Molecular dynamics-driven global tetra-atomic potential energy surfaces: Application to the AlF dimer

TL;DR

This paper introduces a machine learning method to efficiently generate full-dimensional potential energy surfaces for tetra-atomic systems, demonstrated on the AlF dimer, reducing ab initio calculations significantly.

Contribution

A novel active learning approach for constructing tetra-atomic potential energy surfaces that minimizes ab initio calculations and adapts to system-specific accuracy needs.

Findings

Successfully generated AlF-AlF potential energy surface with less than 0.1% ab initio points

Identified key differences in properties compared to CaF and bi-alkali dimers

Method does not require long-range information and is fully general

Abstract

In this work, we present a general machine learning approach for full-dimensional potential energy surfaces for tetra-atomic systems. Our method employs an active learning scheme trained on {\it ab initio} points, which size grows based on the accuracy required. The training points are selected based on molecular dynamics simulations, choosing the most suitable configurations for different collision energy and mapping the most relevant part of the potential energy landscape of the system. The present approach does not require long-range information and is entirely general. As an example, we provide the full-dimensional AlF-AlF potential energy surface, requiring $\lesssim 0.1\%$ of the configurations to be calculated {\it ab initio}. Furthermore, we analyze the general properties of the AlF-AlF system, finding key difference with other reported results on CaF or bi-alkali dimers.