Rohit Goswami^1*, Maxim Masterov², Satish Kamath², Alejandro Peña-Torres¹, Hannes Jónsson^1*

1 Science Institute and Faculty of Physical Sciences, University of Iceland, Reykjavík, Iceland

2 SURF, Amsterdam, The Netherlands

* Corresponding authors emails: rgoswami@ieee.org, hj@hi.is

DOI10.24435/materialscloud:j6-zz [version v1]

Publication date: May 26, 2025

How to cite this record

Rohit Goswami, Maxim Masterov, Satish Kamath, Alejandro Peña-Torres, Hannes Jónsson, Efficient implementation of gaussian process regression accelerated saddle point searches with application to molecular reactions, Materials Cloud Archive 2025.87 (2025), https://doi.org/10.24435/materialscloud:j6-zz

Description

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 Comput. 18, 6974 (2022)]. An order of magnitude reduction in the number of electronic structure calculations needed to reach the saddle point configurations is obtained by using the GPR compared to the dimer method. Despite the wide range in stiffness of the molecular degrees of freedom, the calculations are carried out using Cartesian coordinates and are found to require similar number of electronic structure calculations as an elaborate internal coordinate method implemented in the Sella software package. The present implementation of the GPR surrogate model in C++ is efficient enough for the wall time of the saddle point searches to be reduced in 3 out of 4 cases even though the calculations are carried out at a low Hartree-Fock level. This record contains the saddle search output logs for Sella and EON (dimer, with and without GPR acceleration). The data also includes full trajectories of the GPR accelerated dimer method for visual analysis. These logs are used to generate the figures in the manuscript. For details, refer to the code in the associated GitHub repository.

Materials Cloud sections using this data

Files

File name	Size	Description
gprd_traj.tar.xz MD5md5:74fa72316b68fa0e20d3e8c643166359	6.9 MiB	GPR Dimer trajectories
sella_runs.tar.xz MD5md5:94db03f1fe646fde95a4160435df1c4c	14.2 MiB	Output logs and trajectories for Sella
eon_runs.tar.xz MD5md5:fdd7864c69db964ea605ecb797df4e5a	264.6 MiB	Output logs for EON Dimer (with and without GPR acceleration)
readme.txt MD5md5:8c8ba602ca63f8880287f2c3ca22a246	2.6 KiB	README describing the directory layout and usage.

License

Files and data are licensed under the terms of the following license: Materials Cloud non-exclusive license to distribute v1.0.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

saddle-search gpr transition-state