CECAM discussion meeting “Coarse-graining with Machine Learning in molecular dynamics”

Tuesday, December 4 – Thursday, December 6, 2018
Location: Sanofi Campus Gentilly (82 Avenue Raspail, 94250 Gentilly, RER B “Gentilly”) — see the instructions to reach the place (note that you will need a valid ID to enter the site).

Atomistic systems offer a very precise representation of matter – too precise in fact. Simplified descriptions of matter based on a coarse-grained representation are very helpful to understand the physical properties of the systems under consideration. Such a coarse-grained description can be based on reaction coordinates for biochemical systems, where the conformational changes of a complex molecule should be summarized by a few key functions of the atomic positions; or by atomic descriptors in condensed matter physics to summarize the key features of atomic configurations in order to predict forces and energies. Proposing and constructing reaction coordinates has largely relied on empirical approaches and chemical intuition in the past. The situation is less true for atomic descriptors, for which systematic approaches have been considered.

The aim of the discussion meeting is to bring together a diverse audience of participants from various fields (chemistry, drug design, condensed matter physics, materials science, mathematics) to exchange about state-of-the-art techniques for automatically building coarse-grained information on molecular systems. In particular, we believe that the viewpoint and experience of condensed matter physicists in devising atomic descriptors could prove useful in making the construction of reaction coordinates more systematic. Mathematics offer, in this framework, a common language for the discussion. One distinctive feature of this event is that the emphasis would be on the technical details of the underlying numerical methods.

Organizers: Paraskevi Gkeka (Sanofi), Tony Lelièvre (Ecole des Ponts), Pierre Monmarché (Sorbonne-Université), Gabriel Stoltz (Ecole des Ponts)

Speakers and titles/abstracts:

  • Marino Arroyo (UPC-Barcelona Tech), Data-driven modeling of molecular systems using nonlinear dimensionality reduction (abstract)
  • Panos Angelikopoulos (DE Shaw Team)
  • Amir Barati (Carnegie Mellon), Conditional Generative Adversarial Deep Neural Networks for Spatio-temporal Coarse Graining
  • Michele Ceriotti (EPFL)
  • John Chodera (Sloan Kettering Institute)
  • Aaron Dinner (University of Chicago)
  • Andrew L. Ferguson (University of Chicago), Machine learning collective variable discovery in colloidal assembly and protein folding (abstract)
  • Mauro Maggioni (John Hopkins)
  • Jean-Bernard Maillet (CEA/DAM)
  • Cosmin Marinica (CEA/DEN)
  • Christine Peter (Univ. Konstanz)
  • Fabio Pietrucci (IMPMC)
  • Alexandre Tkatchenko (Univ. Luxemburg), Machine Learning Enables Essentially Exact Molecular Dynamics of Small Molecules
  • Zofia Trstanova (Univ. Edinburgh)
  • Rodolphe Vuillemier (ENS Paris)
  • Ross Walker (GSK)