With this award the CTMC program in the Division of Chemistry is funding Professor Cecelia Clementi at Rice University to develop models for studying properties of matter at various scales. A fundamental challenge for the chemical sciences is to bridge the gap between the ability to study and manipulate matter at the atomistic scale with the desire to understand and predict properties at a macroscopic scale. Recently, there has been an immense increase in high-throughput and high-resolution technologies for experimental observation. In addition there is an increase in high-performance techniques to simulate molecular systems at a microscopic level. These advances have resulted in a vast and ever-increasing amounts of high-dimensional data. Consequently, there is a recent surge of interest in data analysis techniques. In particular, techniques that extract essential features, collective variables or representative states from simulations. These simulation data have to be reconciles with experimental data. However, with very few exceptions, these reconciling techniques are purely descriptive and do not allow the formulation of general principles regulating the macroscopic behavior. Furthermore it is difficult to scale up towards significantly larger and more complex systems. This project develops a new and general approach to address this challenge. The approaches are applicable to very different chemical systems, ranging from signal transduction in cells, over heterogeneous catalysis to the design of polymer brushes. The proposed research impacts a large interdisciplinary community of students and researchers in Chemistry, Physics and Mathematics. The project undertakes curriculum development in computational and mathematical methods applied to chemical systems. Curriculum development includes undergraduate and graduate courses. The project is also recruiting and mentoring women and minority undergraduate and graduate students. This activity is conducted through a collaboration with the Tapia Center at Rice University.
The project is developing a general framework to obtain the effective dynamical models (structure, equations and parameters) governing molecular systems. The key hypothesis is that, in order to be able to understand and model macroscopic systems, there is a need to use purely descriptive models to define generative models from data. Models are developed at the mesoscale from microscale simulations and multiscale experimental data. The approach is fundamentally different from available coarse-graining techniques or model reduction methods. Both the form of the macroscopic model as well as the effective dynamical equations are learned from data. Functional building blocks that can be embedded in higher order simulations are generated in order to bridge the gap between microscopic and macroscopic systems. The method investigates if and how relatively general organizing principles emerge from the interactions of a multitude of atomic degrees of freedoms in different chemical systems. This modeling approach has the potential to serve as a keystone to integrate vast amounts of chemical data into quantitative, mechanistic and comprehensible models. Such models are able to explain how different molecular components organize and interact as a function of time and space in performing functions at the macroscopic scale.
