245 citations · 1.3k across the 17 of their papers we have counts for
10 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…
Reliable transition properties from excited-state mean-field calculations
Susannah Bourne Worster, Oliver Feighan, Frederick R. Manby
Delta-self-consistent field theory (delta-SCF) is a conceptually simple and computationally inexpensive method for finding excited states. Using the maximum overlap method to guide…
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…
Coupling electrons and vibrations in molecular quantum chemistry
Thomas Dresselhaus, Callum B. A. Bungey, Peter J. Knowles +1
We derive an electron-vibration model Hamiltonian in a quantum chemical framework, and explore the extent to which such a Hamiltonian can capture key effects of nonadiabatic dynami…
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…
Polaritonic Coupled-Cluster Theory
Uliana Mordovina, Callum Bungey, Heiko Appel +3
We develop coupled-cluster theory for systems of electrons strongly coupled to photons, providing a promising theoretical tool in polaritonic chemistry with a perspective of applic…
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…
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…
The distinguishable cluster approximation
Daniel Kats, Frederick R. Manby
A new method that accurately describes strongly correlated states and captures dynamical correlation is presented. It is derived as a modification of coupled-cluster theory with si…
Density functional theory embedding for correlated wavefunctions: Improved methods for open-shell systems and transition metal complexes
Jason D. Goodpaster, Taylor A. Barnes, Frederick R. Manby +1
Density functional theory (DFT) embedding provides a formally exact framework for interfacing correlated wave-function theory (WFT) methods with lower-level descriptions of electro…