Mesophase Engineering through Coarse-to-fine Grained Modeling

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Fernando Escobedo of Cornell University aims to create computational tools to engineer organic materials that organize into intricate structures at molecular scales. Simple physical models are very efficient in charting the generic properties and molecular organization of soft materials, but they lack the detail necessary to assign specific chemistries to the molecules, and to guide the materials synthesis to realize the sought-after properties.

Escobedo will develop methods to allow the predictions of widely used physical models to be realizable by adding the missing chemical details. Escobedo will combine advanced molecular simulation methods and machine learning strategies to systematically identify chemistries most likely to fulfill the simple-model predictions of materials that form complex structures of interest.

While the methods developed are expected to be applicable to many classes of organic materials, Escobedo will demonstrate their use with benchmark examples involving large multi-functional molecules capable of self-assembling into three-dimensional networks. Accordingly, results from this work could impact the advanced materials industry by guiding researchers to formulate stable composites for separation membranes and photovoltaics, and porous networks for catalysts and adsorbents.

The project will enable the training of doctoral and undergraduate students in computational materials research. For outreach and education, the PI will coordinate a new workshop series organized at Cornell to celebrate the student accomplishments in research and in inclusivity.

In this project, Dr. Escobedo is developing and applying molecular simulation strategies to identify polyphilic oligomers and functionalized nanoparticles capable of forming complex phases with partial structural order, called mesophases. In particular, starting from a computationally efficient but chemistry-agnostic (CA), coarse-grained (CG) model that forms a target mesophase, Escobedo will develop a scheme able to find chemistry-specific (CS) models which are fine-grained (FG) or atomistically-detailed that preserve the mesophase-formation ability.

This approach will be applied to two distinct classes of CG models that have shown significant promise in generating complex mesophases: (I) Non-linear polyphilic oligomers whose distinct chemical blocks bring about nano-phase segregation, and (II) binary blends of nanoparticles exhibiting non-additive mixing behavior. In both cases, multiple complex 3D network phases have already been predicted and many others are potentially accessible.

The mapping a given CA CG model into a CS FG model exhibiting the same sought-after mesophase behavior will entail the iterative use of a machine learning model to search through a predefined chemical space. Importantly, instead of directly searching for a CS FG model that maps into a target CA CG phase, Escobedo will use a selection “filter” at the fast, computationally efficient CG level.

Specifically, the candidate chemistries will be first mapped into simple CS CG models to readily identify those able to form the target phase; these will then be mapped onto candidate CS FG models for further validation.

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.