245 citations · 1k across the 13 of their papers we have counts for
9 papers · 1 filter
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…
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar +2
We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-netw…
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
Lixue Cheng, Nikola B. Kovachki, Matthew Welborn +1
Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-F…
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
Lixue Cheng, Matthew Welborn, Anders S. Christensen +1
We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and…
Even-handed subsystem selection in projection-based embedding
Matthew Welborn, Frederick R. Manby, Thomas F. Miller
Projection-based embedding offers a simple framework for embedding correlated wavefunction methods in density functional theory. Partitioning between the correlated wavefunction an…
Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
Matthew Welborn, Lixue Cheng, Thomas F. Miller
We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of in…
Incremental Embedding: A Density Matrix Embedding Scheme for Molecules
Hong-Zhou Ye, Matthew Welborn, Nathan D. Ricke +1
The idea of using fragment embedding to circumvent the high computational scaling of accurate electronic structure methods while retaining high accuracy has been a long-standing go…
-SCF: A Direct Energy-targeting Method To Mean-field Excited States
Hong-Zhou Ye, Matthew Wellborn, Nathan D. Ricke +1
The mean-field solutions of electronic excited states are much less accessible than ground state (e.g.\ Hartree-Fock) solutions. Energy-based optimization methods for excited state…