Neural-Network Force Field Backed Nested Sampling: Study of the Silicon p-T Phase Diagram

• Main Authors: Unglert, N, Carrete, J, Pártay, L. B, Madsen, G. K. H
• Format: Journal Article
• Language: English
• Published: 22-08-2023
• Subjects: Physics - Materials Science; Physics - Statistical Mechanics
• DOI: 10.48550/arxiv.2308.11426

Nested sampling is a promising method for calculating phase diagrams of
materials, however, the computational cost limits its applicability if
ab-initio accuracy is required. In the present work, we report on the efficient
use of a neural-network force field in conjunction with the nested-sampling
algorithm. We train our force fields on a recently reported database of silicon
structures and demonstrate our approach on the low-pressure region of the
silicon pressure-temperature phase diagram between 0 and \SI{16}{GPa}. The
simulated phase diagram shows a good agreement with experimental results,
closely reproducing the melting line. Furthermore, all of the experimentally
stable structures within the investigated pressure range are also observed in
our simulations. We point out the importance of the choice of
exchange-correlation functional for the training data and show how the meta-GGA
r2SCAN plays a pivotal role in achieving accurate thermodynamic behaviour using
nested-sampling. We furthermore perform a detailed analysis of the exploration
of the potential energy surface and highlight the critical role of a diverse
training data set.