arXiv subjects

Miha Gunde

Publications and source records attributed to Miha Gunde.

3 recordsLinked to original sources

arXiv2026

Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment

Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that integrates the CI-NEB with the MMF method so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a reduction in energy and force calculations of 57% [95% CrI: -64%, -50%] relative to CI-NEB for the BC set, while a 31% mean reduction is found for the transitions of the heptamer island. These results establish this hybrid method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

physics.chem-ph↗

arXiv2024

SOFI: Finding point group symmetries in atomic clusters as finding the set of degenerate solutions in a shape-matching problem

Point Group (PG) symmetries play a fundamental role in many aspects of theoretical chemistry and computational materials science. With the objective to automatize the search of PG symmetry operations of generic atomic clusters, we present a new algorithm called Symmetry Operation FInder (SOFI). SOFI addresses the problem of identifying PG symmetry by framing it as a degenerate shape-matching problem, where the multiple solutions correspond to distinct symmetry operations. The developed algorithm is compared against three other algorithms dedicated to PG identification, on a large set of atomic clusters. The results, along with some illustrative use cases, showcase the effectiveness of SOFI. The SOFI algorithm is released as part of the IRA library, accessible at https://github.com/mammasmias/IterativeRotationsAssignments.

physics.comp-ph↗

arXiv2021

IRA: A shape matching approach for recognition and comparison of generic atomic patterns

We propose a versatile, parameter-less approach for solving the shape matching problem, specifically in the context of atomic structures when atomic assignments are not known a priori. The algorithm Iteratively suggests Rotated atom-centered reference frames and Assignments (Iterative Rotations and Assignments, IRA). The frame for which a permutationally invariant set-set distance, namely the Hausdorff distance, returns minimal value is chosen as the solution of the matching problem. IRA is able to find rigid rotations, reflections, translations, and permutations between structures with different numbers of atoms, for any atomic arrangement and pattern, periodic or not. When distortions are present between the structures, optimal rotation and translation are found by further applying a standard Singular Value Decomposition-based method. To compute the atomic assignments under the one-to-one assignment constraint, we develop our own algorithm, Constrained Shortest Distance Assignments (CShDA). The overall approach is extensively tested on several structures, including distorted structural fragments. Efficiency of the proposed algorithm is shown as a benchmark comparison against two other shape matching algorithms. We discuss the use of our approach for the identification and comparison of structures and structural fragments through two examples: a replica exchange trajectory of a cyanine molecule, in which we show how our approach could aid the exploration of relevant collective coordinates for clustering the data; and an SiO$_2$ amorphous model, in which we compute distortion scores and compare them with a classical strain-based potential. The source code and benchmark data are available at \url{https://github.com/mammasmias/IterativeRotationsAssignments}.

physics.comp-ph↗