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

水与疏水(避水)材料之间的相互作用是一系列化工过程工业挑战的核心。在工业过程中，水继续取代有机溶剂，引领着以生产可再生化学品为基础的循环经济。工业和生物技术环境中的疏水相互作用发生在界面附近有极性和非极性基团的系统中，但关于特定极性基团的存在如何影响疏水相互作用和相关的水的动态结构，当放置在非极性结构域附近时，人们知之甚少。该项目将使用实验、分子级计算机模拟和以数据为中心的方法的创新组合来解决这一知识差距，并得出疏水相互作用的新设计规则。这些设计规则将适用于满足迫切社会需求的一系列材料，包括用于可持续工艺的设计型表面活性剂、用于水净化的膜以及用于生物制药分离的吸附材料。这一合作研究计划将为研究生提供以数据为中心的方法的培训机会，这些方法整合了实验和计算，随后将被利用来开发一套“从实验和模拟中学习”模块，通过说明分子模拟如何提供对宏观现象的洞察，来吸引K-12和公众受众。这两个研究团队将在他们的实验室接待STEM中代表性不足的群体的本科生，并在REU项目中共同讲授讲座，以展示从计算和实验的整合中出现的机会。该项目旨在为化学非均质界面上疏水相互作用的热力学设计建立新的理解和规则。这项拟议的研究将结合分子动力学模拟、实验和两个模型系统的机器学习来探索基本问题，解决极性基团的身份如何影响非极性区域附近的水结构，以及如何利用这种对水结构的扰动来设计极限分子(~1 nm)和宏观长度尺度上的疏水相互作用的热力学。模型体系由分子表面活性剂和由极性和非极性配体混合物形成的自组装单分子膜组成，之所以被选中，是因为它们可以被精确操纵，与生物材料和工业系统中普遍存在的极性基团一起功能化(确保广泛的相关性)，并提供具有广泛适用性的热力学信息。研究这两个极限长度尺度将有助于分析由于极性基团和带电基团的存在而导致的水结构随尺度的变化，以及这些变化如何影响疏水相互作用的热力学特征。这项工作的结果将是在不同的化工过程工业环境中工程疏水相互作用的分子设计规则。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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 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.