Researchers Accurately Simulate 100 Million Atoms With Machine Learning

Harvard researchers bring the accuracy, sample efficiency, and robustness of deep equivariant neural networks to the simulate 44 million atoms. This is achieved through a combination of innovative model architecture, massive parallelization, and models and implementations optimized for efficient GPU utilization. The resulting Allegro architecture bridges the accuracy speed tradeoff of atomistic simulations and enables ...

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