marcel.science / eqt
Probing the effects of broken symmetries in machine learning

Hello! 👋 This website is a supplement to our paper (see below) investigating the impacts of breaking rotational symmetry in machine-learning interatomic potentials.

Paper

Title: Probing the effects of broken symmetries in machine learning
Authors: Marcel F. Langer, Sergey N. Pozdnyakov, and Michele Ceriotti
Published: Mach. Learn.: Sci. Technol. 5 04LT01 (2024)
DOI: 10.1088/2632-2153/ad86a0
Preprint: arxiv:2406.17747 (2024)

Please contact Michele for questions about the paper. You can also say hi on twitter: 🐦 marceldotsci, 🐦 spozdn, and 🐦 MicheleCeriotti.

Results & Data

eqt-archive: Supporting data repository for the paper, containing the used PET model, input files for production runs, post-processing scripts, resulting data, and plotting scripts.