A Graph-based Methodology for Modeling the Nucleation of Weak Electrolytes

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Jordan R. Schmidt of University of Wisconsin-Madison will develop new methods for simulating the growth of crystals in solution. The nucleation and growth of crystals is a ubiquitous phenomenon that underlies many chemical and biological processes, from the formation of water ice to the growth of bone via biomineralization. Schmidt and his research team will develop and apply new methods that allow the initial stages of crystal growth to be examined in atomistic detail via computer simulations, potentially opening the door to a better understanding for how to direct and control the growth of important crystalline materials. A special emphasis will be placed on modeling the growth of important solutes with low solubility, e.g., emerging nano-porous materials, where existing simulation methods are inadequate. Professor Schmidt will also conduct additional efforts to broaden participation in theoretical chemistry, both in this project and in the wider scientific community, via participation in the UW Madison Chemistry Opportunities (ChOPS) and ACS Bridge to the Doctorate programs. The Schmidt team will also focus on K-12 outreach activities, including live chemistry demonstrations, including those on crystal nucleation, at local elementary and middle schools and the Wisconsin Science Festival. In this project, the Schmidt group at U. Wisconsin-Madison will develop and apply a graph theory-based methodology that is tailored toward materials whose crystal structure exhibits directional bonding, whether purely electrostatic, or via coordination complexes, and thus can be described as a “graph” of connected monomers. Using a bootstrapping approach, the free energies of growing nuclei will be calculated via a rigorous approach rooted in statistical thermodynamics. The methodology eliminates the length- and timescale limitations inherent to the nucleation of such weak electrolytes (e.g. mass transport, desolvation) that currently preclude the atomistic simulation of weak electrolyte nucleation. Following development, validation and optimizations to further enhance the efficiency of this graph-based approach, the team will explore applications to simple salts, weak electrolytes, and nano-porous materials (i.e., metal-organic frameworks), an important class of low-solubility material where nucleation plays a key (and often limiting) role in materials synthesis and crystal engineering. The resulting codes/scripts will be released in open-source form via GitHub to enable applications to other domains by the scientific community at large. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.