Cover Feature: Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches (ChemPhysChem 4/2026)

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Authors: Rohit R. Goswami, Hannes Jónsson
Cited by: 0

Abstract

The Cover Feature illustrates the use of local surrogate energy surfaces for saddle searches, with rabbits and carrots indicating the progress of the algorithm and the samples taken. Pink contours show the fast Gaussian process surrogate model, while the violet surface signifies the computationally expensive true potential energy surface. Thehighernumberof samples on the surrogate translates to computational efficiency in the number of samples and total time. The inset highlights the second major innovation of the optimal transport Gaussian process, described in the Research Article by R. Goswami and H. Jónsson (DOI: 10.1002/cphc.202500730): how the distance between two configurations is computed in terms of solving a separate Earth mover's distance for each element.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced Chemical Physics Studies Gaussian Processes and Bayesian Inference

Publication details

Journal / source
ChemPhysChem
Volume
27
Issue
4
Publication date
25 February 2026
Language
EN