Improved Initialization of Optimal Path Calculations Using Sequential Traversal over the Image Dependent Pair Potential Surface
Yorick L. A. Schmerwitz, Vilhj\'almur \'Asgeirsson, Hannes J\'onsson

TL;DR
This paper introduces a sequential traversal method for initializing reaction path calculations on the IDPP surface, reducing unnecessary bond breaking and improving the starting point for minimum energy path computations.
Contribution
The paper presents a new sequential IDPP approach that constructs initial reaction paths more reliably by gradually introducing images, avoiding issues of bond breaking seen in previous linear interpolation methods.
Findings
Sequential IDPP produces more accurate initial paths.
The method requires minimal computational effort.
It improves the reliability of reaction path calculations.
Abstract
In reaction path optimization, such as the calculation of a minimum energy path (MEP) between given reactant and product configurations of atoms, it is advantageous to start with an initial guess where close proximity of atoms is avoided and bonds are not unnecessarily broken only to be reformed later. When the configurations of the atoms are described with Cartesian coordinates, a linear interpolation between the endpoints can be problematic, and a better option is provided by the so-called image dependent pair potential (IDPP) approach where interpolated pairwise distances are generated to form an objective function that can be used to generate an improved initial path. When started with a linear interpolation, this method can, however, still lead to unnecessary bond breaking in, for example, reactions where a molecular subgroup undergoes significant rotation. In the method presented…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Various Chemistry Research Topics · Computational Drug Discovery Methods
