MolSSI Workshop: Biomolecular Simulation

The vision of this workshop was to explore mechanisms to build common infrastructure that can help the community broadly, allowing for the rapid development of new methods as well as rapid dissemination into a broad range of community codes.

There are already many successful codes and packages for driving simulation for Molecular Dynamics, Monte Carlo, etc (eg CHARMM, AMBER, NAMD, GROMACS, LAMMPS, TINKER, OPENMM, etc). However, there is a paradigm shift emerging as massively parallel compute resources are becoming available. Running on hundreds to thousands of cores is now common, with tens of thousands to millions on the horizon. Traditional schemes for parallelization will clearly fail to scale to those resources for many simulations of interest.

However, a natural scheme to use these​ resources ​and to propel more efficient and advanced science is to employ novel sampling schemes on top of these codes. Our vision is to provide such infrastructure, allowing for a rapid development and dissemination of new methods, as well as cross fertilization between different approaches.

Organizer: Vijay Pande

Dates: 6-7 April 2017

Location: Stanford University


  • Peter Eastman, Stanford
  • Cecilia Clementi, Rice University
  • Peter Kasson, University of Virginia
  • John Chodera, Memorial Sloan Kettering Cancer Center
  • Gianni De Fabritiis, UPF 
  • Shantenu Jha, Rutgers University
  • Frank Noe, Berlin
  • Robert Abel, Schrödinger
  • David Case, Rutgers University
  • Teresa Head-Gordon, UC Berkeley
  • Baron Peters, University of California, Santa Barbara
  • David Mobley, University of California, Irvine
  • Michael Shirts, University of Colorado Boulder
  • Erik Lindahl, Stockholm University
  • Justin MacCallum, University of Calgary
  • Jay Ponder, Washington University
  • Daniel Zuckerman, Oregon Health & Science University
  • Franck Chevalier, Acellera Ltd 
  • Rick Stevens, Argonne National Laboratory
  • Daniel G. A. Smith, MolSSI
  • Christopher Bayly, OpenEye Scientific Software
  • Matthew Harrigan, Stanford
  • Paul Saxe, MolSSI
  • Lee-Ping Wang, UC Davis
  • Charles L. Brooks, III, Univ Michigan
  • Vijay Pande, Stanford University