Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery

合作研究：I-AIM：用于多尺度材料发现的可解释增强智能

• 批准号: 2404816
• 负责人: Wei Chen
• 金额: $ 38.79万
• 依托单位: SUNY at Buffalo
• 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: 2023-10-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2404816&HistoricalAwards=false
• 关键词: Collaborative; Research; AIM; Interpretable; Augmented

The ability to model, predict, and improve the mechanical performance of engineering materials such as polymers, composites, and alloys can have a significant impact on manufacturing, with important economic and societal benefits. As advanced computational algorithms and data science approaches become available, they can be harnessed to disrupt the current approaches to materials modeling, and allow for the design and discovery of new high-strength, high-performance materials for manufacturing. Bringing together multidisciplinary teams of researchers can maximize the impact of these new tools and techniques. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) award supports the conceptualization of an Institute to develop novel data science methods, address fundamental scientific questions of Materials Engineering and Manufacturing, and build such multidisciplinary teams. The project will apply novel data science methods to advance the analysis of large sets of structural data of composite materials and alloys from the atomic scale to correlate with and predict mechanical properties. The methods are based on machine learning techniques and uncertainty quantification, and will help uncover underlying structural features in the materials that determine the properties and performance. The methods and results will help accelerate the development of ultra-high strength and lightweight carbon-based composites for aerospace applications, and multi-element superalloys for more durable engine parts, by navigating in the large possible design space and providing faster predictions than experiments and traditional simulation methods. The project will also lead to new methods and computational algorithms that will become publicly available. The investigators will train graduate and undergraduate students from various disciplines with a focus on engaging women and minorities in STEM fields, develop short courses that integrate novel Materials Science and Engineering applications and Data Science methods, and foster vertical integration of interdisciplinary research from undergraduate students to senior scientists.This project aims at building an effective and interpretable learning framework for materials data across scales to solve a major challenge in current data-driven materials design. The combined Materials Science and Data Science approaches will synergistically contribute to the development and use of interpretable and physics-informed data science methodologies to gain new understanding of mechanical properties of polymer composites and alloys, with the potential to be expanded into different property sets and different systems. The PIs will utilize available data efficiently through combination with physical rules and prior knowledge, to develop an interpretable augmented intelligent system to learn principles behind the association of input structures with material properties with uncertainty quantification. The interconnected tasks involve the (1) collection and curation of large amounts of computational and experimental data for polymer/carbon nanotube composites and alloys from open data sources and targeted calculations and experiments, (2) the development of geometric and topological methods incorporating physical principles to generate a better, more sensitive low-dimensional representation of the multidimensional data and characterize the parameter space related to mechanical properties, (3) the development of a Bayesian deep reinforcement learning framework to generate interpretable knowledge graphs that depict the relational knowledge among physical quantities with uncertainty quantification, and (4) the prediction of mechanical properties to reveal design principles to improve materials performance, evaluate and validate the methods, and develop software for dissemination. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation.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.

建模，预测和改善工程材料（例如聚合物，复合材料和合金）的机械性能的能力可以对制造产生重大影响，并具有重要的经济和社会利益。随着先进的计算算法和数据科学方法的可用，可以利用它们破坏当前的材料建模方法，并允许设计和发现新的高强度高强度，高性能的材料。将多学科的研究人员组成的团队可以最大程度地提高这些新工具和技术的影响。这项利用数据革命机构的科学与工程研究研究机构（HDR-I-DIRSE奖）支持了一个开发新型数据科学方法的研究所的概念化，解决了材料工程和制造的基本科学问题，并建立了此类多学科团队。该项目将采用新颖的数据科学方法来推进对复合材料和合金的大量结构数据的分析，从原子量表中，与机械性能相关并预测。这些方法基于机器学习技术和不确定性量化，并将有助于发现确定属性和性能的材料中的基本结构特征。这些方法和结果将有助于加快用于航空航天应用的超高强度和轻巧的碳基复合材料，以及通过在较大的可能的设计空间中导航并提供比实验和传统仿真方法的更快的预测，以使其更耐用的发动机零件进行多元素超合金。该项目还将导致新的方法和计算算法将公开可用。调查人员将培训来自各个学科的毕业生和本科生，着重于使妇女和少数群体参与STEM领域，开发简短的课程，这些课程将新颖的材料科学和工程应用程序和数据科学方法整合在一起，并促进跨学科研究的垂直整合，从本科生到高级科学家的跨学科研究旨在为材料提供有效的材料数据范围来构建材料数据，以促进材料数据范围内的数据范围，并跨越了材料的范围。合并的材料科学和数据科学方法将协同作用有助于开发和使用可解释和物理信息的数据科学方法，以获得对聚合物复合材料和合金的机械性能的新理解，并有可能扩展到不同的属性集和不同的系统。 PI将通过结合物理规则和先验知识来有效地利用可用的数据，以开发可解释的增强智能系统，以了解输入结构与具有不确定性量化的材料属性的关联背后的原理。相互联系的任务包括（1）收集和策划聚合物/碳纳米管复合材料和来自开放数据源的聚合物/碳纳米管复合材料和合金的大量计算和实验数据，以及目标计算和实验，（2）开发几何学和拓扑方法，以生成更好，更敏感的低量化属性的物理空间，以实现更敏感的多个特性，并具有多个特性的代表性，并具有多个特性的代表性，并具有多个特性的代表性，并且（3）开发贝叶斯深钢筋学习框架，以生成可解释的知识图，这些图形描绘了具有不确定性量化的物理数量之间的关系知识，以及（4）机械性能的预测以揭示设计原理，以提高材料性能，评估和验证方法，并验证这些方法，并开发用于发散的软件。该项目是国家科学基金会利用数据革命（HDR）的大创意活动的一部分，并由民用，机械和制造业创新部共同资助。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。