MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
Published in arXiv preprint arXiv:2504.10655, 2025
Citation: Lingyu Kong, Nima Shoghi, Guoxiang Hu, Pan Li, Victor Fung, arXiv preprint arXiv:2504.10655, 2025. https://arxiv.org/abs/2504.10655
Introduces MatterTune, a modular platform that enables fine-tuning of pre-trained atomistic foundation models for materials science applications, allowing researchers to overcome data limitations and seamlessly integrate advanced machine learning into materials discovery workflows.