Computationally Efficient Characterization of Potential Energy Surfaces Based on Fingerprint Distances

TL;DR

This paper introduces a computationally efficient method combining minima hopping and structural distance insights to approximate potential energy surface networks, aiding in understanding properties and guiding detailed transition state calculations.

Contribution

The method provides an efficient way to approximate energy landscapes and connectivity without extensive transition state computations, facilitating initial analysis and pathway finding.

Findings

Enables qualitative analysis of energy landscapes using approximate networks.

Allows identification of promising pathways for detailed transition state studies.

Reduces computational cost compared to traditional methods.

Abstract

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows to understand important characteristics like thermodynamic, dynamic and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This method allows to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows to generate an approximate network of the minima, their connectivity and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.