2021
DOI: 10.48550/arxiv.2107.05109
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Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning

Abstract: Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many stateof-the-art disciplines such as the design of high entropy alloys or multicomponent catalysts. To address this issue, we adapt sampling tools built upon machine-learning based generative modeling to the material… Show more

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