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UID:submissions.pasc-conference.org_PASC23_sess163_msa199@linklings.com
SUMMARY:MiniGAP: A Proxy App for ML Prediction of Molecular Properties
DESCRIPTION:Minisymposium\n\nAlvaro Vazquez-Mayagoitia and Murat Keceli (A
 rgonne National Laboratory)\n\nOne could argue that AI/ML has disrupted ch
 emistry and materials science, as well as other disciplines, in part, due 
 to the synergy of accessible databases, accelerated computing resources, a
 nd community-supported and open codes. Three common elements found in AI/M
 L in chemistry research are data collection, fingerprint selection, and re
 gression models. This presentation will discuss these three elements in th
 e context of HPC environments, particularly exascale technologies. It will
  also include the development of a proxy application for predicting chemic
 al properties and developing machine learning interatomic potentials that 
 reproduce out-of-equilibrium properties with accuracy within experimental 
 errors. Although training for these properties and potentials requires low
 -scale simulations, they can be used later to infer interactions between m
 illions of atoms in petascale computers. Acknowledgement: This research us
 ed resources of the Argonne Leadership Computing Facility, which is a DOE 
 Office of Science User Facility supported under Contract DE-AC02-06CH11357
 . This research was supported by the Exascale Computing Project (17-SC-20-
 SC), a collaborative effort of the U.S. Department of Energy Office of Sci
 ence and the National Nuclear Security Administration.\n\nDomain: Computer
  Science, Machine Learning, and Applied Mathematics 
\n\nSession Chairs: R
 iccardo Balin (Argonne National Laboratory) and Ramesh Balakrishnan (Argon
 ne National Laboratory)
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