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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 24: Poster Session I

CPP 24.19: Poster

Montag, 16. März 2020, 17:30–19:30, P3

Machine learning for DNA self-assembly: a numerical case study — •Jörn Appeldorn, Arash Nikoubashman, and Thomas Speck — Inst. für Physik, Universität Mainz, Germany

We study the spontaneous self-assembly of single-stranded DNA fragments using the coarse-grained oxDNA2 implementation [1]. A successful assembly is a rare event that requires to cross a free energy barrier. To employ advanced numerical algorithms like forward-flux sampling or Markov state modeling one needs to identify one or more collective variables (order parameters) that faithfully describe the transition towards the assembled state. Formulating appropriate order parameters typically relies on physical insight, which is then verified, e.g., through a committor analysis. Here we explore the use of machine learning to automize this process and to find suitable collective variables based on structural information. For this step one still needs to map configurations onto structural descriptors, which is a non-trivial task. Specifically, we investigate the latent space of EncoderMap [2] and how it changes with the amount of information contained in the descriptor.

[1] - Snodin et al., J. Chem. Phys.(2015), 142, 234901 [2] - T. Lemke and and C. Peter,J.Chem.TheoryComput.(2019), 15, 1209*1215