Theory, computation, and high-performance computers are playing an increasingly important role in helping us understand, design, and characterize a wide range of functional materials, chemical processes, and biomolecular/biomimetic structures. The synergy of computation and experiment is fueling a powerful approach to address some of the most challenging scientific problems. In this talk, I will describe the efforts ongoing in my group to develop algorithmic and software tools for addressing various problems in the development and screening of drugs, including small-molecules to peptide-based therapeutics.  In particular, I will discuss the creation of a new crystal-structure prediction pipeline we are building, which includes tools for random packing and enhanced molecular dynamics based free-energy structure searching.  I will also show how these tools can be used to study crystal melting.  I will describe our recent development of enhanced free energy based methodologies for determining conformational preferences of bound and free oligopeptides and suggest ideas for refining predictions of docking calculations. The strategies we are pursuing include large time-step molecular dynamics algorithms, heterogeneous multiscale modeling and learning 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.  Finally, I will discuss new schemes for using machine learning techniques to represent and perform computations using multidimensional free energy surfaces.

Mark Tuckerman obtained his B.S. in physics from the University of California at Berkeley in 1986 and his Ph.D. from Columbia University in 1993, working in the group of Bruce J. Berne.  From 1993-1994, he held an IBM postdoctoral fellowship at the IBM Forschungslaboratorium in Rüschlikon, Switzerland in the computational physics group of Michele Parrinello.  From 1995-1996, he held an NSF postdoctoral fellowship in Advanced Scientific Computing at the University of Pennsylvania in the group of Michael L. Klein.  He is currently Professor of Chemistry and Mathematics at New York University. His research interests include the use of theoretical and computational chemistry techniques to study reactions in solution, the development of large time-step molecular dynamics algorithms and free-energy based enhanced sampling tools for predicting the conformational equilibria of complex molecules and the exploration of polymorphism in molecular crystals.  Honors and awards include the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation, the Camille Dreyfus Teacher-Scholar Award, an NSF CAREER Award, and the NYU Golden Dozen Teaching Excellence Award, and the Sentinels of Science Award from Publons.