Van Lehn Group
Molecular Simulations of Synthetic and Biological Soft Materials

We use molecular simulations to characterize, predict, and engineer the behavior of synthetic and biological soft materials

The overall goal of Van Lehn group research is to gain molecular-scale insight into the behavior of synthetic and biological soft materials for applications relevant to human health and biotechnology, sustainability, advanced manufacturing, and energy. Our research is entirely computational and employs a variety of simulation techniques, including all-atom and coarse-grained molecular dynamics simulations, enhanced sampling techniques, and implicit solvent thermodynamics methods. A key focus of our approach is the use of chemically specific simulations that enable numerous collaborations with experimental groups at UW-Madison and around the world. We further develop and apply AI/ML methods to complement simulation analysis and accelerate both computational and experimental workflows. We promote an interdisciplinary research environment by recruiting students from the Department of Chemical and Biological Engineering, Department of Chemistry, and Biophysics Training Program, participating in the National Institutes of Health Chemistry-Biology Interface and Biotechnology Training Programs, and contributing to research in interdisciplinary centers, including the Great Lakes Bioenergy Research Center and the Center for Mineral and Mineral Oxide Removal from Biomass. Current research areas are detailed below.

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Designing synthetic materials to interact with biological molecules and materials

Synthetic materials are used in numerous applications, such as drug delivery or biosensing, that require finely tuned interactions with biological molecules or materials. For example, synthetic nanomaterials might ideally adsorb to bacterial membranes to achieve high antimicrobial activity while minimizing interactions with red blood cell membranes to avoid hemolysis. Meeting such criteria is challenging because subtle differences in material properties can manifest as large changes in biological behaviors that are difficult to predict. Moreover, fabrication can be highly laborious and time-consuming, limiting the ability to experimentally screen biomaterial libraries. We address these challenges by utilizing molecular dynamics simulations to understand interactions at the interface between synthetic materials and biosystems and use machine learning methods to uncover structure-activity relationships for the design of new materials, with a particular focus on nanoparticles and synthetic polymers.

Group members working in this area: Srija Chakraborty, Yuhui Huang, Jordan Loeffler, Letian Wang
Recent papers in this area:

  • J. D. Richardson, V. M. Rotello, R. C. Van Lehn. “Mechanistic Insights into Hydrophobicity-Dependent Antimicrobial Selectivity of Quaternary Ammonium Poly(oxanorborneneimide) Polymers Using Coarse-Grained Simulations.” Soft Matter, 2026, 22, 3488-3505. [Link]
  • D.H. Chang, J. D. Richardson, M. Lee, D. M. Lynn, S. P. Palecek, and R. C. Van Lehn. “Machine Learning-Driven Discovery of Highly Selective Antifungal Peptides Containing Non-Canonical Beta-Amino Acids.” Chemical Science, 2025, 16, 5579-5594. [Link]
  • C. A. Huang-Zhu and R. C. Van Lehn. “Influence of Branched Ligand Architectures on Nanoparticle Interactions with Lipid Bilayers.” Nanoscale, 2025, 17, 1659-1672. [Link]

Designing solvent systems for (bio)polymer dissolution and depolymerization

Understanding the behavior of synthetic and biological polymers in single- and multicomponent solvent systems is necessary to design processes that depend upon polymer solubility or solvent-mediated depolymerization. For example, we (with the Huber group at UW-Madison) have recently developed a plastic recycling process called Solvent-Targeted Recovery and Precipitation (STRAP) in which polymers are recovered from mixed plastic waste by selectively dissolving them in carefully chosen solvent systems. To guide STRAP process design, we are developing and applying high-throughput molecular and data-driven models to identify solvent systems suitable for the selective dissolution of common polymers while also considering challenges associated with process costs and safety. We are using similar methods to predict solvent effects on the dissolution and depolymerization of lignin. Together, these methods are leading to new, sustainable methods for recycling plastic waste and upcycling biomass.

Group members working in this area: Ali Altamimi, Ugo Ikegwu, Changsu Kim, Jung Min Lee
Recent papers in this area:

  • P. Zhou, U. M. Ikegwu, E. Radkevich, E. Ra, H. Liburd, G. W. Huber, V. M. Zavala, R. C. Van Lehn. “Solvent-Mediated Contaminant Removal from Plastic Waste Using Thermodynamic Modeling.” Green Chemistry, 2026, 28, 9061-9077. [Link]
  • J. Rajbangshi, C. Sener, and R. C. Van Lehn. “Effect of Solvents on Lignin-Surface Interactions via Molecular Dynamics Simulations.” Journal of Physical Chemistry B, 2025, 129 (36), 9175-9189. [Link]
  • S. Qin, S. Omolabake, A. Diaby, J. Li, L. D. Gonzalez, C. M. Holland, V. M. Zavala, S. S. Stahl, and R. C. Van Lehn. “Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design.” ACS Sustainable Chemistry & Engineering, 2024, 12 (52), 18634-18647. [Link]

Uncovering the physicochemical rules governing interactions with lipid membranes

The lipid membrane is a thin, amphiphilic, soft membrane that regulates transport into and out of the cell. Interactions between synthetic or biological materials and the membrane can induce various processes, including adsorption to the membrane surface, perturbations to membrane structure, or translocation across the membrane, that dictate critical outcomes such as cellular uptake or cytotoxicity. However, these behaviors are difficult to predict and challenging to model computationally as they occur over long timescales. To address this challenge, we perform atomistic and coarse-grained simulations, coupled with advanced sampling techniques, to model membrane adsorption, pore formation, and translocation for a range of materials. Our goal is to uncover physicochemical rules governing these behaviors that are relevant to both understanding biological processes (e.g., bacterial signaling, membrane protein production) and designing synthetic materials (e.g., drug delivery vehicles, antimicrobial agents).

Group members working in this area: ByungUk Park, Letian Wang
Recent papers in this area:

  • B. Park and R. C. Van Lehn. “Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.” Journal of Chemical Information and Modeling, 2026, 66 (4), 2299-2310. [Link]
  • J. D. Richardson and R. C. Van Lehn. “Elucidating the Impact of Membrane Components on Melittin-Induced Pore Formation in Red Blood Cell Membranes with Molecular Dynamics Simulations.” Journal of Physical Chemistry B, 2025, 129 (39), 9983-9997. [Link]
  • B. Park and R. C. Van Lehn. “Bioinspired Design Rules for Flipping across the Lipid Bilayer from Systematic Simulations of Membrane Protein Segments.” Molecular Systems Design & Engineering, 2025, 10, 567-584. [Link]

Understanding structure formation and perturbations in complex liquid environments

Liquids exhibit structure over molecular length scales that is particularly pronounced in systems with strong interactions (e.g., hydrogen bonds). Perturbations to liquid structure can lead to solvent-mediated interactions, such as hydrophobic interactions that emerge from the disruption of interfacial water structure. Preferential interactions between components in liquid mixtures can further drive structure formation to create unique solvation environments that influence macroscopic behaviors like ion transport. These behaviors are challenging to study in part because structural order parameters may be difficult to define. To address these challenges, we combine molecular dynamics simulations with machine learning techniques to analyze structure in a variety of systems, including ionic liquids, organic solvent mixtures, liquid crystals, and interfacial water. This analysis provides new insight relevant to biomaterial interfaces, electrolytes for energy storage, liquid crystal sensors, and solvent effects in liquid-phase catalysis.

Group members working in this area: Avery Greene, Changsu Kim, Yicheng Li
Recent papers in this area:

  • S. G. Johnstone*, L. Je*, B. Liu, H. Tupkar, W. Guo, V. M. Zavala, R. C. Van Lehn, M. A. Gebbie. “Rescaled Electrolyte Charge Density Provides a Unifying Descriptor of Electrocatalytic CO2 Reduction in Ionic Liquid Electrolytes.” ChemRxiv. [Link]
  • Q. Xing, J. M. Lee, Z. Yang, R. C. Van Lehn, and F. Liu. “Directing Selective Solvent Presentations at Electrochemical Interfaces to Enable Initially Anode-Free Sodium Metal Batteries.” Nature Communications, 2025, 16, 8265. [Link]
  • F. Wang, S. Qin, Z. Yang, L. M. Edwards-Medina, B. Chiu, C. Acevedo-Velez, C. Remucal, R. C. Van Lehn, V. M. Zavala, D. M. Lynn. “A Machine Learning-Assisted Liquid Crystal Droplet Array Platform for the Sensitive and Selective Detection of Per- and Polyfluoroalkyl Substances (PFAS) in Water.” ACS Sensors, 2025, 10 (10), 7343-7353. [Link]