Acceleration of first-principles atomistic simulations with Bayesian neural networks
Year: 2025
Type: article
Abstract: Molecular dynamics simulations with first-principles methods, such as density functional theory, are a cornerstone in the development of new battery and fuel cell materials. However, due to their high computational demand, their application is mostly limited to small systems and short time horizons. AI-based methods are a promising approach for accelerating first-principles simulations while maintaining high simulation accuracy. A key challenge, however, is the efficient training of such AI-based methods for specific systems of interest. In this article, we provide an overview of the training approach being researched at the Professorship of Computer Science in Mechanical Engineering at Helmut-Schmidt University, Hamburg.
Source: OpenHSU
DOI: 10.24405/17156
Authors: Rensmeyer, Tim, Niggemann, Oliver
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6926560985
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