Collaborative Research: EAGER: ADAPT: Machine Learning Thermodynamic Speed Limits for Dynamic Materials

协作研究：EAGER：ADAPT：动态材料的机器学习热力学速度限制

• 批准号: 2231470
• 负责人: Igor Mezic
• 金额: $ 5万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231470&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; ADAPT; Machine

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry and the Office of Multidisciplinary Activities (OMA), Jason R. Green of the University of Massachusetts Boston and Igor Mezic of the University of California-Santa Barbara will work to advance the fundamental understanding of how to regulate transformations of energy in chemically-active materials. To benefit applications across the energy, biomedical, and healthcare industries, it is necessary to design materials that execute functional behaviors on chosen time scales. Predicting these dynamical processes requires new theoretical methods to simultaneously navigate their large design space, control the timing of dynamical functions, and regulate the dissipation of energy. This project aims to address this need by combining machine learning and physical theory to create new methods for the design and optimization of functional materials with tailored optical, mechanical, or photonic properties on finely tuned time scales. Coupled to these scientific aims, the project will collaboratively create an active learning curriculum to teach chemists the statistical techniques of data science and contribute to the training of a diverse AI(artificial intelligence)-aware workforce.Materials chemistry now aims to create dissipative materials that function dynamically, forming patterns and generating work on finite time scales. Recent experiments have taken the first steps to identify chemical systems that drive transient formation of materials structures. However, further progress requires navigating their large design space and regulating flows of energy from the nanoscale up. Machine learning has potential to guide experiments and accelerate this process but is not yet able to optimize the energy efficiency and timed delivery of structure. The proposed project will address this challenge by strategically incorporating recent advances in statistical mechanics into predictive models from machine learning. The specific objectives will be to (i) construct the data-driven dynamics of active hydrogels with techniques from AI, (ii) show that thermodynamic speed limits can be cast as optimally predictive models in machine learning, and (iii) implement these speed limits as design principles for maximizing yield and minimizing dissipation. The project includes dedicated activities to develop strength in STEM (science, technology, engineering and mathematics) at the intersection of data science and theoretical chemistry and to broaden participation in STEM through targeted outreach.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.

在化学理论，模型和计算方法（CTMC）方案的支持下，马萨诸塞大学波士顿大学的杰森·R·格林（Jason R.为了使能源，生物医学和医疗保健行业的应用有益，有必要设计以所选时间尺度执行功能行为的材料。预测这些动力学过程需要新的理论方法，以同时浏览其大型设计空间，控制动态功能的时机并调节能量的耗散。该项目旨在通过结合机器学习和物理理论来满足这一需求，从而在精细调整的时间尺度上创建具有量身定制的光学，机械或光子性能的功能材料的新方法。与这些科学目标相结合，该项目将共同创建一个积极的学习课程，以教化学家的数据科学的统计技术，并有助于培训多样化的AI（人工智能）-Aware-Aware-Aware-Aware Workerce.Materalials Chemistres Chemistry现在旨在创造出动态的耗散材料，形成动态的工作，并在有限的时间范围内进行动态工作和生成工作。最近的实验已经采取了第一步，以识别驱动材料结构瞬时形成的化学系统。但是，进一步的进展需要导航其较大的设计空间，并从纳米级的升级中调节能量流。机器学习具有指导实验并加速此过程的潜力，但无法优化能源效率和定时交付结构。拟议的项目将通过将统计力学的最新进展纳入机器学习的预测模型中，以应对这一挑战。特定的目标是（i）使用AI的技术构建活性水凝胶的数据驱动动力学，（ii）表明，热力学速度限制可以作为机器学习中的最佳预测模型施放，并且（iii）将这些速度限制作为设计原理，以最大化产量并最大程度地减少耗散。该项目包括在数据科学与理论化学的交集中开发STEM（科学，技术，工程和数学）力量的专门活动，并通过有针对性的外展来扩大参与STEM的参与。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的功能和广泛的影响来评估NSF的法定任务，并被认为是值得的。