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Thermodynamic properties by on-the-fly machine-learned potentials within and beyond DFT

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Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. In this talk I will discuss a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference, and I will show how it can be employed in order to generate accurate force fields that are capable to predict thermodynamic properties. For the paradigmatic example of zirconia, an important transition metal oxide, I will show that our machine-learned potential correctly captures the temperature-induced phase transitions below the melting point. It can also be used to calculate the heat transport on the basis of Green-Kubo theory, accounting for anharmonic effects to all orders. In addition, I will introduce a ∆-machine learning approach that allows to train interatomic potentials from beyond-density functional theory calculations at an affordable computational cost. The results demonstrate that these techniques enable many-body calculations of finite-temperature properties of materials.

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

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