This article presents a novel framework for developing machine learning potentials (MLPs) in computational materials science, leveraging the pyiron integrated development environment. This system automates the key steps in MLP development: creating systematic databases from Density Functional Theory (DFT) calculations, fitting interatomic potentials, and validating them for a variety of material properties. The approach integrates diverse methodologies, including the Embedded Atom Method (EAM), High-Dimensional Neural Network Potentials (HDNNP), and Atomic Cluster Expansion (ACE). A practical application highlighted is the computation of a phase diagram for the Al-Li alloy, demonstrating the framework’s capacity for large-scale and accurate simulations. By streamlining workflows and emphasizing reproducibility, this platform aims to accelerate innovations in materials science and enhance the accessibility of MLP tools for researchers.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41524-024-01441-0
In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests, including the possibility to discuss about any potential interest in the Materials Square cloud-based platform ( www.matsq.com ), designed for streamlining the execution of materials and molecular atomistic simulations!
Best regards,
Dr. Gabriele Mogni
Technical Consultant and EU Representative
Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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