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.

具有改进性能的材料可以极大地影响可持续性，人类福利和国家繁荣。例如，更坚固的材料可以减轻车辆的重量，从而减少能源消耗和污染。材料的性质通常取决于它们的微观结构（材料中的特征在一微米到数百微米的尺度上）。热力学自由能（提供进化的驱动力）和动力学参数（提供进化发生的速度）共同决定了材料在微观尺度上的进化方式。该项目开发算法和软件，使用人工智能自动提取热力学和动力学信息，以模拟复杂金属混合物的微观结构演变。人工智能微结构模型构建器（AMBER）收集和利用数据，包括第一原理计算、实验显微图和相关的自然语言文本、热力学数据库以及自定义用户输入。然后，它产生输入到微观结构演化模型，促进所需的基本理解，以获得控制的微观结构和由此产生的材料性能。计划对具有商业重要性的合金（镍铝基合金和铝铜基合金）以及相应的高熵合金（含有五种或五种以上接近等摩尔分数的成分的合金）演示其能力。AMBER为材料基因组计划范围内基于模拟的材料发现和开发的软件基础设施做出了贡献。培训活动，包括为社区举办的培训讲习班，以了解软件及其背后的理论，并将其融入本科生和研究生热力学和动力学课程，为教育和专业发展提供机会。镍铝基和铝铜基合金是航空航天和汽车工业的关键材料，因此其结果预计将对制造业产生直接影响。 该项目的目标是开发一个人工智能框架，用于基于各种数据源自主确定相场模型的输入参数，以建立对模型参数的约束。微结构模型构建器（AMBER）利用自动化数据流管道来收集、管理和制表不同的数据源，包括第一原理计算、实验显微图和相关的自然语言文本、热力学数据库和自定义用户输入。然后，高级优化算法迭代地优化相场参数，使得所得模型再现已知的微结构特性（例如，作为时间的函数的相位分数和特征长度尺度）。然后，这些模型可用于模拟材料在与工程和制造相关的一系列条件下的微观结构演变。AMBER的演示涉及商业上重要的Ni-Al基和Al-Cu基合金，其中一些合金包含五种以上的组分，导致复杂的高维参数空间，其中热力学和动力学模型参数必须进行优化。 高熵合金含有接近等摩尔量的五种或更多种成分，这种应用为新的科学发现提供了基础。通过自动化耗时的初始模型参数化，AMMBER减少了材料建模的人力瓶颈，并为提高相场模拟的吞吐量铺平了道路。AMBER补充了现有的材料基因组计划（MGI）的努力，它利用并整合到现有的计算材料研究社区，这些社区围绕开源相场软件（PRISMS-PF，MOOSE），集成计算材料工程框架（PRISMS），CALPHAD工具（ESPEI，Thermo-Calc）和传播平台（nanoHUB）等工具建立。培训研讨会以及将计算工具和研究成果整合到课堂中促进了社区互动和参与。 Ni-Al基和Al-Cu基合金是航空航天和汽车工业的关键材料，因此，预计其结果将对制造业产生直接影响。该提案通过计算机和信息科学与工程理事会的高级网络基础设施办公室以及数学和物理科学理事会的材料研究部获得资金。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。