245 citations · 1.3k across the 17 of their papers we have counts for
18 papers
NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
Zhuoran Qiao, Feizhi Ding, Thomas Dresselhaus +10
Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have…
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…
Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry
Zhuoran Qiao, Anders S. Christensen, Matthew Welborn +3
Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physic…
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…
Structure and efficiency in bacterial photosynthetic light-harvesting
Susannah Bourne Worster, Clement Stross, Felix M. W. C. Vaughan +2
Photosynthetic organisms use networks of chromophores to absorb sunlight and deliver the energy to reaction centres, where charge separation triggers a cascade of chemical steps to…
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…
Energy benchmarks for water clusters and ice structures from an embedded many-body expansion
M. J. Gillan, D. Alfe, P. J. Bygrave +2
We show how an embedded many-body expansion (EMBE) can be used to calculate accurate \emph{ab initio} energies of water clusters and ice structures using wavefunction-based methods…
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…
Machine learning for predictive condensed-phase simulation
Albert P. Bartok, Michael J. Gillan, Frederick R. Manby +1
We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on w…
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…
Assessing the accuracy of quantum Monte Carlo and density functional theory for energetics of small water clusters
M. J. Gillan, F. R. Manby, M. D. Towler +1
We present a detailed study of the energetics of water clusters (HO) with , comparing diffusion Monte Carlo (DMC) and approximate density functional theory (DFT) w…
Tensor factorizations of local second-order Møller Plesset theory
Jun Yang, Yuki Kurashige, Frederick R. Manby +1
Efficient electronic structure methods can be built around efficient tensor representations of the wavefunction. Here we describe a general view of tensor factorization for the com…
Bulk and surface energetics of lithium hydride crystal: benchmarks from quantum Monte Carlo and quantum chemistry
S. J. Binnie, S. J. Nolan, N. D. Drummond +4
We show how accurate benchmark values of the surface formation energy of crystalline lithium hydride can be computed by the complementary techniques of quantum Monte Carlo (QMC) an…