CAREER: Data-Driven Systematic Hierarchical Modeling

职业：数据驱动的系统分层建模

• 批准号: 2410514
• 负责人: Jianing Li
• 金额: $ 68.75万
• 依托单位: Purdue 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: 2023-12-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2410514&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Systematic; Hierarchical

Jianing Li of the University of Vermont & State Agricultural College is jointly supported by the Chemical Theory, Models and Computational Methods program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR) to predict the properties of materials using computer simulation models. The question of how to correlate molecular structures to material properties has been central to the chemical sciences. Computer modeling has been invaluable to help answer this fundamental question, but it is still very difficult to predict the properties of larger structures. Current simulation methods often reach their limitations, since they are not able to emulate large systems for long enough times. Dr. Li is now taking advantage of the immense data from molecular simulations previously completed. She is inventing an efficient approach to automatically learn from existing data to build new molecular models. These models are able to decrease the difficulty of simulating the large amount of underlying molecular components. By connecting these models to predictions of material properties (like stability, shape, and size), Dr. Li is using simulation methods to screen natural and man-made polymers for desirable properties and to accelerate the discovery of new materials. The materials designed in this project will be biocompatible, bioactive nanomaterials for numerous applications in sensing, drug delivery, tissue engineering, etc. The project also provides educational opportunities for students at multiple stages of their career development, by training graduate students with an interdisciplinary focus, as well as by encouraging undergraduate students early on to experiment independently with molecular modeling. The educational activities will broaden STEM participation by providing new learning and research opportunities to undergraduate students. Travel awards will be established to encourage underrepresented students in Vermont to attend the Green Mountain Winter Camp alongside their mentors and peers. For future technological advances in soft materials (e.g. to create new programmable, biocompatible nanostructures formed by peptides, DNAs, and organic polymers) it is critical to understand complex self-assembly processes and to be able to accurately predict the resulting structures. Hierarchical modeling represents an invaluable tool to understand such processes, since it can examine and predict (often in greater detail than experiments) how self-assembly occurs at the relevant atomic, nanoscopic, and mesoscopic scales. However, to invent more powerful hierarchical computational methods for the future, universal highly coarse-grained (HCG) force fields in conjugation with effective backmapping methods are needed. To target these challenges, Dr. Li is creating a systematic, data-driven hierarchical (STAIR) methodology for multiscale modeling. STAIR is designed to overcome major drawbacks of currently available methods for systematic coarse graining, which still require substantial human expertise and labor for force field development. Specifically, STAIR replaces expensive fitting processes by innovative neural network algorithms and reduce human efforts in tasks like particle type determination, ad hoc corrections, etc. With the long-term goal to guide the rational design of complex nanomaterials from relatively simple building blocks, the overall objective is to invent hierarchical, adaptive modeling methods to guide the development of peptide- and DNA-based self-assembled nanomaterials.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.

佛蒙特大学与国家农业学院的李·李（Jianing Li）在化学理论，模型和计算方法计划中共同支持化学和既定计划，以刺激竞争研究（EPSCOR），以使用计算机模拟模型来预测材料的性质。如何将分子结构与材料特性相关的问题对于化学科学至关重要。计算机建模非常宝贵，可以帮助回答这个基本问题，但是预测较大结构的特性仍然非常困难。当前的仿真方法通常会达到其局限性，因为它们无法在足够长的时间内模仿大型系统。 Li博士现在正在利用先前完成的分子模拟中的巨大数据。她正在发明一种有效的方法来自动从现有数据中学习以构建新的分子模型。这些模型能够减少模拟大量基础分子成分的难度。通过将这些模型与材料特性的预测（例如稳定性，形状和大小）联系起来，Li博士使用仿真方法来筛选自然和人造聚合物，以了解所需的特性，并加速发现新材料。该项目设计的材料将是生物相容性的，生物活性的纳米材料，用于在感应，药物输送，组织工程等方面进行多种应用。该项目还通过以跨学科的焦点来培训研究生，并通过培训研究生，并通过培训研究生，并通过培训研究生，并通过培训研究生，并通过培训较早的学生来独立地实验，从而为他们的职业发展的多个阶段提供教育机会。 教育活动将通过为本科生提供新的学习和研究机会来扩大STEM的参与。 将建立旅行奖，以鼓励佛蒙特州的代表性不足的学生与他们的导师和同龄人一起参加绿色山区营地。对于未来的技术进步（例如，要创建由肽，DNA和有机聚合物形成的新的可编程，生物相容性的纳米结构），了解复杂的自组装过程并能够准确预测所得的结构，这一点至关重要。层次建模代表了理解此类过程的宝贵工具，因为它可以检查和预测（通常比实验更详细）自组装如何发生在相关的原子，纳米镜和介质量表上。但是，为了为未来发明更强大的分层计算方法，需要与有效的背景方法结合使用通用高度粗粒（HCG）力场。为了针对这些挑战，Li博士正在为多尺度建模创建一种系统的，数据驱动的层次结构（楼梯）方法。楼梯旨在克服目前可用的系统粗砂方法的主要缺点，这些方法仍然需要大量的人类专业知识和劳动力才能进行力场发展。具体而言，楼梯通过创新的神经网络算法替代了昂贵的拟合过程，并减少了人类在诸如粒子类型确定，临时校正等任务中的努力，以指导相对简单的构件的复杂纳米材料的合理设计，从相对简单的构建基础中，总体目标是发明层次，适应性模型的发展，以指导基于自适应的模型和DNA的发展，DNA的纽约和DNA的发展。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。