About

Glenn Fredrickson is the Mitsubishi Chemical Professor at the University of California, Santa Barbara, within the Department of Chemical Engineering. He is also the Director of the Mitsubishi Chemical Center for Advanced Materials and the Complex Fluids Design Consortium. His research focuses on the theoretical analysis of complex fluids and polymers, including suspensions, polymer solutions, and melts, with particular emphasis on block and graft copolymers. A significant aspect of his work involves developing new computer simulation tools, known as 'field theoretic simulations,' for analyzing statistical field theory models of polymers and complex fluids. These tools are applied to the design of improved complex fluid formulations and high-performance plastic materials.

Research topics

• Chemistry
• Materials science
• Physics
• Nanotechnology
• Composite material
• Artificial Intelligence
• Mathematics
• Computer Science
• Theoretical computer science
• Chromatography
• Physical chemistry
• Chemical engineering
• Chemical physics
• Polymer chemistry
• Organic chemistry
• Biochemistry
• Statistical physics
• Statistics
• Computational chemistry
• Quantum mechanics
• Thermodynamics
• Algorithm
• Mathematical optimization
• Psychology

Selected publications

• Microstructure Evolution during Nonsolvent-Induced Phase Separation: A Joint Experimental–Computational Investigation into Membrane Formation

  Macromolecules · 2026-04-27

  articleSenior authorCorresponding

  Our current fundamental understanding of morphology evolution during nonsolvent-induced phase separation (NIPS) is limited to insights developed from qualitative observation and trial-and-error experimentation. Emerging particle and field-based simulation methods offer a powerful framework for quantitatively describing the NIPS process and understanding the formation of specific microstructures (e.g., nodules, micropores, or macrovoids). However, such simulations are seldom validated against experimental analyses at similar conditions. In this study, experimental polysulfone (PSf)-based ternary NIPS membrane-forming systems, referenced in this work as casting solutions or dopes, are analyzed alongside phase-field simulated membrane structures. Experimental and simulation results consistently demonstrated strong relationships between Flory–Huggins χ parameters and thermodynamics, kinetics, and membrane morphology arising from the NIPS process. Notably, in both analyses, increasing values of the χns parameter describing binary interactions between nonsolvent (n) and solvent (s) components improved overall casting solution thermodynamic stability while increasing shrinkage and micropore content of resulting PSf membranes. Increasing the χnp parameter between the nonsolvent and PSf (p) accelerated NIPS demixing kinetics. Manipulation of individual χ parameters in phase-field simulations provided further insight on the influence of pairwise thermodynamic interactions on membrane formation. Although some features like nodules or macrovoids cannot presently be generated in phase-field simulated membrane structures, the overall agreement between experimental and computational efforts in this study represents an important step forward in the development of physically accurate NIPS modeling techniques.

  PublisherDOI

• Rubbery Midblock Content Controls Formation and Mechanical-Separation Trade-offs in Block Polymer Membranes

  Macromolecules · 2026-04-27

  article

  Nonsolvent-induced phase separation (NIPS) is widely used to fabricate ultrafiltration (UF) membranes; however, the non-equilibrium nature of this process often yields disordered structures that limit separation performance. Self-assembly of amphiphilic block polymers can impose surface order; yet, most block polymer platforms reported to date rely on brittle polystyrene-based structural matrices. Here, we examine an amphiphilic tetrablock polymer, poly(styrene-b-cis-4,1-isoprene-b-styrene-b-4-vinylpyridine) (SISV), in which a rubbery poly(cis-4,1-isoprene) (PI) midblock is introduced between two glassy poly(styrene) (PS) blocks to enhance toughness. We consider four block polymers with PI contents ranging from 0 to 36 wt %, a fixed poly(4-vinylpyridine) (P4VP) fraction of 25 wt %, and an overall number-average molecular weight of Mn ≈ 100 kDa. Bulk mechanical testing of notched and unnotched specimens reveals a two-step transition in mechanical response with increasing PI content: from brittle to rubber-toughened thermoplastic, and ultimately to thermoplastic elastomer. Membranes fabricated via self-assembly assisted NIPS (SNIPS) exhibit a concomitant shift from ordered to increasingly disordered surface morphologies with increasing PI content; this shift alters the molecular-weight cutoff (MWCO) and poly(ethylene glycol) (PEG) rejection profiles while maintaining high pure-water permeance (ca. 1300 L m–2 h–1 bar–1). Together, these results delineate a clear trade-off between separation performance and mechanical robustness, and identify block polymer architecture and phase-inversion conditions as practical levers for manufacturing membranes that balance separation performance with large-scale processability.

  PublisherDOI

• Exact kinetic propagators for coherent state complex Langevin simulations

  Physical review. A/Physical review, A · 2026-01-14

  articleOpen accessSenior author

  We introduce and benchmark an improved algorithm for complex Langevin simulations of bosonic coherent state path integrals. Our approach utilizes a Strang splitting of the imaginary-time propagator rather than the conventional linear-order Taylor expansion, allowing us to construct an action that incorporates higher-order terms at negligible computational cost. The resulting algorithm enjoys guaranteed linear stability independent of the imaginary-time discretization, enabling more resource-efficient simulations. We demonstrate this improved performance for single-species bosons and for two-component bosons with Rashba spin-orbit coupling.

  PublisherOA PDFDOI

• Coherent state field theory: A tool for inhomogeneous polymer dynamics and rheology

  The Journal of Chemical Physics · 2025-10-15

  article1st authorCorresponding

  A non-equilibrium framework is introduced for recasting microscopic kinetic models of polymer dynamics into a compact field-theoretic form. Specifically, we adapt the Doi-Peliti formalism, which transforms a classical many-body problem into a second-quantized Schrödinger equation that is subsequently expressed as a real-time path integral using a boson coherent state basis. The framework is well-suited to the analysis of non-equilibrium, spatially inhomogeneous systems, which is illustrated using a simple Brownian dynamics model of dumbbell polymers in implicit solvent. By invoking a mean-field approximation, equations are derived that describe the coupled dynamics of polymer concentration and stress to second order in spatial gradients. New stress-concentration coupling and stress diffusion terms are found to arise from non-bonded interactions and serve to generalize previous theories beyond the dilute limit. Strategies are discussed for exploring fluctuation effects beyond the mean-field approximation, both analytically and numerically via field-theoretic simulation. The method can be extended to a wide variety of non-equilibrium polymer models, including those with reversible or irreversible chemical reactions.

  PublisherDOI

• Hydrogen Bonding in Supramolecular <i>AB</i> / <i>C</i> Melts: From Negative χ to Coherent-States Theory

  Macromolecules · 2025-12-08

  articleSenior authorCorresponding

  We revisit the phase behavior of supramolecular AB/C polymer blends using a coherent-states supramolecular interaction model (CS-SIM), which explicitly captures reversible hydrogen bonding through one-to-one associations between A and C segments. By comparing CS-SIM predictions with those from the conventional auxiliary-field attractive interaction model (AF-AIM) based on the negative χ approach, we investigate how bond strength and C homopolymer molecular weight impact microphase transitions. While both models yield qualitatively similar phase diagrams, CS-SIM captures the bond saturation effect, which avoids the overestimation of phase transitions seen in AF-AIM. In addition, it provides direct access to bonding statistics and spatial distributions, enabling interpretation of phase behavior in terms of the underlying supramolecular physics. With the versatile and physically grounded CS-SIM framework in hand, we further investigate the influence of reactive site placement along the polymer backbone on bond spatial distribution and phase behavior.

  PublisherDOI

• Efficient Computation of Single‐Chain Partition Functions in Field‐Theoretic Simulations of Polymers With Nested Tree‐Like Topologies

  Macromolecular Theory and Simulations · 2025-05-12 · 1 citations

  articleSenior authorCorresponding

  Abstract A general algorithm is introduced to compute single‐chain partition functions in field‐theoretic simulations of polymers with nested tree‐like topologies, including self‐consistent field theory simulations that invoke the mean‐field approximation. The algorithm is an extension of a method used in a number of recent studies on the phase behavior of bottlebrush block copolymers. In those studies, the computational cost of computing single‐chain partition functions is reduced by aggregating the statistical weight of degenerate side arms. By extending this method to chains with arbitrary degrees of branching, the computational cost is reduced to scale with the total length of unique segments in the chain instead of the total length/mass of the entire chain. The method is first validated on a model dendrimer system by comparing results to coarse‐grained molecular dynamics simulations and also demonstrate its advantage over more conventional approaches to compute single‐chain partition functions. The algorithm is subsequently used to analyze the phase behavior of a molecularly informed field‐theoretic model of poly(butyl acrylate)‐ graft ‐poly(dodecyl acrylate) (pBA‐ graft ‐pDDA) copolymers in a dodecane solvent. The methodology can help advance field‐theoretic investigations of branched polymers by leveraging degeneracy in the chain to reduce computational cost and avoid the need to develop architecture‐specific algorithms.

  PublisherDOI

• Phase Behavior of Reversibly Bonding Polymer Blends

  Macromolecules · 2025-06-18 · 6 citations

  articleSenior authorCorresponding

  Blending polymers is a versatile strategy for creating materials with tailored properties, but controlling the phase behavior of polymer blends remains a central challenge. Functionalization with sparse, associative chemical groups is a powerful way to shift phase behavior without changing individual component properties. We develop a field-theoretic model for heteroassociating polymer blends using the coherent states formalism, enabling an exact treatment of reversible bonding while avoiding explicit enumeration of polymer topologies. This framework captures the full distribution of supramolecular species, including higher-order branching and large clusters, and reveals how correlations between association sites of multifunctional polymers govern thermodynamic behavior across length scales. Using the random phase approximation, we identify conditions for macrophase separation and microphase ordering, and uncover a new motif for microphase separation in which bond density, rather than species density, exhibits spatial variations. These results unify and extend existing theories of reversibly bonding polymers, including phenomena such as gelation, and establish a foundation for designing compatibilizers through polymer architecture and sequence-level control of reversible interactions.

  PublisherDOI

• Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

  Macromolecules · 2025-11-12 · 1 citations

  articleSenior authorCorresponding

  Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.

  PublisherDOI

• Fast phase prediction of charged polymer blends by white-box machine learning surrogates

  ArXiv.org · 2025-09-08

  preprintOpen access

  Compatibilized polymer blends are a complex, yet versatile and widespread category of material. When the components of a binary blend are immiscible, they are typically driven towards a macrophase-separated state, but with the introduction of electrostatic interactions, they can be either homogenized or shifted to microphase separation. However, both experimental and simulation approaches face significant challenges in efficiently exploring the vast design space of charge-compatibilized polymer blends, encompassing chemical interactions, architectural properties, and composition. In this work, we introduce a white-box machine learning approach integrated with polymer field theory to predict the phase behavior of these systems, which is significantly more accurate than conventional black-box machine learning approaches. The random phase approximation (RPA) calculation is used as a testbed to determine polymer phases. Instead of directly predicting the polymer phase output of RPA calculations from a large input space by a machine learning model, we build a parallel partial Gaussian process model to predict the most computationally intensive component of the RPA calculation that only involves polymer architecture parameters as inputs. This approach substantially reduces the computational cost of the RPA calculation across a vast input space with nearly 100% accuracy for out-of-sample prediction, enabling rapid screening of polymer blend charge-compatibilization designs. More broadly, the white-box machine learning strategy offers a promising approach for dramatic acceleration of polymer field-theoretic methods for mapping out polymer phase behavior.

  PublisherOA PDFDOI

• Molecular understanding of ion transport in a zwitterionic electrolyte

  The Journal of Chemical Physics · 2025-12-04 · 1 citations

  article

  Zwitterions (ZIs) are unique molecules that carry both positive and negative charges, resulting in overall charge neutrality and high dielectric constants. These distinctive properties have enabled broad applications of zwitterionic functionality, including the emerging use of ZIs in lithium-ion battery electrolytes. As a contribution to this developing field, we use all-atom molecular dynamics simulations to investigate the ion transport mechanisms in amorphous mixtures of a zwitterionic liquid containing a range of LiTFSI salt concentrations. The local coordination environment around the Li+ ions plays a strong role in governing ionic conductivity, as well as the enhancement of Li+ transport numbers with increasing salt concentration. Addition of small amounts of water leads to increased conductivity and ion mobilities due to the water coordinating with the Li+ ions, which reduces direct interactions with larger charged species.

  PublisherDOI

Recent grants

• Field-Theoretic Simulations: Coherent States and Particle-Field Linkages

  NSF · $625k · 2021–2025

• Field-Theoretic Simulations: Polarization Phenomena and Coherent States

  NSF · $415k · 2018–2021

• DMREF: Collaborative: Computationally Driven Discovery and Engineering of Multiblock Polymer Nanostructures Using Genetic Algorithms

  NSF · $390k · 2013–2016

• DMREF: Collaborative Research: Computationally-Driven Design of Advanced Block Polymer Nanomaterials

  NSF · $688k · 2017–2021

• Field-Theoretic Polymer Simulations: Fundamentals and Applications

  NSF · $300k · 2006–2009

Frequent coauthors

• Kris T. Delaney

  University of California, Santa Barbara

  183 shared

• Edward J. Krämer

  98 shared

• Craig J. Hawker

  University of California, Santa Barbara

  42 shared

• Frank S. Bates

  University of Minnesota

  32 shared

• Nabil Laachi

  University of California, Santa Barbara

  29 shared

• Rachel A. Segalman

  University of California, Santa Barbara

  28 shared

• Carlos J. García‐Cervera

  University of California, Santa Barbara

  27 shared

• Nathaniel A. Lynd

  The University of Texas at Austin

  27 shared

Awards & honors

• Materials Theory Award from the Materials Research Society
• William H. Walker Award from the AIChE
• Fellow, American Association for the Advancement of Science
• Election to the National Academy of Sciences
• Fellow, American Academy of Arts & Sciences