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

协作研究：整合模拟、实验和机器学习来理解和设计疏水相互作用

• 批准号: 2245375
• 负责人: Reid Van Lehn
• 金额: $ 29.7万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245375&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Simulations; Experiments

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.

水和疏水（疏水）材料之间的相互作用是广泛的化学过程工业挑战的中心。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。在工业和生物技术背景下，疏水相互作用发生在具有极性和非极性基团相邻界面的系统中，但是对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，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空白，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解决紧迫社会需求的一系列材料，包括用于可持续工艺的设计表面活性剂、用于水净化的膜和用于生物制药分离的吸附材料。这个合作研究项目将为培养研究生提供出色的机会，以数据为中心的方法，将实验和计算相结合，随后将利用这些方法开发一套“通过实验和模拟学习”模块，通过说明分子模拟如何提供对宏观现象的洞察，吸引K-12和公众观众。两个研究团队将在实验室中接待来自STEM中代表性不足的群体的本科生，并在REU项目中共同教授讲座，以展示计算和实验整合带来的机会。本项目旨在为化学非均相界面上疏水相互作用的热力学设计建立新的理解和规则。提出的研究将结合分子动力学模拟、实验和机器学习两种模型系统，探索解决极性基团的特性如何影响非极性域附近的水结构的基本问题，以及如何利用这种对水结构的扰动来设计限制分子（~1 nm）和宏观长度尺度下疏水相互作用的热力学。模型系统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层组成，之所以被选择，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允许分析由于极性和带电基团的存在而导致的水结构的尺度依赖性变化，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学过程工业背景下的工程疏水相互作用的分子设计规则。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。