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.

非技术摘要材料科学的基本原理之一是材料特性由结构决定。显微结构或微米级（百万分之一米）的内部结构被特别确定为对于物理性能至关重要，包括用于建筑、制造和销售的陶瓷和金属部件的机械强度、延展性和断裂韧性。其他工业应用。由于未来特殊材料的微观结构有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，这些活动将采用数据科学和机器学习的领先方法来应对这一挑战。具体来说，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，为探索可能的微观结构（包括先前实现的和未实现的微观结构）提供系统方法。这种方法还有望提高模型的准确性和效率，以仅根据微观结构来预测材料特性。该奖项将为数学和材料科学领域的本科生和研究生创造在学科和机构之间进行交叉培训的机会。数学专业的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，使所提出的算法能够被研究人员和工业从业者以及研究具有相似几何形状的结构的其他学科的个人广泛使用。技术摘要该奖项支持开发一种新的微观结构状态表示形式该空间平衡了保留足够信息来预测材料物理特性的需要，以及它足够低维和通用以作为灵活材料数据库的基础的要求。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一个点，(ii) 该状态空间指定足够的信息来准确预测材料特性，以及 (iii) 优化例程可用于在状态空间中搜索具有所需特性的微观结构。在该研究项目中，PI 将采用数据科学和机器学习的领先方法，以发现适用于各种材料类别的微观结构空间的实用表示。形式上，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改进。目标 I：定义并实施物理驱动的指标，以评估局部和全局尺度上微观结构的相似性。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来预测材料属性。目标三：学习窗口分布空间的坐标表示，并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领域的本科生和研究生创造机会，让他们在学科和领域之间进行交叉培训。机构。数学专业的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，使所提出的算法能够被研究人员和工业从业者以及研究具有相似几何形状的结构的其他学科的个人广泛使用。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。