Smart Data Approaches for the Inverse Design of Soft Materials

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

The traditional approach to the design of new, smart materials requires costly experimentation to explore a vast combination of parameters. This process can be accelerated in a "virtual lab" with computer simulations but it requires a non-traditional approach. Until recently, the focus of computer simulations has been the forward problem: given values of the parameters such as molecular architecture, composition, etc., find the material properties. But the inverse problem in which given a desired set of material properties we seek values of the parameters that give rise to those properties, is more critical for accelerating the design and advancement of new materials. This inverse design problem, which is an extraordinarily challenging, high-dimensional global optimization problem, is one of the main thrusts of this project. The envisioned mathematical and computational framework will be broadly applicable to a wide variety of materials such as block copolymers and nano-structured soft materials. This project will have a substantial impact in education with an increased participation of students from underrepresented groups and undergraduates Traditionally, computer simulations are performed for some educated choices of the model parameters. Each simulation is in general costly so that, even with state of the art computing resources, only a limited number of simulations is feasible for realistic models. This is a particularly serious problem when the parameter space is moderate to high dimensional and is impossible to fully explore that space. In this project, the principal investigator will develop a probabilistic approach to solve the inverse design problem, to design superior methods for the forward problem, and to perform statistical inference for the construction of computationally more efficient, coarse-grained models. This is a smart-data approach, in which the use of prior belief about the objective function to be optimized or to be inferred (based prior evaluations) to guide the sampling of the parameter will be explored for the first time in the context of field-theoretic models of multi-block copolymers. 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.