Peer-Reviewed Publication
Nat Commun2024;15(1):8865.October 14, 2024Journal Article
Analytical ab initio hessian from a deep learning potential for transition state optimization.
Eric C-Y Yuan1,2, Anup Kumar3, Xingyi Guan1,2, Eric D Hermes4, Andrew S Rosen5,6, Judit Zádor7, Teresa Head-Gordon8,9,10, Samuel M Blau11
1Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, CA, USA.
2Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
3Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
4Quantum-Si, Branford, CT, USA.
5Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
6Department of Materials Science and Engineering, University of California, Berkeley, CA, USA.
7Combustion Research Facility, Sandia National Laboratories, Livermore, CA, USA.
8Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, CA, USA. thg@berkeley.edu.
9Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. thg@berkeley.edu.
10Departments of Bioengineering and Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA. thg@berkeley.edu.
11Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. smblau@lbl.gov.
Abstract
Identifying transition states-saddle points on the potential energy surface connecting reactant and product minima-is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational…
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