OrbNet-Equi: Informing geometric deep learning with electronic interactions to accelerate quantum chemistry
Zhuoran Qiao(Io Therapeutics (United States)), Thomas F. Miller(Bayer (United States)), Animashree Anandkumar(University of California, Irvine), Anders S. Christensen(Fate Therapeutics (United States)), Frederick R. Manby(University of Bristol), Matthew Welborn(Ensco (United States))
Cited by 1
Related Papers
Systematic Computational and Experimental Investigation of Lithium-Ion Transport Mechanisms in Polyester-Based Polymer Electrolytes
|ACS Central Science|2015|223
Multi-modal molecule structure–text model for text-based retrieval and editing
|Nature Machine Intelligence|2023|156
State-specific protein–ligand complex structure prediction with a multiscale deep generative model
|Nature Machine Intelligence|2024|141
#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol
|The International Journal of High Performance Computing Applications|2022|97
Informing geometric deep learning with electronic interactions to accelerate quantum chemistry
|Proceedings of the National Academy of Sciences|2022|87