Collaborative Research: Integrating Simulations, Experiments, and Machine Learning to Understand and Design Hydrophobic Interactions

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The interactions between water and hydrophobic (water avoiding) materials are at the center of a wide range of chemical process industry challenges. Water continues to replace organic solvents in industrial processes, leading the way to a circular economy based on manufacturing renewable chemicals. Hydrophobic interactions in industrial and biotechnological contexts occur in systems with interfaces that have polar and nonpolar groups in proximity, but little is understood regarding how the presence of specific polar groups, when placed adjacent to nonpolar domains, impact hydrophobic interactions and the associated dynamic structure of water. This project will use an innovative combination of experiments, molecular-level computer simulations, and data-centric methods to address this gap in knowledge and arrive at new design rules for hydrophobic interactions. These design rules will be suitable for deployment in a range of materials that address pressing societal needs, including designer surfactants for sustainable processes, membranes for water purification, and sorbent materials for biopharmaceutical separations. This collaborative research program will provide outstanding opportunities for training graduate students in data-centric approaches that integrate experiments and computation, which will subsequently be leveraged to develop a set of “learning through experiment and simulation” modules to engage K-12 and public audiences by illustrating how molecular simulations can provide insight into macroscopic phenomena. Both research teams will host undergraduates from groups underrepresented in STEM in their laboratories and co-teach lectures in REU programs in both institutions to demonstrate opportunities that emerge from the integration of computation and experiments.This project seeks to establish new understanding and rules for the thermodynamic design of hydrophobic interactions at chemically heterogeneous interfaces. The proposed research will combine molecular dynamics simulations, experiments, and machine learning for two model systems to explore fundamental questions addressing how the identity of polar groups impacts water structure near nonpolar domains, and how such perturbations to water structure can be used to design the thermodynamics of hydrophobic interactions at limiting molecular (~1 nm) and macroscopic length scales. The model systems, which comprise molecular surfactants and self-assembled monolayers formed from mixtures of polar and nonpolar ligands, were selected because they can be precisely manipulated, functionalized with polar groups ubiquitous in biological materials and industrial systems (ensuring broad relevancy), and provide access to thermodynamic information that has broad applicability. Studying these two limiting length scales will permit analysis of scale-dependent changes to water structure due to the presence of polar and charged groups and how these changes affect thermodynamic signatures of hydrophobic interactions. The outcome of the work will be molecular design rules for engineering hydrophobic interactions in diverse chemical process industry contexts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.