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20132026
most citedANI-1: An extensible neural network potential with DFT accuracy at force field computational cost

2.1k citations · 3.9k across the 15 of their papers we have counts for

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2 papers · 1 filter

physics.app-ph2023★ 1 cited

Machine learning potentials with Iterative Boltzmann Inversion: training to experiment

Sakib Matin, Alice Allen, Justin S. Smith +7

Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different c…

physics.chem-ph2023★ 5 cited

Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning

Alice E. A. Allen, Nicholas Lubbers, Sakib Matin +4

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditio…