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DTSTAMP:20220812T074334Z
LOCATION:Nairobi Room
DTSTART;TZID=Europe/Stockholm:20220627T160000
DTEND;TZID=Europe/Stockholm:20220627T163000
UID:submissions.pasc-conference.org_PASC22_sess131_msa155@linklings.com
SUMMARY:Neural Networks Learning Quantum Chemistry
DESCRIPTION:Minisymposium\n\nNeural Networks Learning Quantum Chemistry\n\
 nIsayev\n\nThe artificial intelligence (AI) methods that focus on the use 
 of large and diverse data sets in training new atomistic potentials, have 
 consistently proven to be universally applicable to systems containing the
  atomic species in the training set. In this talk, we will present a fully
  transferable deep learning potential that is applicable to complex and di
 verse molecular systems well beyond the training data. Focusing on paramet
 rization for organic molecules (with CHNOSFCl atoms so far), we have devel
 oped a universal neural network potential that is <em>highly accurate comp
 ared to reference QM calculations at speeds 107 faster.</em> The potential
  is shown to accurately represent the underlying physical chemistry of mol
 ecules through various test cases including: <em>chemical reactions (both 
 thermodynamics and kinetics), thermochemistry, structural optimization, an
 d molecular dynamics simulations</em>. The results presented in this talk 
 will provide evidence of the broad applicability of deep learning to vario
 us chemistry problems involving organic molecules.\n\nDomain: Chemistry an
 d Materials, Life Sciences, Physics
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