Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime

TL;DR

This paper introduces a machine-learning based framework for accurate finite-temperature materials modeling, combining electronic property approximations with thermodynamic sampling to predict properties of nickel across a wide temperature range.

Contribution

It presents a novel approach integrating machine learning and statistical sampling to efficiently simulate finite-temperature properties of materials, including quantum effects and electronic entropy.

Findings

Accurately predicts bulk, interfacial, and defect properties of nickel from 100 to 2500 K.

Demonstrates modeling of nuclear quantum fluctuations and electronic entropy effects.

Framework is adaptable to complex alloys and other materials.

Abstract

Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions. Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations, that can therefore be combined with extensive thermodynamic sampling. We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point, and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost. We demonstrate the calculation of a broad array of bulk, interfacial and defect properties over a temperature range from 100 to 2500 K, modeling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy. The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.