CAREER: Multiscale Simulation and Machine Learning for Smart Polymer Design

职业：智能聚合物设计的多尺度仿真和机器学习

• 批准号: 2237470
• 负责人: Michael Webb
• 金额: $ 59.53万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237470&HistoricalAwards=false
• 关键词: CAREER; Multiscale; Simulation; Machine; Learning

NONTECHNICAL SUMMARYPolymers are very long chain-like molecules that consist of repeating subunits. Depending on their underlying chemistry and architecture, certain polymers are known to be “stimuli-responsive” in the sense that they can drastically alter their characteristics based on environmental conditions. Consequently, such polymers could be used to create “smart,” adaptive materials that alter function in response to triggers like temperature, acidity, and stress. However, while it is innately known that the nature and extent of stimuli-response depends on chemical structure, predicting whether any given polymer is suitable for a given application is elusive. This CAREER award focuses on developing predictive tools to facilitate the understanding and design of stimuli-responsive polymers. In particular, the research team aims to improve upon the accuracy of current molecular modeling strategies by implementing “environment-aware” simulation algorithms. Furthermore, the research team will harness the power of machine learning to guide structure formation of polymers based on stimuli-response. The knowledge and methods generated via these activities will set the stage for future campaigns in polymer design across diverse applications, such as smart sensing, diagnostics, drug-delivery, coatings, clothing, and purification. The major goals and methods of the research, which derive from the power of modern computation, will also supplement numerous education and training activities. Research activities will be coupled to “Princeton’s Laboratory Learning Program” for high school students with targeted outreach efforts to catalyze interest in using computation for engineering, across youth and by underrepresented groups. Moreover, the principal investigator will enhance and/or develop two engineering electives predicated on machine learning and materials design, emphasizing domain-relevant examples and applications to accelerate understanding and utilization. In the same vein, the team will also develop “handbook”-style guides that illuminate common pitfalls and best practices for molecular modeling that are ubiquitously encountered but seldom formally taught. All developed educational products will be made publicly accessible to extend the reach and utility of these materials. In the long-term, these efforts will snowball into more expansive projects that more firmly integrate molecular modeling and machine learning into traditional engineering curricula and prepare graduates to meet the ever-increasing demands of the technical workforce. TECHNICAL SUMMARYResearch for this CAREER award will substantially advance capabilities to design “smart” functional materials based on stimuli-responsive polymers. Stimuli-responsive polymers are macromolecules that adapt their functionality in response to exposure to certain triggers/stimuli and can thus be exploited for numerous applications, such as sensing, robotics, drug-delivery, and separations. The prospect of tailoring the chemistry and architecture of a stimuli-responsive polymer to elicit a specific, desired functional response is highly enticing; however, there are no existing robust, predictive frameworks to inform their design in a high-dimensional parameter space. The research team will resolve key technical bottlenecks that currently inhibit computationally guided design of stimuli-responsive polymers. In particular, major projects include (i) multiscale modeling of thermo-sensitive polymers with expressive coarse-grained potential energy functions, (ii) modeling polymer dynamics in inhomogeneous environments within implicit-solvent frameworks, and (iii) leveraging machine learning to control emergent structural properties of polymeric materials. In aggregate, these activities will provide a foundation for modern computational techniques to be exploited during design of novel smart nanomaterials. Educational and training activities as part of this CAREER award will address an urgent need to cultivate trainees for the next-generation workforce. Skills in molecular modeling and machine learning are increasingly relevant in both academic and industrial settings, yet these are rarely integrated directly into curricula for physical scientists and engineers. Consequently, trainees often learn such skills outside of traditional pedagogical environments and in contexts that are divorced from their intended domain of application; this delays integration into and innovation by the workforce. The principal investigator will lead activities that bridge technical gaps, including (i) development of two engineering electives related to machine learning and materials design, (ii) creation of educational aids on practical considerations for molecular modeling, and (iii) expanded participation in training programs for high school students that highlight the visibility and utility of computation in engineering. These activities will provide near-term enhancements in important technical training for young professionals and more firmly ingrain modeling/data science into future engineering curricula.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.

非技术摘要聚合物是由重复亚基组成的很长的链样分子。根据其潜在的化学和结构，某些聚合物在某种意义上可以根据环境条件极大地改变其特征，而某些聚合物具有“刺激性响应性”。因此，这些聚合物可用于创建“智能”的自适应材料，这些材料会因温度，酸度和压力等触发因素而改变功能。但是，尽管天生知道刺激反应的性质和程度取决于化学结构，但预测任何给定的聚合物是否适合给定应用是难以捉摸的。该职业奖的重点是开发预测工具，以促进刺激反应性聚合物的理解和设计。特别是，研究小组旨在通过实施“环境感知”仿真算法来提高当前分子建模策略的准确性。此外，研究团队将利用机器学习的力量来指导基于刺激反应的聚合物的结构形成。通过这些活动生成的知识和方法将为跨潜水员应用程序（例如智能传感，诊断，药物交付，涂料，衣服和净化）等聚合物设计的未来运动奠定舞台。源自现代计算的力量的研究的主要目标和方法也将补充众多的教育和培训活动。研究活动将与具有针对性的外展活动的高中学生相结合，以促进对使用计算进行工程，跨青年和代表性不足的群体的兴趣。此外，主要研究人员将增强和/或开发两种预测机器学习和材料设计的工程选修课，并强调。与域相关的示例和应用程序，以加速理解和利用。同样，团队还将开发“手册”风格的指南，以阐明常见的陷阱和用于分子建模的最佳实践，这些指南无处不在，但很少正式教授。所有开发的教育产品将被公开使用，以扩大这些材料的覆盖范围和实用性。从长远来看，这些努力将滚雪球成更广泛的项目，这些项目首先将分子建模和机器学习整合到传统的工程课程中，并准备毕业生以满足技术劳动力的不断增长的需求。该职业奖的技术摘要研究将大大提高基于刺激反应性聚合物设计“智能”功能材料的能力。刺激响应性聚合物是大分子，可适应其功能，以响应某些触发因素/刺激，因此可以在许多应用中探索，例如敏感性，机器人，药物分解和分离。刺激刺激反应性聚合物的化学和结构以引起特定的，所需的功能反应的前景是高度诱人的。但是，没有现有的强大的预测框架可以在高维参数空间中告知其设计。研究团队将解决目前抑制刺激反应性聚合物的计算指导设计的关键技术瓶颈。特别是，主要项目包括（i）具有表达性粗粒势能函数的热敏感聚合物的多尺度建模，（ii）在隐式溶剂框架内的不均匀环境中建模聚合物动力学，以及（iii）利用机器学习以控制聚合物材料的出现结构特性。总体而言，这些活动将为新型智能纳米材料设计期间探索的现代计算技术提供基础。作为本职业奖的一部分，教育和培训活动将迫切需要为下一代劳动力培养学员。在学术和工业环境中，分子建模和机器学习的技能越来越重要，但是这些技能很少直接整合到物理​​科学家和工程师的课程中。因此，学员经常在传统的教学环境和从其预期的应用领域转移的情况下学习此类技能。这延迟了劳动力的整合和创新。首席研究人员将领导弥合技术差距的活动，包括（i）开发与机器学习和材料设计有关的两种工程选修课，（ii）根据分子建模的实际考虑，创建教育辅助工具，以及（iii）扩大参与培训计划，以强调工程中计算的可视性和计算能力。这些活动将为年轻专业人员的重要技术培训提供近期增强，并将更重要的INRAIN建模/数据科学纳入未来的工程课程中。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来支持的。