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DTSTAMP:20220812T074357Z
LOCATION:Nairobi Room
DTSTART;TZID=Europe/Stockholm:20220627T160000
DTEND;TZID=Europe/Stockholm:20220627T180000
UID:submissions.pasc-conference.org_PASC22_sess131@linklings.com
SUMMARY:MS2F - Advances in Methods and Applications in Computational Chemi
 stry and Materials Science
DESCRIPTION:Minisymposium\n\nThe COVID-19 pandemic put a pause to holding 
 scientific meetings and exchanging scientific ideas. This minisymposium is
  a timely opportunity to catch up with current developments in method and 
 software development related to computational chemistry and materials scie
 nces, as well as novel applications they are being applied to. The minisym
 posium will highlight opportunities for scientific discoveries made possib
 le via the emerging synergies between recent advances in high performance 
 computing software and hardware development, method development, big data 
 science and artificial intelligence/machine learning for computational che
 mistry and materials sciences applications. The workshop will feature thre
 e speakers and a discussion panel comprised of speakers and organizers.\n\
 nHow Much Chemistry do Machine-Learning Models Learn?\n\nRiniker\n\nFrom s
 imple clustering techniques to sophisticated neural networks, the use of m
 achine learning (ML) has become a valuable tool in many fields of chemistr
 y in the past decades. While domain applicability is a common concept when
  assessing ML models in cheminformatics (e.g., predicting biological act..
 .\n\n---------------------\nExascale Simulations via the Submatrix Matrix 
 Method\n\nKühne\n\nWe present the submatrix method and a novel linear-scal
 ing electronic-structure method in conjunction with approximate computing,
  as well as the implementation of the technique in CP2K [1]. Even though i
 nitially proposed for inverse p-th roots [2], it has recently been recogni
 zed that the submatrix ...\n\n---------------------\nNeural Networks Learn
 ing Quantum Chemistry\n\nIsayev\n\nThe artificial intelligence (AI) method
 s that focus on the use of large and diverse data sets in training new ato
 mistic potentials, have consistently proven to be universally applicable t
 o systems containing the atomic species in the training set. In this talk,
  we will present a fully transferable d...\n\n---------------------\nPanel
  Discussion\n\nKobayashi\n\nThis session will be an open discussion of dev
 elopments and future directions in HPC Methods and Applications in Computa
 tional Chemistry and Materials Science led by our panel of speakers.\n\n\n
 Domain: Chemistry and Materials, Life Sciences, Physics
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