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High-throughput computational thermodynamics: a unique insight into the potential energy surface

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During the past decade, we have adapted the Bayesian statistical approach, nested sampling, for exploring the potential energy surface of atomistic systems. Nested sampling’s primary advantage lies in its automatic sampling of thermodynamically relevant configurations across the entire configuration space, from gas phase to crystalline solid structures, in proportion to their phase space volume. This enables the calculation of the partition function at any temperature, facilitating the evaluation of key thermodynamic properties such as free energy and heat capacity. Nested sampling has proven particularly effective for sampling phase transitions, allowing us to calculate pressure-temperature phase diagrams for various materials and model systems. This unbiased and predictive sampling approach has revealed surprising properties and new structures in several systems, enhancing our understanding of interatomic potential models and guiding improvements, including those in machine-learned potentials. The talk describes the main features of the nested sampling method and highlights a few such example applications.

This talk is part of the Lennard-Jones Centre series.

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