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: 2245376
• 负责人: Nicholas Abbott
• 金额: $ 35.64万
• 依托单位: Cornell University
• 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=2245376&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 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.

水和疏水（避免水）材料之间的相互作用是各种化学过程工业挑战的中心。水继续取代工业过程中的有机溶剂，引领基于制造可再生化学品的循环经济。在工业和生物技术背景下的疏水相互作用发生在具有极性和非极性基团接近的界面的系统中，但很少有人了解特定极性基团的存在，当被放置在非极性域附近时，如何影响疏水相互作用和水的相关动态结构。该项目将使用实验，分子水平的计算机模拟和以数据为中心的方法的创新组合，以解决这一知识差距，并达到疏水相互作用的新设计规则。这些设计规则将适用于部署在一系列满足紧迫社会需求的材料中，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剂材料。这项合作研究计划将为培训研究生提供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宏观现象的洞察力来吸引K-12和公众。这两个研究团队将在各自的实验室接待来自STEM领域代表性不足的本科生，并在两个机构的REU项目中共同教授讲座，以展示计算与实验相结合所带来的机会。该项目旨在为化学异质界面疏水相互作用的热力学设计建立新的理解和规则。拟议的研究将结合联合收割机分子动力学模拟，实验和机器学习的两个模型系统，探讨基本问题，解决极性基团的身份如何影响水结构附近的非极性域，以及如何这样的扰动水结构可以用来设计在限制分子（~1 nm）和宏观长度尺度的疏水相互作用的热力学。模型系统，其中包括分子表面活性剂和自组装单分子层形成的极性和非极性配体的混合物，被选中，因为它们可以被精确地操纵，功能化与生物材料和工业系统中无处不在的极性基团（确保广泛的相关性），并提供访问热力学信息，具有广泛的适用性。研究这两个限制长度尺度将允许分析尺度相关的变化，水结构由于极性和带电基团的存在，以及这些变化如何影响疏水相互作用的热力学特征。该奖项反映了NSF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。