Machine learning interatomic potentials, particularly those based on deep
equivariant neural networks, have demonstrated state-of-the-art accuracy and
computational efficiency in atomistic modeling tasks like molecular dynamics
and high-throughput screening. The size of datasets and demands of downstream
workflows are growing rapidly, making robust and scalable software essential.
This work presents a major overhaul of the NequIP framework focusing on
multi-node parallelism, computational performance, and extensibility. The
redesigned framework supports distributed training on large datasets and
removes barriers preventing full utilization of the PyTorch 2.0 compiler at
train time. We demonstrate this acceleration in a case study by training
Allegro models on the SPICE 2 dataset of organic molecular systems. For
inference, we introduce the first end-to-end infrastructure that uses the
PyTorch Ahead-of-Time Inductor compiler for machine learning interatomic
potentials. Additionally, we implement a custom kernel for the Allegro model’s
most expensive operation, the tensor product. Together, these advancements
speed up molecular dynamics calculations on system sizes of practical relevance
by up to a factor of 18.

Questo articolo esplora i giri e le loro implicazioni.

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