Thermodynamic properties by on-the-fly machine-learned potentials within and beyond DFT скачать в хорошем качестве

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

Lennard-Jones Centre discussion group seminar by Dr Carla Verdi from the University of Vienna. 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. This talk discusses a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference, and shows 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, it is shown that the 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, the talk introduces 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. The seminar was held on 28th March 2022. 🖥️ Check out our websites: https://linktr.ee/cumaterials