OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
Anders S. Christensen(University of Copenhagen), Thomas F. Miller(California Institute of Technology), Animashree Anandkumar, Michael B. O’Connor(Ensco (United States)), Zhuoran Qiao(Discovery Laboratories (United States)), Frederick R. Manby(Ensco (United States)), Matthew Welborn(Ensco (United States)), Peter J. Bygrave(University of Southampton), Sai Krishna Sirumalla(Ensco (United States)), Feizhi Ding, Daniel G. A. Smith(Molecular Sciences Software Institute)
Cited by 7
Related Papers
P <scp>SI4</scp> 1.4: Open-source software for high-throughput quantum chemistry
|The Journal of Chemical Physics|2020|984
Report on the sixth blind test of organic crystal structure prediction methods
|Acta Crystallographica Section B Structural Science Crystal Engineering and Materials|2016|604
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