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

非技术总结该奖项支持计算、以数据为中心的理论研究和教育，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，如组成微晶或颗粒之间的界面，会严重影响其强度或耐腐蚀性和抗裂性。科学家和工程师通过找到并增加与这些特性相关的界面或晶界的比例，已经能够创造出数量有限的、具有增强的耐腐蚀性和抗裂性的材料。不幸的是，这些成功仅限于一小部分真实世界的材料。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性质联系起来。将特别注意哪些方法提供最有用的洞察力，以及哪些方法允许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性能进行量身定做。这将使一系列优化材料成为可能，如具有优异耐腐蚀性和抗变形能力的合金、高强度高塑性材料和增强的抗断裂材料。该项目支持未来数据密集型材料研究工作人员的教育和培训。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。技术总结该奖项支持计算、以数据为中心的理论研究和教育，以将机器学习和多分辨率表示相结合，以发现晶界结构-性能关系并改进材料设计。确定影响材料宏观性能的微观特征是材料设计的关键。在晶界起重要作用的情况下，优化合金性能受到当前对影响宏观性能的微观结构的理解的限制。PI将结合机器学习和多分辨率表示来发现晶界结构-性能关系。初步结果导致了保留物理解释的表述，并表明控制晶界行为的物理机制可以被识别。PI的目标是获得包含原子结构和晶体结构的晶界结构-性能关系。为了实现这一目标，PI将：1.将统计力学应用于颗粒边界：将检查颗粒边界势能景观对静态和动态特性的影响；2.确定结构对特性的长期影响：将使用多尺度表示法来预测特性，如剪切耦合，仅使用短期环境无法很好地预测这些特性。PI的目标是识别与各种性质相关的短程和长程结构，并使用另一种表示法-散布卷积网络来验证它们的作用。将晶体特性趋势与原子结构联系起来：将在本项目中学到的原子特性趋势转换为实验或结晶学坐标。这将把微观结构与宏观性能联系起来，向晶界工程迈进。这项研究结合了建模、理论、机器学习和模拟，以发现晶界结构-性能关系及其主导物理。两名研究生将与两位PI密切合作，组成一个跨学科的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向更广泛的社区提供。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.