Published December 21, 2023
| Version v1
Dataset
Open
Code and data for "Machine-learning-boosted ab-initio study of the thermal conductivity of Janus PtSTe van der Waals heterostructures"
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1.
Instituto de Nanociencia y Materiales de Aragón
- 2. Technische Universität Wien
Description
Code and data for Machine-learning-boosted ab-initio study of the thermal conductivity of Janus PtSTe van der Waals heterostructures
Contents:
- neuralil.tar.xz: version used in the manuscript of the force-field code described in the articles A Differentiable Neural-Network Force Field for Ionic Liquids and Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. General-purpose releases can be found here.
- DFT_data.tar.xz: first-principles data created for training and validating the force field, stored as ASE databases in JSON format.
- model_params_plain_ensemble_DEEP_413E12A9.pkl: saved parameters of the fully trained force field.
- 0001-Use-equipartition-occupancies.patch: patch for Phono3py to use classical (equipartition) occupations instead of Bose-Einstein values.