Exploration and learning of free energy landscapes of molecular crystals and oligopeptides

Mark Tuckerman
New York University
Chemistry and Courant Institute

Theory and computation, in synergy with experiment, are playing an increasingly important role in the design and characterization of new materials. In this talk, I will describe the efforts we are making in my group to develop new computational methodologies that address specific challenges in free energy exploration and generation. In particular, I will describe our recent development of enhanced free energy based methodologies for predicting structure and polymorphism in molecular crystals [1-3] and for determining conformational equilibria of oligopeptides[4-7]. The strategies we are pursuing include heterogeneous multiscale modeling techniques, which allow “landmark” locations (minima and saddles) on a high-dimensional free energy surface to be mapped out, and temperature-accelerated methods, which allow relative free energies of the landmarks to be generated efficiently and reliably. I will then discuss a new scheme for using neural networks to represent multidimensional free energy surfaces and the use of the aforementioned enhanced sampling methods to generate the data needed to train the networks.

[1] T. -Q. Yu and M. E. Tuckerman Phys. Rev. Lett. 107, 015701 (2011).
[2] T. –Q. Yu, E. Vanden-Eijnden, P. –Y. Chen, A. Samanta, and M. E. Tuckerman J. Chem. Phys. 140, 214109 (2014).
[3] E. Schneider, L. Vogt, and M. E. Tuckerman, Acta Cryst. B (in press).
[4] J. B. Abrams and M. E. Tuckerman J. Phys. Chem. B 112, 15742 (2008).
[5] M. Chen, M. A. Cuendet, and M. E. Tuckerman J. Chem. Phys. 137, 024102 (2012).
[6] A. T. Tzanov, M. A. Cuendet, and M. E. Tuckerman J. Phys. Chem. B 118, 6529 (2014).
[7] M. Chen, T. –Q. Yu, and M. E. Tuckerman Proc. Natl. Acad. Sci. U.S.A. 112, 3235 (2015)

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