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

Research in the Van Lehn group is thematically organized around the goal of relating molecular properties to macroscopic function by employing a variety of simulation techniques to interrogate the behavior of soft materials at multiple length and time scales. We focus on understanding and predicting interfacial behavior, studying both synthetic (e.g., nanoparticles, liquid crystals, and peptides) and biological (e.g., proteins, lipids, and biosurfactants) soft materials in close collaboration with experimentalists. Current research areas are detailed below.

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Integrating molecular simulations and machine learning for soft materials design

An outstanding challenge – and opportunity – for soft materials design is the large set of parameters that can be manipulated synthetically. For example, surface coatings can be created from varying mole fractions of ligands whose physical and chemical properties are dictated by a wide selection of functional groups. Moreover, macroscopic behavior is highly sensitive to these choices; for example, the cellular uptake of nanoparticles depends strongly on particle surface charge and size. Developing design rules for these systems is challenging due to the difficulty in experimentally screening material compositions or modeling long-timescale processes with chemical accuracy. We are developing methods to combine molecular simulations with machine learning techniques to quantitatively predict experimental observables and guide the exploration of high-dimensional design spaces. This approach enables the rapid screening and design of soft materials for applications in drug delivery and catalysis.


Group members working in this area: Alex Chew, Atharva Kelkar, Amy Qin
Recent papers in this area:

  • S. Qin, T. Jin, R. C. Van Lehn*, and V. M. Zavala*, “Predicting surfactant critical micelle concentrations using graph convolutional neural networks.” In preparation.
  • A. K. Chew, S. Jiang, W. Zhang, V. M. Zavala, and R. C. Van Lehn. “Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics and convolutional neural networks.” Chemical Science, 2020, 11, 12464-12476. [Link]
  • A. S. Kelkar, B. C. Dallin, and R. C. Van Lehn. “Predicting interfacial hydrophobicity by learning spatiotemporal features of interfacial water structure: Combining molecular dynamics simulations with convolutional neural networks.” Journal of Physical Chemistry B, 2020, 124 (41), 9103-9114. [Link]

Multiscale modeling of nanoparticle interactions with lipid bilayers

Synthetic nanoparticles <10 nm in diameter are promising materials for biomedical applications including drug delivery and biosensing. These applications require understanding nanoparticle interactions with the lipid bilayer – the primary structural component of the cell membrane – which dictate critical outcomes including cellular uptake and cytotoxicity. Nanoparticle-bilayer interactions are challenging to predict because they depend on processes occurring at multiple length scales, such as contact between nanoparticle surface components and lipids at a <1 nm length scale and the deformation of the bilayer at ~10-100 nm length scales. We perform atomistic and coarse-grained simulations, coupled with advanced sampling techniques, to model nano-bio interactions for a range of experimentally accessible nanoparticles. These simulations can guide the design of nanoparticles that bypass the cell membrane or assemble on the cell surface for applications in drug delivery and photothermal therapy.


Group members working in this area: Alex Chew, Jonathan Sheavly
Recent papers in this area:

  • C. A. Lochbaum, A. K. Chew, X. Zhang, V. M. Rotello, R. C. Van Lehn*, and J.A. Pedersen*. “The lipophilicity of cationic ligands promotes irreversible adsorption of nanoparticles to lipid bilayers.” ACS Nano, accepted.
  • J. K. Sheavly and R. C. Van Lehn. “Bilayer-mediated assembly of cationic nanoparticles adsorbed to lipid bilayers: Insights from molecular simulations.” AIChE Journal, 2020, 66 (12), e16993. [Link]
  • J. K. Sheavly, J. A. Pedersen, and R. C. Van Lehn. “Curvature-driven adsorption of cationic nanoparticles to phase boundaries in multicomponent lipid bilayers.” Nanoscale 2019, 11, 2767. [Link]

Solvent screening and design for separations and catalysis

Liquid-phase physical and chemical processes are strongly influenced by the solvent composition. For example, the solvent environment can influence reaction selectivity by stabilizing reactants, transition states, or products while also impacting downstream process considerations such as product separation. Given the effectively infinite number of possible single- and multicomponent solvent systems, identifying optimal solvent systems for a process is not feasible via trial-and-error experimentation. We are developing and applying high-throughput molecular and thermodynamic models to design solvent systems that simultaneously promote the production of desired products (e.g., by chemical reactions or physical solubilization processes) and enable their economical separation and recovery (e.g., by liquid-liquid extraction). We are applying these protocols to design solvent systems for the recycling of waste plastics and the catalytic valorization of biomass.


Group members working in this area: Jianping Li, Zeynep Sumer, Panzheng Zhou
Recent papers in this area:

  • T. W. Walker, N. Frelka, Z. Shen, A. K. Chew, J. Bannick, S. Grey, J. A. Dumesic, R. C. Van Lehn, and G. W. Huber. “Recycling of multilayer plastic packaging materials by solvent-targeted recovery and precipitation.” Science Advances, 2020, 6 (47), eaba7599. [Link]
  • A. K. Chew*, T. W. Walker*, Z. Shen, B. Demir, L. Witteman, J. Euclide, G. W. Huber, J. A. Dumesic, and R. C. Van Lehn. “Effect of mixed-solvent environments on the selectivity of acid-catalyzed dehydration reactions.” ACS Catalysis 2020, 10, 1679-1691. [Link]
  • Z. Shen and R. C. Van Lehn. “Solvent selection for the separation of lignin-derived monomers using the Conductor-like Screening Model for Real Solvents.” Industrial & Engineering Chemistry Research 2020, 59 (16), 7755-7764. [Link]

Tuning solvent-mediated interactions at functionalized interfaces

In liquid environments, materials can experience indirect solvent-mediated interactions that emerge from the collective behavior of interfacial solvent molecules. For example, the disruption of water molecules near nonpolar interfaces leads to an attractive hydrophobic interaction that drives diverse behaviors in aqueous environments, including self-assembly, protein binding, and polymer collapse. Similar interactions can also emerge in organic or mixed-solvent systems due to the disruption of solvent structure near an interface. Solvent-mediated interactions are difficult to predict, particularly for materials with spatially varying physical and chemical properties that cooperatively influence the structure and behavior of interfacial solvent molecules. We are developing simulation methods to understand how solvent-mediated interactions depend on the properties of functionalized interfaces and applying this insight to guide the design of surface coatings and biomaterials.


Group members working in this area: Brad Dallin, Atharva Kelkar
Recent papers in this area:

  • B. C. Dallin and R. C. Van Lehn. “Spatially heterogenous water properties at disordered surfaces decrease the hydrophobicity of nonpolar self-assembled monolayers.” Journal of Physical Chemical Letters 2019, 10, 3991-3997. [Link]
  • B. C. Dallin, H. Yeon, A. R. Ostwalt, N. L. Abbott, and R. C. Van Lehn. “Molecular order affects interfacial water structure and temperature-dependent hydrophobic interactions between nonpolar self-assembled monolayers.” Langmuir 2019, 35(6), 2078-2088. [Link]
  • J. H. Dwyer, Z. Shen, K. R. Jinkins, W. Wei, M. S. Arnold, R. C. Van Lehn, and P. Gopalan. “Solvent-mediated affinity of polymer-wrapped single-walled carbon nanotubes for chemically modified surfaces.” Langmuir 2019, 35, 12492-12500. [Link]

Understanding long-timescale transport across soft interfaces

The hydrophobic core of the lipid bilayer regulates transport into and out of the cell by inhibiting the passive diffusion of polar and charged molecules. However, bilayer permeability can substantially increase due to perturbations of the bilayer structure. For example, some transmembrane proteins can increase the permeability of charged molecules by stabilizing transient bilayer defects. This behavior is important to biological transport processes and can impact the behavior of synthetic materials (e.g., bilayer-active peptides) but is challenging to study computationally because transport is a long-timescale process. We are developing and applying advanced sampling techniques to uncover design rules for biomolecule transport across lipid bilayers with application to bacterial signaling, membrane protein production, and the behavior of engineered peptides. We are applying related computational tools to design sensors that detect the transport of environmental pollutants across liquid crystal thin films.


Group members working in this area: Samartha Patel, Jonathan Sheavly
Recent papers in this area:

  • T. Jin, S. J. Patel, and R. C. Van Lehn. “Molecular simulations of lipid membrane partitioning and translocation by bacterial quorum sensing modulators.” PLoS ONE, 2021, 16:2, e0246187. [Link]
  • J. K. Sheavly, J. I. Gold, M. Mavrikakis, and R. C. Van Lehn. “Molecular simulations of analyte partitioning and diffusion in liquid crystal sensors.” Molecular Systems Design & Engineering 2019, 5, 304-316. [Link]
  • S. J. Patel and R. C. Van Lehn. “Characterizing the molecular mechanisms for flipping charged peptide flanking loops across a lipid bilayer.” The Journal of Physical Chemistry B 2018, 122(45), 10337-10348. [Link]