112 citations · 121 across the 2 of their papers we have counts for
4 papers
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…
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…