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

在化学系化学理论、模型和计算方法（CTMC）项目和多学科活动办公室（OMA）的支持下，马萨诸塞大学波士顿分校的Jason R. Green和加州大学圣巴巴拉分校的Igor Mezic将致力于推进对如何调节化学活性材料中的能量转换的基本理解。为了使能源、生物医学和医疗保健行业的应用受益，有必要设计在选定的时间尺度上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来同时导航其大的设计空间，控制动力学函数的时间，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解决这一需求，为设计和优化功能材料创造新的方法，这些材料在精细调整的时间尺度上具有定制的光学、机械或光子特性。结合这些科学目标，该项目将共同创建一个积极的学习课程，向化学家教授数据科学的统计技术，并为培养多样化的人工智能（人工智能）意识劳动力做出贡献。材料化学现在的目标是创造耗散材料，这些耗散材料可以动态地发挥作用，在有限的时间尺度上形成模式并产生工作。最近的实验已经迈出了第一步，以确定驱动材料结构瞬态形成的化学系统。然而，进一步的进展需要导航他们的大设计空间和调节从纳米尺度向上的能量流。机器学习具有指导实验和加速这一过程的潜力，但尚未能够优化能源效率和结构的定时交付。拟议的项目将通过战略性地将统计力学的最新进展纳入机器学习的预测模型来应对这一挑战。具体目标将是：(i)利用人工智能技术构建活性水凝胶的数据驱动动力学，（ii）表明热力学限速可以作为机器学习中的最佳预测模型，以及（iii）将这些限速作为最大化产量和最小化耗散的设计原则实施。该项目包括专门的活动，在数据科学和理论化学的交叉领域发展STEM（科学、技术、工程和数学）的实力，并通过有针对性的外展扩大STEM的参与。该奖项反映了美国国家科学基金会的法定使命，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。

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.