projection-Based Wavefunction-in-DFT Embedding
Sebastian J. R. Lee, Feizhi Ding, Frederick R. Manby +1
Projection-based embedding provides a simple, robust, and accurate approach for describing a small part of a chemical system at the level of a correlated wavefunction method while…