Delayed Start

My lab will advance the design and adoption of new, sustainable, and bio-based materials. Biopolymers provide an exciting platform allowing for the precise placement of chemistry; my lab will pursue how the complementary controls of nematic ordering, chirality, and charge can be used to create new biomaterials with novel properties. My research program will entail the concerted use of a full suite of analytical analysis and multiscale simulation techniques that bridge atomic-scale descriptions of chemistry with large, continuum models that can predict material microstructure and properties. An enabling tool of my research group will lie in relative entropy coarse graining, a powerful information theoretic formulation for inverse potential design, and the group will explore other ways in which the tools of information theory and statistical mechanics can be leveraged for materials design.

I have identified three critical research themes that will advance the end-to-end design of biopolymers with novel properties:

1. Engineering Nematic and Chiral Order in Biopolymers

A distinguishing hallmark of many biopolymers is the ease with which they nematically align and how nature can even control chirality hierarchically over many scales. This research theme addresses how custom functionalization of biopolymers modifies their natural self-assembling behavior.

Impact: Using all atom simulations and relative entropy coarse graining we will quantify how varying functionalizations of biopolymer scaffolds impact their propensity to form ordered structures. This will enhance our ability to rationally transform bio-sourced polymer feedstocks and scaffolds into materials with desired performance characteristics.

2. Sequence Effects and Interplay of Nematicity and Chirality in Charged Polymeric Systems

Solid phase synthesis means we are now able to precisely pattern chemical functionality into polymers. When do nematicity and chirality enhance charge interactions and vice versa? How might nematicity and chirality modify mechanical properties of charged polymers?

Impact: Using rapid field theoretic simulations, we will explore a large variety of sequences to understand how the patterning of chemical functionality in sequence space manifests in their spatial organization. Using ideas from integral equation theory we will build statistical learning models aware of interactions across space, facilitating the mapping from sequence to structure. These tools will enable the design of a new class of polymers that can achieve higher spatial control of charge and chirality, with design applications in areas like antimicrobial peptides and membranes for chiral separation processes.

3. Accelerating Exploration of the Breadth of Bio-monomer Chemical Space

How well does the standard alphabet of biological monomers cover property space? How similar (or different) is it from the chemical space spanned by synthetic monomers? Can we identify high-yield areas of chemical space that nature has not yet exploited? Even with the fastest computers we can not explore the massive chemical space fast enough.

Impact: We will develop transferable and invertible molecular embeddings for machine learning based upon our lab’s unique ability to calculate effective interactions at multiple resolutions. We will then be able to intuitively explore chemical space via designing molecular forces, and rely on generative models to propose chemistries suitable for experimental realization.

Research Experience

My experience uniquely prepares me to pursue this research program. During my PhD I developed analytical statistical mechanical theories for modeling charged polymers. Afterwards, during my postdoctoral scholarship I developed multiscale simulation methods and addressed outstanding challenges in constructing coarse-grained models that preserve chemical detail and predict phase behavior and self-assembly. I showed how the Fisher information is related to correlations in molecular structure, and can be used to optimize simulation ensembles for the collection of configurational data that maximize the quality of coarse-grained molecular models. This has directly advanced our ability to conduct large-scale continuum field theory simulations that are faithful to atomistic simulations. I have also worked with industry partners in BASF and Dow to incorporate my development of multiscale simulation techniques into their research capabilities. This experience has given me insight into industrial needs for molecular computation, as pertains to the search for novel, eco-friendly materials. Currently I am working with the BioPACIFIC Materials Innovation Platform (NSF DMR-1933487) to demonstrate the application of our multiscale modeling techniques on intrinsically disordered proteins.

Teaching Interests

Teaching is of paramount importance to me. I am indebted to countless inspirational mentors

through my scientific career, and I see education as an important force for the development of future

scientists, engineers, and citizens engaged with the biggest problems facing society. I have always valued teaching, and was awarded “Outstanding TA” twice at Caltech for the attention and care I provided students.

My teaching will be guided by two core principles. First, I aim to nurture curiosity intrinsic to every student. I believe activating students’ curiosity brings out their creativity and energy. Where possible, I like grounding questions in vivid questions—“How much longer would it take to cook an ostrich egg than a normal egg?” Or, in a mass balance class one could ask, “How much nitrogen do we need to fix a year to feed the human population?” I will also assign hands-on and investigative projects for students – I especially enjoy workshopping with students about questions they are interested in pursuing. Secondly, I see it as my duty to help students realize their potential and career goals. To realize this objective, I look forward to organizing extracurricular mentorship programs where I bring in industry guest speakers, and engage education and career offices on campus to lead regular workshops on topics like writing resumes, elevator pitches, conducting informational interviews, and finding internships.

I can teach any core class in the Chemical Engineering curriculum, with a special interest in the subjects of thermodynamics and statistical mechanics. Not only are these useful subjects in their own right, I believe they are exemplary case studies of how human ingenuity has learned to transform complexity (10²³ atoms!) into a handful of usable engineering controls. In addition, I am also eager to support or develop courses introducing students to modern techniques in molecular simulation, chemical informatics, and statistical learning. These computational tools are becoming ubiquitous in all aspects of industry, and are in increasing demand. As part of a computational course for graduate students, I envision a significant component to be helping all students, even non-computational students, incorporate new computational tools into their research. Finally, I would also be excited to help teach introductory chemical engineering classes that can excite a larger portion of the student body to become chemical engineers. Even for students who do not wish to pursue STEM careers, I strongly believe that the systems-level thinking associated with understanding how materials and energy flow through life and society are key perspectives that are integral to being responsible, scientifically literate citizens.