README.md

Evaluation of foundational machine learning interatomic potentials for halide perovskites

References:

1. Size dependent solid-solid crystallization of halide perovskites. arXiv preprint arXiv:2404.05644 (2024)
2. https://e3nn.org
3. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun 13, 2453 (2022)
4. Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations. arXiv preprint arXiv:2306.12059 (2023)
5. A foundation model for atomistic materials chemistry. arXiv preprint arXiv:2401.00096 (2023)
6. DeePMD-kit v2: A software package for Deep Potential models. J. Chem. Phys., 159, 054801 (2023)
7. Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing. Phys. Rev. X 14, 021036 (2024)
8. Mattersim: A deep learning atomistic model across elements, temperatures and pressures. arXiv preprint arXiv:2405.04967 (2024)
9. Orb: A fast, scalable neural network potential. arXiv preprint arXiv:2410.22570 (2024)
10. Open materials 2024 (omat24) inorganic materials dataset and models. arXiv preprint arXiv:2410.12771 (2024)
11. Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations. Journal of Chemical Theory and Computation (2024)

Acknowledgement

• Swiss National Science Foundation through post-doc mobility Fellowship No. P500PN_206693
• Kelvin2: Northern Ireland High Performance Computing (NI-HPC) service funded by EPSRC