Abstract:
The different solid structures or polymorphs of atomic and molecular crystals often possess
different physical and chemical properties. Structural differences between organic molecular
crystal polymorphs can affect, for example, bioavailability of active pharmaceutical formulations,
the lethality of contact insecticides, the performance of molecular electronic devices, and diffusive
behavior in host-guest systems. Such differences can also influence the behavior of smart materials, such as self-healing crystals. Crystallization conditions can influence polymorph selection, making an experimentally driven hunt for polymorphs difficult. Theory and computation can potentially play a vital role in mapping the landscape of crystal polymorphism. Traditional methods for predicting crystal structures and investigating solid-solid phase transformation behavior face their own challenges, and therefore, new approaches are needed. In this talk, I will show, by leveraging concepts from mathematics, specifically geometry and topology, in combination with simple physical principles, database processing that we have been able to develop a new framework, which we are calling “CrystalMath” that represents a new paradigm in our ability to predict molecular crystal structures and crystal properties, orders of magnitude faster and with far fewer resources than more traditional methods. Examples to be presented will show that CrystalMath can handle molecules with considerable flexibility by decomposing them into rigid fragments and applying the rules of CrystalMath to them separately and rejoining the molecules after application of the rules. In our series of examples, we will resolve a long-standing controversy concerning the polymorph landscape of the anti-cancer drug Galunisertib and show how to predict crystal structures of molecules as complex as ritonavir. I will also discuss the challenges associated with generative AImodels and describe how it can be tuned to have some impact on the problem while pushing for the use of AI in more downstream tasks such as property prediction.

Acknowledgements. This work was supported by the National Science Foundation grant nos.
CHE-1955381 and DMR-2118890 as well as grants from Pfizer Inc. and Abbvie Inc.


Bio:
Mark Tuckerman is a native of California who 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, Physics, and Mathematics at New York University. His
research program spans a variety of topics including development of free-energy based enhanced
sampling tools for predicting the conformational equilibria of complex molecules, development
of novel methods for predicting structure and polymorphism in molecular crystals, simulation
studies of electrolyte liquids for clean energy applications, development of machine learning
models for electronic structure theory and statistical mechanics applications, and path-integral
methods for quantum dynamics. Honors and awards include the Japan Society for the
Promotion of Science Fellowship, 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, the Andreas C.
Albrecht Lectureship from Cornell University, the Pitzer Lectureship from UC Berkeley, the
Kennedy Lectureship from Washington University, the Institute Lectureship from the Indian
Institute of Technology, Kanpur, where he was selected as Distinguished Visiting Professor,
election as a Fellow of the AAAS, a Dreyfus award for Machine Learning in the Chemical
Sciences and Engineering, an Einstein Fellowship from the Einstein Stiftung Berlin, a CNRS
Fellowship, the Linett Professorship from the University of Cambridge, and recent award of
Doctor Honoris Causa from the Université de Toulouse. He is the Principal Investigator and
Director Elect of the Simons Center for Computational Physical Chemistry at New York
University and former Chair of the Department of Chemistry at NYU.