Collaborative Research: EAGER: ADAPT: Charting the Space of Material Microstructures with Artificial Intelligence

合作研究：EAGER：ADAPT：用人工智能绘制材料微观结构的空间

• 批准号: 2232968
• 负责人: Jeremy Mason
• 金额: $ 10.68万
• 依托单位: University of California-Davis
• 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232968&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; ADAPT; Charting

NONTECHNICAL SUMMARYOne of the fundamental principles of materials science is that material properties are determined by structure. The microstructure, or the internal structure at the micron scale (one millionth of a meter), is specifically identified as being essential to physical properties including the mechanical strength, ductility, and fracture toughness of ceramic and metal components used in construction, manufacturing, and other industrial applications. Since it is possible and even likely that microstructures of exceptional materials of the future will not resemble those of conventional materials, a key challenge in material development is the determination of the all feasible microstructures. This award will support research and education activities that will adapt leading methods in data science and machine learning to address this challenge. Specifically, the research will integrate expert knowledge about physically-meaningful comparisons of microstructures into machine learning models to provide a systematic method for exploring possible microstructures, both previously realized and unrealized ones. This approach is also expected to improve the accuracy and efficiency of models to predict material properties on the basis of microstructure alone. This award will create opportunities for undergraduate and graduate students in mathematics and materials science to be cross-trained between disciplines and institutions. The mathematics students will benefit from interactions with materials scientists and vice versa. In addition, the PIs will create user-friendly software to make the proposed algorithms widely accessible, both to researchers and industrial practitioners and to individuals in other disciplines studying structures with similar geometry.TECHNICAL SUMMARYThis award supports the development of a new representation of microstructure state space that balances the need to retain enough information to predict physical properties of materials with the requirement that it be sufficiently low-dimensional and general to serve as the basis for a flexible materials database. The concept of computational materials design relies on the underlying ideas that (i) a microstructure can be represented as a point in an appropriate state space, (ii) this state space specifies enough information to accurately predict material properties, and (iii) optimization routines could be used to search the state space for microstructures with desirable properties. In this research program, the PIs will adapt leading methods in data science and machine learning to discover a practicable representation of this microstructure space applicable to a variety of material classes. Formally, the feature extraction, classification, and interpretability of experimental microstructure data will be improved by achieving three aims. Aim I: Define and implement physically-motivated metrics to evaluate the similarity of microstructures on both local and global scales. Aim II: Leverage the local metric with manifold learning to construct a coordinate representation for the space of windows, and apply these coordinates in conjunction with new machine learning techniques to to predict material properties. Aim III: Learn a coordinate representation for the space of window distributions and use it to construct a proof-of-concept microstructure database.This award will create opportunities for undergraduate and graduate students in mathematics and materials science to be cross-trained between disciplines and institutions. The mathematics students will benefit from interactions with materials scientists and vice versa. In addition, the PIs will create user-friendly software to make the proposed algorithms widely accessible, both to researchers and industrial practitioners and to individuals in other disciplines studying structures with similar geometry.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.

材料科学基本原理的非技术总结是材料特性取决于结构。微观结构或微米尺度上的内部结构（一百万米）被专门确定为对物理特性所必需的，包括陶瓷和金属组件的机械强度，延性和裂缝韧性，用于建筑，制造业和其他工业应用。由于未来的特殊材料的微观结构是可能的，甚至可能与常规材料的微观结构相似，因此材料开发的关键挑战是确定所有可行的微观结构。该奖项将支持研究和教育活动，以适应数据科学和机器学习中的领先方法，以应对这一挑战。具体而言，该研究将将有关微观结构的物理性比较的专家知识整合到机器学习模型中，以提供一种系统的方法来探索可能的微观结构，包括以前已实现和未实现的微观结构。还希望这种方法可以提高模型的准确性和效率，以仅基于微观结构来预测材料特性。该奖项将为学科和机构之间的数学和材料科学学科和材料科学领域的本科生和研究生创造机会。数学学生将从与材料科学家的互动中受益，反之亦然。此外，PI还将创建用户友好的软件，以使所提出的算法可为研究人员和工业实践者以及其他学科中的个人进行广泛访问，以相似的几何形状研究结构。技术摘要颁发奖项，该奖项支持以下需要保持较低的信息，以使材料的新元素保持不变，从而可以保持材料，以至于均匀的材料，以至于均匀的材料，以衡量材料，以至于均衡材料，以至于均匀的材料，该奖项的材料均可构成材料，以至于它是估计的材料。作为灵活材料数据库的基础。计算材料设计的概念取决于（i）可以将微观结构表示为适当状态空间中的一个点，（ii）此状态空间指定足够的信息来准确预测材料属性，并且（iii）优化程序可用于搜索具有理想特性的微观结构。在该研究计划中，PI将适应数据科学和机器学习中的领先方法，以发现适用于各种材料类的微观结构空间的可行表示。正式地，通过实现三个目标，将改善实验微观结构数据的特征提取，分类和可解释性。 AIM I：定义和实施物理动机的指标，以评估本地和全球尺度上微观结构的相似性。 AIM II：利用流形学习的本地指标来构建窗户空间的坐标表示，并将这些坐标与新的机器学习技术结合使用以预测材料属性。 AIM III：学习窗口分布空间的坐标表示，并使用它来构建概念验证的微观结构数据库。该奖项将为学科和机构之间的学科和材料科学领域的本科生和研究生创造机会。数学学生将从与材料科学家的互动中受益，反之亦然。此外，PI将创建用户友好的软件，以使提出的算法可为研究人员和工业从业人员以及其他学科中的个人提供广泛访问的算法，这些算法以类似的几何形式研究结构。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是值得通过评估的。