Accelerating Simulated Annealing of Glassy Materials with Data Assimilation

TL;DR

This paper introduces a data assimilation approach using simulated annealing and interatomic potentials to efficiently construct atomic models of glassy materials, overcoming challenges posed by long relaxation times.

Contribution

It presents a novel method combining data assimilation with simulated annealing and interatomic potentials, enabling structure refinement without high Q-range diffraction data.

Findings

The method reproduces experimental data accurately.

It produces more ordered amorphous structures at intermediate range.

It reduces the need for extensive diffraction data.

Abstract

The ultra-long relaxation time of glass transition makes it difficult to construct atomic models of amorphous materials by conventional methods. We propose a novel method for building such atomic models using data assimilation method by simulated annealing with an accurately computed interatomic potential augmented by penalty from experimental data. The advantage of this method is that not only can it reproduce experimental data as the structure refinement methods like reverse Monte Carlo but also obtain the reasonable structure in terms of interatomic potential energy. In addition, thanks to the interatomic potential, we do not need high $Q$ range diffraction data, which is necessary to take into account the short-range order. Persistent homology analysis shows that the amorphous ice obtained by the new method is indeed more ordered at intermediate range.