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.

非技术性SUMMARY聚合物是由重复的亚基组成的非常长的链状分子。根据其基本的化学和结构，某些聚合物是已知的“刺激响应性”，因为它们可以根据环境条件大幅改变其特性。因此，这种聚合物可以用来创造“智能”的适应性材料，对温度、酸度和压力等触发因素做出反应，改变功能。然而，尽管人们天生就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的应用是难以捉摸的。这一职业奖的重点是开发预测工具，以促进对刺激响应性聚合物的理解和设计。特别是，该研究小组的目标是通过实施“环境感知”模拟算法来提高当前分子建模策略的准确性。此外，研究小组将利用机器学习的力量来指导基于刺激反应的聚合物结构形成。通过这些活动产生的知识和方法将为未来各种应用领域的聚合物设计活动奠定基础，如智能传感、诊断、药物输送、涂层、服装和净化。这项研究的主要目标和方法源于现代计算的力量，也将补充许多教育和培训活动。研究活动将与针对高中生的“普林斯顿实验室学习计划”相结合，通过有针对性的外展努力，在青年和代表性不足的群体中促进将计算用于工程的兴趣。此外，首席研究员将加强和/或开发两门以机器学习和材料设计为基础的工程选修课，强调与领域相关的例子和应用，以促进理解和利用。同样，该团队还将开发“手册”式的指南，说明分子建模的常见陷阱和最佳实践，这些都是普遍遇到但很少正式教授的。所有开发的教育产品都将向公众开放，以扩大这些材料的覆盖范围和用途。从长远来看，这些努力将滚雪球般扩大到更广泛的项目中，将分子建模和机器学习更坚定地整合到传统的工程课程中，并为毕业生做好准备，以满足技术劳动力日益增长的需求。技术总结这一职业奖项的研究将极大地提高设计基于刺激响应性聚合物的“智能”功能材料的能力。刺激响应型聚合物是一种大分子，可以根据特定的触发/刺激来调整其功能，因此可以用于许多应用，如传感、机器人、药物输送和分离。定制刺激响应型聚合物的化学和结构以引发特定的、所需的功能反应的前景非常诱人；然而，目前还没有强大的、可预测的框架来在高维参数空间中为其设计提供信息。研究团队将解决目前阻碍刺激响应型聚合物计算指导设计的关键技术瓶颈。特别是，主要项目包括(I)用表达的粗粒势能函数对热敏聚合物进行多尺度建模，(Ii)在隐含溶剂框架内的非均匀环境中对聚合物动力学进行建模，以及(Iii)利用机器学习来控制聚合物材料的紧急结构性质。总而言之，这些活动将为在设计新型智能纳米材料期间利用现代计算技术提供基础。作为这一职业奖励的一部分，教育和培训活动将解决为下一代劳动力培养受训人员的迫切需要。分子建模和机器学习的技能在学术和工业环境中都越来越重要，但这些技能很少直接纳入物理科学家和工程师的课程。因此，受训人员往往在传统教学环境之外，在脱离其预期应用领域的环境中学习这种技能；这推迟了劳动力的融入和创新。首席研究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门工程学选修课，(Ii)创建关于分子建模实用考虑的教育辅助材料，以及(Iii)扩大对高中生培训计划的参与，突出计算在工程学中的可见性和实用性。这些活动将在短期内加强对年轻专业人员的重要技术培训，并更坚定地将建模/数据科学纳入未来的工程课程。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。