# On-the-fly machine learning force field generation: Application to melting points

## TL;DR

This paper introduces an on-the-fly machine learning force field method that efficiently generates force fields during molecular dynamics, significantly reducing computational costs while accurately predicting melting points of various materials.

## Contribution

The paper presents a novel Bayesian inference-based on-the-fly machine learning approach integrated into electronic-structure calculations for rapid, accurate force field generation during simulations.

## Key findings

- Over 99% of first principles calculations are bypassed.
- Simulations are accelerated by a factor of 1000.
- The method accurately reproduces first principles melting points.

## Abstract

An efficient and robust on-the-fly machine learning force field method is developed and integrated into an electronic-structure code. This method realizes automatic generation of machine learning force fields on the basis of Bayesian inference during molecular dynamics simulations, where the first principles calculations are only executed, when new configurations out of already sampled datasets appear. The developed method is applied to the calculation of melting points of Al, Si, Ge, Sn and MgO. The applications indicate that more than 99 \% of the first principles calculations are bypassed during the force field generation. This allows the machine to quickly construct first principles datasets over wide phase spaces. Furthermore, with the help of the generated machine learning force fields, simulations are accelerated by a factor of thousand compared with first principles calculations. Accuracies of the melting points calculated by the force fields are examined by thermodynamic perturbation theory, and the examination indicates that the machine learning force fields can quantitatively reproduce the first principles melting points.