ESDW: Topics in Classical MD

Classical molecular dynamics (MD) is a broad field, with many domains of expertise. Those specialist domains include topics like transition path sampling (which harvests many examples of a process in order to study it at a statistical level [1]), metadynamics (which runs a trajectory with modified dynamics that enhance sampling, and from which free energy profiles can be constructed [2]), as well as various topics focused on the underlying dynamics, either by providing better representations of the interactions between atoms (e.g., force fields [3] or neural network potentials [4]) or by changing the way the dynamics are performed (e.g., integrators [5]).

Frequently, experts in one domain are not experienced with the software of other domains. This workshop aims to combine both depth, by extending domain-specific software, and breadth, by providing participants an opportunity to learn about software from other domains. As an extended software development workshop (ESDW), a key component of the workshop will be the development of modules that extend existing software packages. Ideally, some modules may connect multiple domain-specific packages.

Topics at this workshop will include using and extending modern MD software in the domains of:

* advanced path sampling methods (and the software package OpenPathSampling)
* metadynamics and the calculation of collective variables (and the software package PLUMED)
* machine learning for molecular dynamics simulatons (including local structure recognition and representation of potential energy surfaces)

In addition, this workshop will feature an emphasis on performance testing and benchmarking software, with particular focus on high performance computing. This subject is relevant to all specialist domains.

By combining introductions to software from different specialist fields with an opportunity to extend domain-specific software, this workshop is intended to provide opportunities for cross-pollination between domains that often develop independently.

References
[1] Bolhuis, P.G. and Dellago, C. Trajectory‚ÄźBased Rare Event Simulations. Reviews in Computational Chemistry, 27, p. 111 (2010).
[2] A. Laio and F.L. Gervasio. Rep. Prog. Phys. 71, 126601 (2008).
[3] J.A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K.E. Hauser, and C. Simmerling. J. Chem. Theory. Comput. 11, 3696 (2015).
[4] T. Morawietz, A. Singraber, C. Dellago, and J. Behler. Proc. Natl. Acad. Sci USA, 113, 8368 (2016).
[5] B. Leimkuhler and C. Matthews. Appl. Math. Res. Express, 2013, 34 (2013).

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