A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules. - Lixue Cheng; Matthew Welborn; Thomas F. Miller III (CoRR, 2019)

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning. - Lixue Cheng; Nikola B. Kovachki; Matthew Welborn; Thomas F. Miller III (CoRR, 2019)

Dimension-free path-integral molecular dynamics without preconditioning. - Roman Korol; Nawaf Bou-Rabee; Thomas F. Miller III (CoRR, 2019)

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features. - Zhuoran Qiao; Matthew Welborn; Animashree Anandkumar; Frederick R. Manby; Thomas F. Miller III (CoRR, 2020)

A generalized class of strongly stable and dimension-free T-RPMD integrators. - Jorge L. Rosa-Raíces; Jiace Sun; Nawaf Bou-Rabee; Thomas F. Miller III (CoRR, 2020)

Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces. - Zhuoran Qiao; Feizhi Ding; Matthew Welborn; Peter J. Bygrave; Daniel G. A. Smith; Animashree Anandkumar; Frederick R. Manby; Thomas F. Miller III (CoRR, 2020)

UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry. - Zhuoran Qiao; Anders S. Christensen; Frederick R. Manby; Matthew Welborn; Anima Anandkumar; Thomas F. Miller III (CoRR, 2021)

Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process. - Jiace Sun; Lixue Cheng; Thomas F. Miller III (CoRR, 2021)

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space. - Lixue Cheng; Jiace Sun; Thomas F. Miller III (CoRR, 2022)

Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning. - Jiace Sun; Lixue Cheng; Thomas F. Miller III (CoRR, 2022)

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression. - Lixue Cheng; Jiace Sun; J. Emiliano Deustua; Vignesh C. Bhethanabotla; Thomas F. Miller III (CoRR, 2022)

Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models. - Zhuoran Qiao; Weili Nie; Arash Vahdat; Thomas F. Miller III; Anima Anandkumar (CoRR, 2022)

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. - Abigail C. Dommer; Lorenzo Casalino; Fiona L. Kearns; Mia A. Rosenfeld; Nicholas Wauer; Surl-Hee Ahn; John Russo 003; A. Sofia F. Oliveira; Clare Morris; Anthony T. Bogetti; Anda Trifan; Alexander Brace; et al. (Int. J. High Perform. Comput. Appl., 2023)

NeuralPLexer evaluation datasets and predictions. - Zhuoran Qiao; Weili Nie; Arash Vahdat; Thomas F. Miller III; Animashree Anandkumar (Zenodo, 2023)

NeuralPLexer evaluation datasets and predictions. - Zhuoran Qiao; Weili Nie; Arash Vahdat; Thomas F. Miller III; Animashree Anandkumar (Zenodo, 2023)

NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models. - Zhuoran Qiao; Feizhi Ding; Thomas Dresselhaus; Mia A. Rosenfeld; Xiaotian Han; Owen Howell; Aniketh Iyengar; Stephen Opalenski; Anders S. Christensen; Sai K. Sirumalla; Frederick R. Manby; Thomas F. Miller III; et al. (CoRR, 2024)

State-specific protein-ligand complex structure prediction with a multiscale deep generative model. - Zhuoran Qiao; Weili Nie; Arash Vahdat; Thomas F. Miller III; Animashree Anandkumar (Nat. Mac. Intell., 2024)