# Self-learning analytical interatomic potential describing laser-excited silicon

## TL;DR

This paper introduces a self-learning, temperature-dependent interatomic potential for silicon that accurately models laser-excited states and can simulate various boundary conditions, matching ab initio results.

## Contribution

It presents a novel, flexible analytical potential for silicon that automatically adapts through self-learning to reproduce ab initio molecular dynamics under laser excitation.

## Key findings

- Successfully reproduces thermal and nonthermal features of silicon.
- Simulates laser-excited silicon nanoparticles and observes critical damping.
- Captures nonthermal melting phenomena in silicon.

## Abstract

We develop an electronic-temperature dependent interatomic potential $\Phi (T_\text{e})$ for unexcited and laser-excited silicon. The potential is designed to reproduce ab initio molecular dynamics simulations by requiring force- and energy matching for each time step. $\Phi (T_\text{e})$ has a simple and flexible analytical form, can describe all relevant interactions and is applicable for any kind of boundary conditions (bulk, thin films, clusters). Its overall shape is automatically adjusted by a self-learning procedure, which finally finds the global minimum in the parameter space. We show that $\Phi (T_\text{e})$ can reproduce all thermal and nonthermal features provided by ab initio simulations. We apply the potential to simulate laser-excited Si nanoparticles and find critical damping of their breathing modes due to nonthermal melting.