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

TL;DR

This paper introduces an automated active-learning approach using replica-exchange nested sampling to efficiently generate training data and accurately predict phase diagrams from first principles for materials like silicon, germanium, and titanium.

Contribution

It presents a novel fully automated active-learning framework combining RENS with machine-learning potentials for comprehensive phase diagram prediction.

Findings

AL converges within 10-15 iterations for all systems

Generated potentials accurately reproduce known phase transitions

Framework is general and autonomous for broad thermodynamic conditions

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.