Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions
Rohit Goswami (1), Maxim Masterov (2), Satish Kamath (2), Alejandro Pe\~na-Torres (1), Hannes J\'onsson (1) ((1) Science Institute, Faculty of Physical Sciences, University of Iceland, Reykjav\'ik, Iceland, (2) SURF, Amsterdam, The Netherlands)

TL;DR
This paper presents an efficient Gaussian process regression (GPR) accelerated method for locating saddle points in high-dimensional energy surfaces, significantly reducing electronic structure calculations needed for molecular reaction analysis.
Contribution
The authors develop and implement a GPR-accelerated saddle point search method that outperforms traditional approaches in efficiency, applicable to complex molecular reactions.
Findings
Achieves an order of magnitude reduction in energy evaluations.
Requires similar calculations as internal coordinate methods despite Cartesian coordinates.
Reduces wall time in most cases at low Hartree-Fock level.
Abstract
The task of locating first order saddle points on high-dimensional surfaces describing the variation of energy as a function of atomic coordinates is an essential step for identifying the mechanism and estimating the rate of thermally activated events within the harmonic approximation of transition state theory. When combined directly with electronic structure calculations, the number of energy and atomic force evaluations needed for convergence is a primary issue. Here, we describe an efficient implementation of Gaussian process regression (GPR) acceleration of the minimum mode following method where a dimer is used to estimate the lowest eigenmode of the Hessian. A surrogate energy surface is constructed and updated after each electronic structure calculation. The method is applied to a test set of 500 molecular reactions previously generated by Hermez and coworkers [J. Chem. Theory…
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Taxonomy
MethodsGaussian Process · Sparse Evolutionary Training
