InstaDeep's mlip library ports MACE, NequIP, and ViSNet to JAX with a JAX-MD backend, ships SPICE2-trained organics models, reports faster MD steps than its own Torch routes, and proposes a faster gated MACE variant in Appendix C.
Gaussian Approximation Potentials: a brief tutorial introduction
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We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian Approximation Potentials (GAP) framework, discussing a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use.
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Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
InstaDeep's mlip library ports MACE, NequIP, and ViSNet to JAX with a JAX-MD backend, ships SPICE2-trained organics models, reports faster MD steps than its own Torch routes, and proposes a faster gated MACE variant in Appendix C.