Hector Ceniceros
University of California-Santa Barbara, CA·Mathematics & Statistics
Plastics, rubbers, gels, DNA and proteins are all built from polymers, long chain-like molecules. How those chains arrange themselves into patterns, or morphologies, determines whether a material is stiff, porous, conductive or self-assembling.
- Fields
Mathematics & Statistics
Materials Science
Computer Science
- Topics
polymers
soft materials
self-consistent field theory
- Grants
- 1 known grant, through Jul 2027
What they work on
- Develop efficient numerical methods that embed machine learning inside polymer self-consistent field theory, a framework predicting the stable morphology of a polymer mixture, to cut the cost of repeated expensive function evaluations.
- Accelerate the forward problem: predicting, for a given set of formulation parameters, which stable morphology a soft material will adopt.
- Solve the inverse design problem by finding the formulation parameters that stabilize a specified target morphology, enabling design of polymers with desired properties.
- Establish the machine-learning-plus-scientific-computing framework as a transferable approach for other computationally costly simulation problems in materials modeling.
More about the research
Computer simulation can predict these structures without making the material first, but the calculations are expensive: each candidate formulation requires repeatedly evaluating costly mathematical functions, so sweeping through many possible mixtures quickly becomes impractical.
Why it matters
Faster simulation of polymer systems could shorten the search for new soft materials with tuned properties, letting designers start from a desired structure and work backwards to a recipe instead of testing formulations one at a time. The same machine-learning-accelerated strategy may transfer to other expensive physical simulations, and the project trains students in this emerging combination of skills.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ahmad Najafi
Drexel University, PA·Engineering·Early-career
Create a 3-M (multiphysics, multiscale, multifunctional) optimization framework that designs a material's constituents and internal architecture at the same time while balancing conflicting functions.
Charles Sing
University of Illinois Urbana-Champaign, IL·Engineering
Use hybrid polymer field theory informed by particle simulations to test how steric repulsion and hydrophobic interactions control coacervation between polycations and surfactant micelles.
Ting Ge
University of South Carolina-Columbia, SC·Materials Science·Early-career
Determine how ring polymer topologies, including non-concatenated loops, rings threaded by linear chains, and interlocked "Olympic" rings, form as a function of experimentally controllable parameters such as ring size and linear chain content, using coarse-grained molecular simulations with topological analysis.
The school
University of California-Santa Barbara: 17 labs on PI Pages, $303M a year on research, 403 research doctorates a year.
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