Publication date: 10th April 2024
Recently, message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, have attracted significant attention due to their data efficiency and high accuracy. Moreover, the atomic embedding vector in GNN-IPs allows for training over large datasets with diverse chemistry, resulting in pretrained, general-purpose machine learning force fields. In this presentation, we introduce the development of an efficient parallelization scheme compatible with GNN-IPs and its implementation into a package named SevenNet (Scalable EquiVariance-Enabled Neural NETwork), which is based on the NequIP architecture. Through benchmark tests on a 32-GPU cluster with examples of SiO2, SevenNet achieves over 80% parallel efficiency in weak-scaling scenarios and exhibits nearly ideal strong-scaling performance as long as GPUs are fully utilized. We then pre-train SevenNet with a vast dataset from the Materials Project (dubbed ‘SevenNet-0’) and demonstrate its out-of-distribution generalization capabilities. We subsequently apply SevenNet-0 to investigate electrolytes in lithium batteries, such as screening organic molecules in liquid electrolytes and identifying new solid-state electrolytes.
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