About

Bradley D. Olsen is the Alexander and I. Michael Kasser Professor and the Department Executive Officer at the Massachusetts Institute of Technology's Department of Chemical Engineering. His research focuses on chemical engineering, with particular emphasis on areas such as energy, environment and sustainability, materials, and systems. As a faculty member, he contributes to the academic leadership and research initiatives within the department, advancing knowledge and innovation in chemical engineering.

Research topics

• Organic chemistry
• Materials science
• Data Mining
• Computer Science
• Chemistry
• Nanotechnology
• Polymer science
• Polymer chemistry
• Chemical engineering
• Composite material

Selected publications

• Tracer Diffusivity in Amphiphilic Polymer Model Co-Networks

  Macromolecules · 2026-01-16 · 2 citations

  articleOpen accessCorresponding

  Amphiphilic polymer conetworks (APCNs) are highly interesting material for membranes, drug delivery, or tissue engineering since their heterogeneous structure and interactions allow for the control of the diffusion of molecules differing by architecture, size, and interactions. We investigate the diffusion of hydrophilic and hydrophobic star polymers in model APCNs formed by heterocomplementary end-linking of tetra-poly(ethylene glycol) (t-PEG) and tetra-poly(ε-caprolactone) (t-PCL). Using Fluorescence Recovery After Photobleaching (FRAP) and Forced Rayleigh Scattering (FRS), we gain complementary insights into star polymer transport across different length and time scales. We compare the diffusion of hydrophilic t-PEG and hydrophobic t-PCL of various molecular weights across a wide range of APCN polymer volume fractions, swollen in a cosolvent (toluene) and a selective solvent (water). FRS reveals Fickian diffusion for all tracers in APCNs swollen in toluene. In the unentangled regime, the diffusivity of the tracer follows approximately the expected Rouse scaling for semidilute solutions. Corrections arise for increasing polymer content due to enforcing contacts with the other type of polymer in the APCN. At larger concentrations, the PEG tracers develop a diffusion behavior, as expected for entangled star polymers. Since the transition occurs below the expected entanglement concentration, an additional impact of the strangulation regime is likely. Partial swelling in a selective solvent leads to an enhanced diffusion behavior as compared to a homogeneously swollen network at the same polymer volume fraction; however, the concentration dependence of diffusion agrees best with the strangulation regime, despite an overall enhanced diffusion. At swelling equilibrium in the selective solvent water, the equilibrium degree of swelling, the network morphology, and the diffusion behavior become independent of the preparation conditions. These findings provide insights into the diffusion mechanism of star polymers within APCNs and contribute to the development of polymer-based drug delivery systems for biomedical applications.

  PublisherDOI

• Extending BigSMILESto Include Topological Bonds

  Figshare · 2026-03-04

  articleSenior author

  Machine-readable line notations such as SMILES are rapidly gaining popularity as a means of storing, searching, and analyzing chemical information. Within the SMILES framework, BigSMILES was developed to represent the stochastic connectivity characteristics of polymers. More recently, noncovalent BigSMILES extended the notation to encode noncovalent interactions, recognizing the stochastic nature of these bonds. Inspired by this framework, Topological BigSMILES is introduced to provide a further extension to represent topological interactions in macromolecules. This notation appends optional topological bond descriptors and associated indices, enabling the annotation of complex molecular architectures. In particular, the notation presented herein can encode the topological interactions found in knotted macrocycles and polymers, polycatenanes, and polyrotaxanes. The progression from BigSMILES to Topological BigSMILES highlights the potential for this framework to be used in representing broader classes of soft materials systems.

  DOI

• Sequence Effect in the Cononsolvency of Elastin-like Polypeptides in Water, Ethanol, and Sodium Chloride Solutions

  Biomacromolecules · 2026-01-07 · 1 citations

  articleSenior authorCorresponding

  Elastin-like polypeptides (ELPs) are a family of recombinant biopolymers that offer precise sequence control. Six ELP sequences with systematically varied hydrophobicity and charge were designed to investigate how hydrophobicity and charge influence dilute solution phase behavior in 0-40 mol % ethanol and 0-200 mM sodium chloride. Both hydrophobic and hydrophilic ELPs in this study display four characteristic regimes in their phase diagrams: lower critical solution temperature (LCST)-like transitions at low ethanol concentrations, a one-phase region at low-to-moderate ethanol concentrations, upper critical solution temperature (UCST)-like transitions at intermediate ethanol concentrations, and full miscibility at high ethanol concentrations. Despite an identical overall composition with a previously studied ELP sequence, differences in sequence and molecular weight significantly impact phase behavior in ethanol/water mixtures. These results reveal both sequence dependence in the phase behavior of ELPs and universal cononsolvency behavior in uncharged hydrophobic and hydrophilic ELPs.

  PublisherDOI

• Structural Heterogeneity and Hydrodynamics of an Intrinsically Disordered Protein Condensate

  Journal of the American Chemical Society · 2026-02-17

  articleSenior authorCorresponding

  Biology demonstrates precise control over the free-energy landscape through the selective partitioning of biomacromolecules into membraneless organelles, enabling essential functions such as biochemical transformations, signaling cascades, and mechanical reinforcement. Although the function of these condensates depends on their underlying structure and hydrodynamics, molecular-scale information on these systems remains sparse. Here, neutron scattering is used to probe the organization and dynamics of the intrinsically disordered N-terminal domain of Galectin-3, an extracellular lectin responsible for facilitating liquid–liquid phase separation on the cellular surface, in both dilute and condensed phases. Dilute solutions contain isolated protein chains in equilibrium with mesoscopic clusters, whereas the condensed phase adopts a bicontinuous, microemulsion-like morphology. The dilute phase behavior is quantitatively described by coarse-grained polymer models from soft-matter physics, demonstrating their predictive power for complex biological proteins. At elevated concentrations, the proteins self-assemble akin to block copolymers, microphase separating through the aggregation of hydrophobic domains along the protein contour. The resulting condensate remains fluid-like despite a 25-fold increase in concentration; its internal hydrodynamics slow by only a factor of 3 relative to dilute protein chains. These results provide a molecular-level framework for how disordered proteins achieve both the structural complexity and dynamic fluidity of biomolecular condensates.

  PublisherDOI

• The Effect of Excluded Volume on Fracture Mechanisms in Coarse-Grained Simulations of Polymer Networks

  Macromolecules · 2026-03-31

  articleSenior authorCorresponding

  Macroscopic fracture behavior of polymer networks is governed by chain scission at the molecular level, yet the mechanism of fracture remains elusive. While state-of-the-art, highly coarse-grained simulation methods have been successful at predicting macroscopic fracture properties for strain rates of approximately 1/s, they are usually limited by key assumptions of periodic boundary conditions and purely attractive interactions within network elements. Incompressibility is enforced by constraining a constant lateral contraction of periodic boundary conditions to achieve Poisson’s ratio of 0.5 throughout the linear and nonlinear tensile regimes, which restricts the network structure and dynamics, preventing accurate analysis of fracture propagation at the molecular level. Here, a new coarse-grained fracture simulation approach is presented, which models repulsive interactions within a network as a mean-field excluded volume (EV) potential and eliminates the need for imposed deformation along nontensile axes. Simulation results reveal that consideration of EV interactions in networks leads to physically consistent and more realistic fracture behavior. Networks with EV exhibit a necking regime during fracture, consistent with the behavior of real networks under pure tensile deformation. Linear elastic moduli in networks with EV show excellent quantitative agreement with previously reported experimental results. While simulations without EV can predict correct quantitative trends in toughness, which is also reproduced in the simulations with EV, the molecular-level chain scission mechanisms obtained with EV are much more physically consistent, in stark qualitative and quantitative contrast to simulations without EV. Most notably, the presence of EV interactions facilitates accurate simulation of elastic recoil of the network after fracture, whereas the lack of EV leads to network collapse post-failure. EV interactions contribute to network homogenization during stress relaxation, leading to delayed fracture under low strains, whereas stress concentration is observed without EV.

  PublisherDOI

• MacroSimGNN: Efficient and Accurate Prediction of Macromolecule Pairwise Similarity via A Graph Neural Network

  Macromolecules · 2026-02-16

  articleCorresponding

  Efficient and accurate prediction of macromolecule pairwise similarity is essential for developing database search engines and is useful for machine learning based predictive tools. Existing methods for calculating macromolecular similarity suffer from significant drawbacks. Graph edit distance is accurate but computationally expensive, and graph kernel methods are computationally efficient but inaccurate. This study introduces a graph neural network model, MacroSimGNN, which significantly improves computational efficiency while maintaining high accuracy on macromolecule pairwise similarity. Furthermore, this approach enables feature embeddings based on macromolecular similarities to a set of landmark molecules, enhancing both unsupervised and supervised learning tasks. This method represents a significant advancement in macromolecular cheminformatics, paving the way for the development of advanced search engines and data-driven design of macromolecules.

  PublisherDOI

• Biodegradability of Acrylate-Lipoic Acid Copolymers

  Journal of the American Chemical Society · 2026-05-08

  articleSenior authorCorresponding

  Lipoic acid has attracted a great deal of attention for its ability to impart chemical degradability into vinyl polymer chains. This work investigates the biodegradability of acrylate-lipoic acid copolymers to understand how sulfide bonds along the backbone of these copolymers impact biodegradation. To achieve this objective, several acrylate monomers (methyl, ethyl, and n-butyl acrylates) were polymerized with lipoic acid (1, 3, 5, 10, and 15 mol %) via reversible addition–fragmentation chain transfer (RAFT) polymerization, and biodegradation was assessed via a high-throughput clear-zone polymer biodegradation method using Paucimonas lemoignei (P. lemoignei) and three other bacterial species. Mass loss and hydrolysis tests were also conducted in liquid culture with liquid chromatography-mass spectrometry for degradation product analysis. The acrylate homopolymers did not show any biodegradation, whereas poly(methyl acrylate-co-lipoic acid) and poly(ethyl acrylate-co-lipoic acid) copolymers showed biodegradation by P. lemoignei above a critical lipoic acid mole fraction that increased with increasing hydrophobicity of the acrylate monomer. While degradation is clearly accelerated by the formation of labile linkages in the polymer backbone resulting from the incorporation of sulfur atoms from lipoic acid, experiments with methyl acrylate oligomers show that small acrylate fragments are also degraded by P. lemoignei and that small molecule products are produced below the molar mass of the acrylate fragments. Therefore, this work illustrates that the incorporation of lipoic acid provides a route toward fully biodegradable polyacrylates.

  PublisherDOI

• Hard sphere-driven phase transitions in coarse-grained simulations of globular protein–polymer block copolymers

  The Journal of Chemical Physics · 2026-04-22

  articleSenior author

  Although globular protein-polymer bioconjugates in solution have been shown to self-assemble into many of the same nanostructures observed for traditional coil-coil block copolymers, there are also key differences that appear to be universal across bioconjugate systems with different protein and polymer blocks. This suggests that factors originating from the coarse-grained shape of the molecules may play a key role in many of these behaviors. Here, coarse-grained molecular dynamics simulations of dumbbells consisting of a hard sphere, representing the protein, and a soft sphere, representing the polymer, were used to investigate the physics underlying self-assembly. This highly coarse-grained model captured many of the most notable features of the protein-polymer bioconjugate phase diagram, including compositional asymmetry and a lyotropic reentrant order-disorder transition. The hard sphere block was found to be an important determinant for phase diagram asymmetry, with the rigidity of the hard sphere prohibiting the formation of spheres and inverse phases. Furthermore, entropically driven hard sphere ordering at high concentrations appears to be correlated with a restriction in the rearrangement of the dumbbells into a well-ordered nanostructure, leading to the reentrant order-disorder transition into a weakly ordered phase. The insights from this model can inform the design of biomaterials that incorporate globular protein-polymer block bioconjugates.

  PublisherDOI

• Learning Universal Fundamental Relations for Predicting the Phase Behavior of Liquid Mixtures

  Industrial & Engineering Chemistry Research · 2026-03-30

  articleSenior authorCorresponding

  Predicting properties of liquid mixtures is central to chemical engineering, yet accurate predictions from unimolecular properties have proven elusive. Activity models are a popular class of mixture models due to their parsimony yet strong correlative ability. The latest evolution in activity models is data-driven machine learning models using neural networks (NN). However, NN models require a substantial amount of training data to make their predictions, and many lack an accessible free energy function, hindering extrapolation. This work presents a joint data- and theory-driven model learning a set of universal fundamental relations (UFR) governing small molecule liquid mixtures. UFR models accurately correlate experimental infinite dilution activity coefficient data (IDAC). Trained only on IDAC data, UFR models can predict T–x–y and P–x–y vapor–liquid equilibrium curves of mixtures outside the training set. Several UFR models were also able to simultaneously predict binary liquid–liquid phase separations.

  PublisherDOI

• Learning nature’s assembly language with polymers

  Proceedings of the National Academy of Sciences · 2026-02-10

  articleOpen accessSenior authorCorresponding

  The self-assembly of matter into ordered structures is ubiquitous throughout nature and engineered systems. Programming a material's macroscopic properties via molecular-level structural control is a grand scientific challenge, requiring methods for inverse design that can design a targeted molecule to achieve a given self-assembled structure. One model system that serves as a common proving ground for inverse design algorithms is block copolymers. In these systems, self-consistent field-theory (SCFT) provides a robust thermodynamic model for predicting self-assembly for a given molecular sequence. This work presents a computational algorithm which learns the reverse translation, allowing a target structure to be achieved by varying molecular sequence. The algorithm is based on development of an adjoint solution of the SCFT equations allowing incorporation of automatic differentiation. The power of this algorithm is demonstrated by inverse designing polymer sequences to yield equilibrium structures, resolving the long-standing dilemma of navigating the combinatorial explosion of sequence possibilities offered by complex copolymer designs. The inverse designed sequences show that the algorithm learns to modulate unfavorable block interactions to stabilize these complex morphologies. By learning how to program self-assembly at the molecular-level using only a thermodynamic model, this work opens the door to similar computational inverse design across other soft matter systems.

  PublisherDOI

Recent grants

• NSF Convergence Accelerator Track D: A Community Resource for Innovation in Polymer Technology (CRIPT)

  NSF · $5.0M · 2021–2025

• Engineering a new family of consensus repeat proteins based on nucleoporins

  NSF · $350k · 2017–2022

• CAREER: Self-Assembly of Fusion Proteins to Form Biofunctional Materials

  NSF · $500k · 2013–2019

• Dynamics of Associative Polymers Revealed by Self-Diffusion

  NSF · $360k · 2017–2023

• NSF Convergence Accelerator Track D: A Community Resource for Innovation in Polymer Materials

  NSF · $1.0M · 2020–2021

Frequent coauthors

• Rachel A. Segalman

  University of California, Santa Barbara

  97 shared

• Michael F. Toney

  41 shared

• Stephen L. Craig

  Duke University

  41 shared

• Haley K. Beech

  University of California, Santa Barbara

  35 shared

• Ali Khademhosseini

  Terasaki Foundation

  35 shared

• Jeremiah A. Johnson

  Massachusetts Institute of Technology

  34 shared

• Reginald K. Avery

  Harvard University

  31 shared

• Michael Rubinstein

  27 shared

Labs

• MIT ChemEPI

Education

• Ph.D., Chemical Engineering

  Massachusetts Institute of Technology

  1990

• M.S., Chemical Engineering

  Massachusetts Institute of Technology

  1986

• B.S., Chemical Engineering

  University of California, Berkeley

  1984

Awards & honors

• MIT ChemE Individual Commendation Award, 2026
• American Physical Society (APS) Fellow, 2023
• Fulbright Amazonia Scholar, 2023
• Alexander and I. Michael (1960) Kasser Chair in Chemical Eng…
• ACS Macro Letters/Biomacromolecules/Macromolecules Young Inv…