Replica Exchange Nested Sampling.

Nested sampling (NS) has emerged as a powerful tool for exploring thermodynamic properties in materials science. However, its efficiency is often hindered by the limitations of Markov chain Monte Carlo (MCMC) sampling. In strongly multimodal landscapes, MCMC struggles to traverse energy barriers, leading to biased sampling and reduced accuracy.

Surface phase diagrams from nested sampling.

Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite temperatures. The Bayesian-inference-based nested sampling (NS) algorithm allows for modeling phase equilibria at arbitrary temperatures by directly and efficiently calculating the partition function, whose relationship with free energy is well known. This work extends NS to calculate adsorbate phase diagrams, incorporating all relevant configurational contributions to the free energy.

A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles.

A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost.

Stability of the high-density Jagla liquid in 2D: sensitivity to parameterisation.

We computed the pressure-temperature phase diagram of the hard-core two-scale ramp potential in two-dimensions, with the parameterisation originally suggested by Jagla [E. A. Jagla, , , 061501 (2001)], as well as with a series of systematically modified variants of the model to reveal the sensitivity of the stability of phases.