Elements: Data Driven Autonomous Thermodynamic and Kinetic Model Builder for Microstructural Simulations

元素：用于微观结构模拟的数据驱动自主热力学和动力学模型构建器

• 批准号: 2209423
• 负责人: Katsuyo Thornton
• 金额: $ 60万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209423&HistoricalAwards=false
• 关键词: Elements; Data; Driven; Autonomous; Thermodynamic

Materials with improved properties can dramatically impact sustainability, human welfare, and national prosperity. As an example, a stronger material can reduce the weight of vehicles and can therefore reduce energy consumption and pollution. Properties of materials frequently depend on their microstructures (features in materials at scales of one micrometer to hundreds of micrometers). Thermodynamic free energy (providing the driving force for evolution) and kinetic parameters (providing how quickly the evolution can occur) together govern how a material evolves at the microscale. This project develops algorithms and software that automate the extraction of the thermodynamics and kinetic information using artificial intelligence to enable simulation of microstructure evolution for complex mixtures of metals. The AI-enabled Microstructure Model BuildER (AMMBER) harvests and harnesses data ranging from first-principles calculations, experimental micrographs and associated natural language text, and thermodynamic databases, as well as custom user input. It then produces input to microstructure evolution models that facilitate the fundamental understanding needed to gain control of the microstructure and resulting material properties. The demonstration of its capability is planned for commercially important alloys (nickel-aluminum-based and aluminum-copper-based alloys), as well as the corresponding high-entropy alloys (alloys with five or more components with near equimolar fractions). AMMBER contributes to the software infrastructure for simulation-based material discovery and development within the context of the Material Genome Initiative. Training activities, including training workshops for the community to learn about the software and the theory behind it and integration into the undergraduate and graduate thermodynamics and kinetics courses, provide opportunities for education and professional development. Nickel-aluminum-based and aluminum-copper-based alloys are key materials in the aerospace and automobile industries, and thus the results are expected to have a direct impact on manufacturing. The goal of this project is to develop an artificial intelligence framework for the autonomous determination of input parameters for phase-field models based on a variety of data sources to establish constraints on the model parameters. The AI-enabled Microstructure Model BuildER (AMMBER) leverages automated data-stream pipelines to collect, curate, and tabulate disparate data sources spanning first-principles calculations, experimental micrographs, and associated natural language text, thermodynamic databases, and custom user input. Then, advanced optimization algorithms iteratively optimize phase-field parameters such that the resulting models reproduce known microstructural characteristics (e.g., the phase fraction and characteristic length scale as a function of time). These models can then be used to simulate the microstructural evolution of materials over a range of conditions that are relevant to engineering and manufacturing. The demonstration of AMMBER involves commercially important Ni-Al-based and Al-Cu-based alloys, some of which contain more than five components, leading to a complicated high-dimensional parameter space in which thermodynamic and kinetic model parameters must be optimized. The application to high-entropy alloys, which contain near equimolar amounts of five or more components, provides a ground for new scientific discoveries. By automating the time-consuming initial model parameterization, AMMBER reduces the human bottleneck of materials modeling and paves the way to increased throughput of phase-field simulations. AMMBER complements existing Materials Genome Initiative (MGI) efforts, and it leverages and integrates into existing computational materials research communities built around tools such as open-source phase-field software (PRISMS-PF, MOOSE), an integrated computational materials engineering framework (PRISMS), CALPHAD tools (ESPEI, Thermo-Calc), and a dissemination platform (nanoHUB). The training workshops and integration of the computational tools and research findings into classrooms facilitate community interaction and engagement. Ni-Al-based and Al-Cu-based alloys are key materials in the aerospace and automobile industries, and thus the results are expected to have a direct impact on manufacturing.This proposal receives funds through the Office of Advanced Cyberinfrastructure in the Computer and Information Science and Engineering Directorate and the Division of Materials Research in the Mathematical and Physical Sciences Directorate.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.

具有改善性质的材料会极大地影响可持续性，人类福利和国家繁荣。例如，更强大的材料可以减轻车辆的重量，因此可以减少能耗和污染。材料的性能经常取决于它们的微观结构（材料的特征在一个微米的尺度到数百微米）。热力学自由能（提供进化的驱动力）和动力学参数（提供了进化的速度）共同控制材料如何在微观尺度上进化。该项目开发了算法和软件，可以使用人工智能自动化热力学和动力学信息的提取，以启用微观结构演化的模拟金属复杂混合物。 AI支持的微观结构模型构建器（AMMBER）收获和利用数据，从第一原理计算，实验显微照片和相关的自然语言文本以及热力学数据库以及自定义用户输入。然后，它产生了微观结构演化模型的输入，这些模型促进了控制微观结构和产生的材料特性所需的基本理解。它的能力的演示计划用于商业上重要的合金（基于镍铝的和铝 - 铜的合金），以及相应的高渗透合金（具有五个或更多含量具有近乎等摩尔级数的合金的合金）。 Ammber为在材料基因组计划的背景下为基于仿真的材料发现和开发的软件基础架构做出了贡献。培训活动，包括社区的培训研讨会，以了解该软件及其背后的理论，并将其整合到本科和研究生的热力学和动力学课程中，为教育和专业发展提供了机会。基于镍铝和基于铝的合金是航空航天和汽车行业的关键材料，因此预计结果将对制造业产生直接影响。 该项目的目的是开发一个人工智能框架，以根据各种数据源自动确定相位模型的输入参数，以建立对模型参数的约束。 AI支持的微观结构模型构建器（AMMBER）利用自动数据流管道来收集，策划和制表不同的数据源，这些数据源涵盖了第一原理计算，实验显微照片以及相关的自然语言文本，热力学数据库和自定义用户的输入。然后，高级优化算法迭代优化相位场参数，以使所得模型重现已知的微观结构特征（例如，相位分数和特征长度比例随时间的函数）。然后，这些模型可用于模拟与工程和制造相关的一系列条件中材料的微观结构演变。 AMMBER的演示涉及商业上重要的基于Ni-Al的基于AL-CU的合金，其中一些包含五个以上的组件，导致必须优化热力学和动力学模型参数的复杂高维参数空间。 高渗透合金的应用含有接近等摩尔量的五个或更多组件，为新的科学发现提供了基础。通过自动化时间耗时的初始模型参数化，Ammber减少了材料建模的人类瓶颈，并为增加相位模拟的吞吐量铺平了道路。 Ammber补充了现有的材料基因组计划（MGI）的工作，并且它利用并集成到围绕诸如开源相相软件（Prisms-PF，Moose）等工具（综合计算材料工程框架（PRISMS），CALPHAD工具），Calphad工具（ESPEI，Thermo-Calc）和Aneemaub（NanoHub）（Nanohohub）等工具（Prisms-PF，Moose），诸如开源相相软件（PRISMS-PF，MOOSE）构建的现有计算材料研究社区。培训研讨会以及计算工具和研究发现中的整合到课堂上，有助于社区互动和参与。 基于NI-AL的基于Ni-Al-Cu的合金是航空航天和汽车行业的关键材料，因此预计结果将直接影响制造业。该提案通过计算机和信息科学和工程局的高级网络基础设施办公室获得资金使用基金会的智力优点和更广泛的影响评估标准进行评估。