activity
20142026
most citedANI-1: An extensible neural network potential with DFT accuracy at force field computational cost

2.1k citations · 6k across the 29 of their papers we have counts for

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physics.comp-ph2019★ 1 cited

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

Marco Fronzi, Mutaz Abu Ghazaleh, Olexandr Isayev +3

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a…

physics.comp-ph2018★ 872 cited

Less is more: sampling chemical space with active learning

Justin S. Smith, Ben Nebgen, Nicholas Lubbers +2

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train suc…