Constant-pressure nested sampling with atomistic dynamics

TL;DR

This paper improves the nested sampling algorithm for phase diagram calculations by introducing all-particle Monte Carlo moves, significantly reducing computational cost for systems with complex interactions.

Contribution

It introduces all-particle Monte Carlo moves, such as Galilean and enthalpy Hamiltonian Monte Carlo, into nested sampling for more efficient phase diagram computation.

Findings

Enables phase transition temperature determination with similar accuracy at 1/N cost.

Reduces computational cost for systems with complex interactions.

Maintains accuracy comparable to previous methods.

Abstract

The nested sampling algorithm has been shown to be a general method for calculating the pressure-temperature-composition phase diagrams of materials. While the previous implementation used single-particle Monte Carlo moves, these are inefficient for condensed systems with general interactions where single-particle moves cannot be evaluated faster than the energy of the whole system. Here we enhance the method by using all-particle moves: either Galilean Monte Carlo or a total enthalpy Hamiltonian Monte Carlo algorithm, introduced in this paper. We show that these algorithms enable the determination of phase transition temperatures with equivalent accuracy to the previous method at $1/N$ of the cost for an $N$-particle system with general interactions, or at equal cost when single particle moves can be done in $1/N$ of the cost of a full $N$-particle energy evaluation.