245 citations · 2.1k across the 36 of their papers we have counts for
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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…
A generalized class of strongly stable and dimension-free T-RPMD integrators
Jorge L. Rosa-Raíces, Jiace Sun, Nawaf Bou-Rabee +1
Recent work shows that strong stability and dimensionality freedom are essential for robust numerical integration of thermostatted ring-polymer molecular dynamics (T-RPMD) and path…
Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
Tamara Husch, Jiace Sun, Lixue Cheng +2
Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. We d…
Sum-Frequency Signals in 2D-Terahertz-Terahertz-Raman Spectroscopy
Griffin Mead, Haw-Wei Lin, Ioan-Bogdan Magdău +2
We demonstrate that halogenated methane 2D-Terahertz Terahertz Raman (2D-TTR) spectra are determined by the complicated structure of the instrument response function (IRF) along $f…
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…