Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling

TL;DR

This paper introduces an automated method to build accurate machine-learning models for predicting material phase diagrams using efficient sampling techniques.

Contribution

A novel active-learning strategy using replica-exchange nested sampling for autonomous generation of training data and phase diagram prediction.

Findings

The AL process converges in ~10–15 iterations for silicon, germanium, and titanium.

The method reproduces known phase transitions and thermodynamic trends with high accuracy.

Abstract

Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within ~ 10–15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions.

Figures