A DFT-driven multifidelity framework for constructing efficient energy models for atomic-scale simulations

Research output: Contribution to journalArticlepeer-review

Authors

Institutes & Expert groups

  • Università della Svizzera italiana
  • CEA Saclay - Commissariat à l'énergie atomique
  • EDF – R&D - MMC
  • KTH - Royal Institute of Technology

Documents & links

Abstract

The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of migration barriers in atomistic Kinetic Monte Carlo simulations. Accurate (high-fidelity) methods are often available, but since they are usually computationally expensive, they must be replaced by less accurate (low-fidelity) models that introduce some degrees of approximation. Machine-learning techniques such as artificial neural networks can be employed to work around
this limitation and extract the needed parameters from large databases of high-fidelity data. However, the latter are often computationally expensive to produce. This work introduces an alternative method based on the multifidelity approach. Correlations between high-fidelity and low-fidelity predictions are exploited to make an educated guess of the high-fidelity value based only on quick low-fidelity estimations, to be used for instance as an efficient and reliable source of physical data for atomistic simulations. With respect to neural networks, this
approach requires less training data because of the lower amount of fitting parameters involved. The method is tested on the prediction of ab initio formation and migration energies of vacancy diffusion in iron-copper alloys,
and compared with the neural networks trained on the same database.

Details

Original languageEnglish
Pages (from-to)15-21
Number of pages7
JournalNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
Volume483
DOIs
Publication statusPublished - 15 Nov 2020

Keywords

  • Machine Learning, Kinetic Monte Carlo, Atomistic simulation

ID: 7000565