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Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%
Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin +1
General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of million structures, assuming transferability requires such scale. We test…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Thermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag(111)
Reinhard J. Maurer, Wei Liu, Igor Poltavsky +4
We investigate the thermal and electronic collective fluctuations that contribute to the finite-temperature adsorption properties of flexible adsorbates on surfaces on the example…