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2020 – today
- 2023
[j2]Abigail C. Dommer, Lorenzo Casalino
, Fiona L. Kearns, Mia A. Rosenfeld, Nicholas Wauer, Surl-Hee Ahn, John Russo, A. Sofia F. Oliveira, Clare Morris, Anthony T. Bogetti, Anda Trifan, Alexander Brace, Terra Sztain, Austin Clyde, Heng Ma, S. Chakra Chennubhotla, Hyungro Lee, Matteo Turilli, Syma Khalid, Teresa Tamayo-Mendoza, Matthew Welborn, Anders S. Christensen, Daniel G. A. Smith, Zhuoran Qiao, Sai K. Sirumalla, Michael O'Connor, Frederick R. Manby, Anima Anandkumar, David J. Hardy, James C. Phillips, Abraham C. Stern, Josh Romero, David Clark, Mitchell Dorrell, Tom Maiden, Lei Huang, John D. McCalpin, Christopher J. Woods, Alan Gray, Matt Williams, Bryan Barker, Harinda Rajapaksha, Richard Pitts, Tom Gibbs, John E. Stone, Daniel M. Zuckerman, Adrian J. Mulholland, Thomas F. Miller III, Shantenu Jha, Arvind Ramanathan, Lillian T. Chong, Rommie E. Amaro:
#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. Int. J. High Perform. Comput. Appl. 37(1): 28-44 (2023)- 2021
- [i5]Zhuoran Qiao, Anders S. Christensen, Frederick R. Manby, Matthew Welborn, Anima Anandkumar, Thomas F. Miller III:
UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry. CoRR abs/2105.14655 (2021) - 2020
- [i4]Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III:
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features. CoRR abs/2007.08026 (2020) - [i3]Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Daniel G. A. Smith, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III:
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces. CoRR abs/2011.02680 (2020)
2010 – 2019
- 2019
- [i2]Lixue Cheng, Matthew Welborn, Thomas F. Miller III:
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules. CoRR abs/1901.03309 (2019) - [i1]Lixue Cheng, Nikola B. Kovachki, Matthew Welborn, Thomas F. Miller III:
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning. CoRR abs/1909.02041 (2019) - 2015
- [j1]Matthew Welborn, Jiahao Chen, Lee-Ping Wang, Troy A. Van Voorhis:
Why many semiempirical molecular orbital theories fail for liquid water and how to fix them. J. Comput. Chem. 36(12): 934-939 (2015)
Coauthor Index
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