Discovering the Building Blocks and Structure Property Relationships of Grain Boundaries Using Machine Learning

使用机器学习发现晶界的构建块和结构属性关系

• 批准号: 1817321
• 负责人: Eric Homer
• 金额: $ 42万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817321&HistoricalAwards=false
• 关键词: Discovering; Building; Blocks; Structure; Property

NONTECHNICAL SUMMARYThis award supports computational, data-centric, theoretical research and education aimed to include the effects of defects in optimizing the properties of metal alloys. Defects in the structure of materials, such as interfaces between constituent crystallites or grains, can strongly influence their strength or resistance to corrosion and cracking. Scientists and engineers have been able to create a limited number of materials with enhanced corrosion and crack resistance by finding and increasing the fraction of interfaces, or grain boundaries, that were correlated with these properties. Unfortunately, these successes were limited to a small fraction of real-world materials. The PIs' preliminary work suggests that through artificial intelligence techniques - machine learning - grain boundary structures can be identified that correlate with specific material behaviors. This award supports the application of machine learning and other data intensive techniques to correlate microscopic grain boundary structure with different macroscopic properties. Specific attention will be paid to which methods provide the most useful insight and which methods allow one to identify the physical processes controlling grain boundary behaviors of interest. The work will enable the next generation of materials to be tailored with properties unique to specific applications. This could enable a range of optimized materials such as alloys with superior corrosion-resistance and deformation-resistance, high strength-high ductility materials, and enhanced fracture-resistant materials.This project supports education and training of the future workforce in data-intensive materials research. Software developed through research and data created through research will be made available to the broader community.TECHNICAL SUMMARYThis award supports computational, data-centric, theoretical research and education to combine machine learning and multi-resolution representations to discover grain-boundary structure-property relationships and advance materials design. Identifying the microscopic features that affect macroscopic materials properties is essential to materials design. Optimizing alloy performance where grain boundaries play an important role is limited by current understanding of the microscopic structures that affect macroscopic properties. The PI will combine machine learning and multi-resolution representations to discover grain boundary structure-property relationships. Preliminary results led to representations that retain physical interpretability and suggest that the physical mechanisms that control grain boundary behavior can be identified. The PI's aim to obtain grain boundary structure-property relationships that incorporate both atomistic and crystallographic structure. To pursue this goal, the PIs will: 1. Apply Statistical Mechanics to Grain Boundaries: Grain boundary potential energy landscapes will be examined for their influence on both static and dynamical properties;2. Determine Long-Range Effects of Structures on Properties: A multi-scale representation will be used to predict properties, such as shear coupling, which cannot be predicted well using only short-range environments. The PIs aim to identify the short- and long-range structures that correlate with various properties and verify their role using another representation, scattering convolutional networks.3. Connect Crystallographic Property Trends to Atomic Structure: Translate atomistic property trends learned in this project into experimental, or crystallographic, coordinates. This will connect microscopic structure to macroscopic properties and advance toward grain boundary engineering.The research combines modeling, theory, machine learning, and simulations to discover grain boundary structure-property relationships and their governing physics. Two graduate students will work closely with both PIs to form a cross-disciplinary team to tackle this challenging problem and will be trained in data-intensive materials research and other advanced methods of materials research. Software developed through research and data created through research will be made available to the broader community.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.

非技术摘要该奖项支持以数据为中心的计算、理论研究和教育，旨在包括缺陷对优化金属合金性能的影响。材料结构中的缺陷，例如组成微晶或晶粒之间的界面，会强烈影响其强度或耐腐蚀和开裂性。通过寻找和增加与这些特性相关的界面或晶界的比例，科学家和工程师已经能够创造出数量有限的具有增强耐腐蚀性和抗裂性的材料。不幸的是，这些成功仅限于现实世界材料的一小部分。 PI 的初步工作表明，通过人工智能技术（机器学习）可以识别与特定材料行为相关的晶界结构。该奖项支持应用机器学习和其他数据密集型技术将微观晶界结构与不同的宏观特性关联起来。将特别关注哪些方法提供最有用的见解以及哪些方法允许人们识别控制感兴趣的晶界行为的物理过程。这项工作将使下一代材料能够根据特定应用的独特特性进行定制。这可以实现一系列优化材料，例如具有优异耐腐蚀和抗变形能力的合金、高强度高延展性材料以及增强的抗断裂材料。该项目支持对数据密集型材料研究的未来劳动力的教育和培训。通过研究开发的软件和通过研究创建的数据将提供给更广泛的社区。技术摘要该奖项支持计算、以数据为中心的理论研究和教育，将机器学习和多分辨率表示相结合，以发现晶界结构-性能关系并推进材料设计。 识别影响宏观材料性能的微观特征对于材料设计至关重要。目前对影响宏观性能的微观结构的理解限制了晶界发挥重要作用的合金性能的优化。 PI 将结合机器学习和多分辨率表示来发现晶界结构-性质关系。初步结果得出了保留物理可解释性的表示，并表明可以识别控制晶界行为的物理机制。 PI 的目标是获得包含原子结构和晶体结构的晶界结构-性质关系。为了实现这一目标，PI 将： 1. 将统计力学应用于晶界：将检查晶界势能景观对静态和动态特性的影响；2. 将统计力学应用于晶界。确定结构对属性的远程影响：多尺度表示将用于预测属性，例如剪切耦合，仅使用短程环境无法很好地预测这些属性。 PI 旨在识别与各种属性相关的短程和长程结构，并使用另一种表示形式（散射卷积网络）验证它们的作用。3．将晶体学性质趋势与原子结构联系起来：将本项目中学到的原子性质趋势转化为实验或晶体学坐标。这将把微观结构与宏观特性联系起来，并推进晶界工程。该研究结合了建模、理论、机器学习和模拟，以发现晶界结构-特性关系及其控制物理。两名研究生将与两位 PI 密切合作，组成一个跨学科团队来解决这一具有挑战性的问题，并将接受数据密集型材料研究和其他先进材料研究方法的培训。通过研究开发的软件和通过研究创建的数据将提供给更广泛的社区。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character

• DOI: 10.1016/j.actamat.2022.118006
• 发表时间: 2022-05-26
• 期刊: ACTA MATERIALIA
• 影响因子: 9.4
• 作者: Homer, Eric R.;Hart, Gus L. W.;Serafin, Lydia Harris
• 通讯作者: Serafin, Lydia Harris

Relative grain boundary energies from triple junction geometry: Limitations to assuming the Herring condition in nanocrystalline thin films

三结几何形状的相对晶界能量：假设纳米晶薄膜中赫林条件的局限性

• DOI: 10.1016/j.actamat.2022.118476
• 发表时间: 2023
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Patrick, Matthew J.;Rohrer, Gregory S.;Chirayutthanasak, Ooraphan;Ratanaphan, Sutatch;Homer, Eric R.;Hart, Gus L． W.;Epshteyn, Yekaterina;Barmak, Katayun
• 通讯作者: Barmak, Katayun

Computed Aluminum Grain Boundary Structures and Energies Covering the 5D Space of Crystallographic Character

计算覆盖晶体特征 5D 空间的铝晶界结构和能量

• DOI: 10.17632/4ykjz4ngwt
• 发表时间: 2022
• 期刊: Mendeley
• 作者: Homer, Eric