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A practical guide to machine learning interatomic potentials – Status and future

Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin D. Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, KJ Schmidt, So Takamoto, Aidan P. Thompson, Julia Westermayr, Brandon M. Wood

2025Current Opinion in Solid State and Materials Science171 citationsDOIOpen Access PDF

Topics & Concepts

Interatomic potentialComputer scienceData scienceChemistryComputational chemistryMolecular dynamicsMachine Learning in Materials ScienceAdvanced Materials Characterization TechniquesHydrogen embrittlement and corrosion behaviors in metals
A practical guide to machine learning interatomic potentials – Status and future | Litcius