233 citations · 609 across the 13 of their papers we have counts for
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physics.chem-ph2019★ 70 cited
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…
physics.chem-ph2019★ 112 cited
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…