Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data

Troy D Loeffler,Henry Chan,Subramanian K R S Sankaranarayanan,Mathew Cherukara,Tarak K Patra

doi:10.1021/acs.jpcc.0c00047

Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data

Troy D Loeffler, Henry Chan + Show 3 more

https://doi.org/10.1021/acs.jpcc.0c00047

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Journal: The Journal of Physical Chemistry C	Publication Date: Jan 31, 2020
Citations: 30

Affiliation: Argonne National Laboratory, University of Illinois Urbana-Champaign

#Molecular Dynamics #Dynamical Evolution Of Systems #Potential Energy Landscape #Water Clusters #Functional Forms #Evolution Of Systems #Energy Landscape #Active Learning #Dynamical Evolution #Training Data

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Molecular dynamics with predefined functional forms is a popular technique for understanding dynamical evolution of systems. The predefined functional forms impose limits on the physics that can be...

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Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data