245 citations · 1k across the 13 of their papers we have counts for
13 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…
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…
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…
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…
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…
Incremental Embedding: A Density Matrix Embedding Scheme for Molecules
Hong-Zhou Ye, Matthew Welborn, Nathan D. Ricke +1
The idea of using fragment embedding to circumvent the high computational scaling of accurate electronic structure methods while retaining high accuracy has been a long-standing go…
-SCF: A Direct Energy-targeting Method To Mean-field Excited States
Hong-Zhou Ye, Matthew Wellborn, Nathan D. Ricke +1
The mean-field solutions of electronic excited states are much less accessible than ground state (e.g.\ Hartree-Fock) solutions. Energy-based optimization methods for excited state…
Accurate densities of states for disordered systems from free probability: Live Free or Diagonalize
Matthew Welborn, Jiahao Chen, Troy Van Voorhis
We investigate how free probability allows us to approximate the density of states in tight binding models of disordered electronic systems. Extending our previous studies of the A…
Error analysis of free probability approximations to the density of states of disordered systems
Jiahao Chen, Eric Hontz, Jeremy Moix +5
Theoretical studies of localization, anomalous diffusion and ergodicity breaking require solving the electronic structure of disordered systems. We use free probability to approxim…