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: 2232967
• 负责人: Benjamin Schweinhart
• 金额: $ 19.24万
• 依托单位: George Mason University
• 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=2232967&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.

材料科学的基本原理之一是材料的性质是由结构决定的。微观结构或微米级（百万分之一米）的内部结构被特别确定为对建筑，制造和其他工业应用中使用的陶瓷和金属部件的物理性能（包括机械强度，延展性和断裂韧性）至关重要。由于未来特殊材料的微观结构可能甚至很可能与传统材料的微观结构不同，因此材料开发中的一个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的领先方法来应对这一挑战。具体而言，该研究将把有关微观结构的物理意义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，包括以前实现的和未实现的微观结构。这种方法也有望提高模型的准确性和效率，仅根据微观结构预测材料性能。该奖项将为数学和材料科学的本科生和研究生创造机会，在学科和机构之间进行交叉培训。数学学生将受益于与材料科学家的互动，反之亦然。此外，PI将创建用户友好的软件，使拟议的算法广泛使用，技术总结该奖项支持开发一种新的微观结构状态空间表示法，该表示法平衡了保留足够信息以预测材料物理特性的需要和足够低维和通用，以作为柔性材料数据库的基础。计算材料设计的概念依赖于以下基本思想：（i）微观结构可以表示为适当状态空间中的点，（ii）该状态空间指定足够的信息以准确预测材料特性，以及（iii）优化例程可以用于搜索具有所需特性的微观结构的状态空间。在这项研究计划中，PI将采用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别的微结构空间的实用表示。从形式上讲，实验微观结构数据的特征提取，分类和可解释性将通过实现三个目标来提高。目的一：定义和实施物理动机的指标，以评估局部和全局尺度上的微观结构的相似性。目标二：利用流形学习的局部度量来构建窗口空间的坐标表示，并将这些坐标与新的机器学习技术结合应用于预测材料属性。目标三：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。该奖项将为数学和材料科学的本科生和研究生创造机会，在学科和机构之间进行交叉培训。数学学生将受益于与材料科学家的互动，反之亦然。此外，PI将创建用户友好的软件，使提出的算法广泛访问，无论是研究人员和工业从业者和个人在其他学科研究结构与类似的几何形状。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。