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SUMMARY:Machine Learning for Intermolecular Interactions - Professor David
  Sherrill\, Georgia Tech
DTSTART:20210512T140000Z
DTEND:20210512T150000Z
UID:TALK154399@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Machine learning promises high-quality predictions at tremendo
 usly reduced computational cost compared to standard quantum chemistry met
 hods.  Several machine learning models for single-molecule properties have
  already demonstrated considerable success.  However\, standard approaches
  to machine learning in chemistry are not well suited to modeling intermol
 ecular interactions\, which govern protein-ligand binding\, biomolecular s
 tructure\, and properties of condensed phases.  This talk will explain the
  challenges for applying machine learning to intermolecular interactions\,
  and our approaches to overcome them.  We have examined pure machine learn
 ing methods\, physics-based models whose parameterization has been acceler
 ated by machine learning\, and combinations of the two. The speed and accu
 racy of the resulting methods will be illustrated for protein-ligand inter
 actions.\n\n[1] Approaches for Machine Learning Intermolecular Interaction
  Energies and Application to Energy Components From Symmetry Adapted Pertu
 rbation Theory\, D. P. Metcalf\, A. Koutsoukas\, S. A. Spronk\, B. L. Clau
 s\, D. A. Loughney\, S. R. Johnson\, D. L. Cheney\, and C. D. Sherrill\, J
 . Chem. Phys. 152\, 074103 (2020) (doi: 10.1063/1.5142636)\n[2] AP-Net: An
  Atomic-Pairwise Neural Network for Smooth and Transferable Interaction Po
 tentials\, Z. L. Glick\, D. P. Metcalf\, A. Koutsoukas\, S. A. Spronk\, D.
  L. Cheney\, and C. D. Sherrill\, J. Chem. Phys. 153\, 044112 (2020) (doi:
  10.1063/5.0011521)\n[3] Electron-Passing Neural Networks for Atomic Charg
 e Prediction in Systems with Arbitrary Molecular Charge\, D. P. Metcalf\, 
 A. Jiang\, S. A. Spronk\, D. L. Cheney\, and C. D. Sherrill\, J. Chem. Inf
 . Model. 61\, 115 (2021) (doi: 10.1021/acs.jcim.0c01071)\n
LOCATION:Zoom: Meeting ID: 936 2232 6567 Passcode: 659838
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