Molecular Simulation with Machine Learning

DataX: Molecular Simulation with Machine Learning

A two-day virtual workshop covering theory and hands-on tutorials on the software package for molecular simulation with machine learning (ML) tools developed at the Computational Chemical Science Center “Chemistry in Solution and at Interfaces” (  The package includes codes to construct and use deep neural network models of the potential energy surface and electronic properties of multi-atomic systems that reproduce the results of electronic density functional theory. ML codes need interfacing with community codes for electronic structure and ab-initio simulation, classical molecular dynamics, path-integral molecular dynamics, and enhanced sampling of rare events. The workshop will promote discussions on how to better achieve code integration within the molecular simulation community across disciplines ranging from physical chemistry to condensed matter physics and materials science. The workshop will include general presentations, panel discussions, and tutorial sessions. The tutorial sessions, held in the afternoons, will use cloud computing resources. 

Registration is free.   A registration form, available on the workshop webpage at  should be completed by the deadline of  June 26, 2020.  Due to limits in the available resources, at most 50 registered participants will be admitted to the tutorial sessions. Information on admissions and use of cloud computer resources will be provided by July 5, 2020.  

If you already registered previously, you do not have to register again. I will reach out to those individuals separately to inquire which sessions they will be participating - lectures, tutorials or both.



We gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation and our Princeton University partners:

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