Collaborative Research: DMREF: Data-Driven Discovery of the Processing Genome for Heterogenous Superalloy Microstructures

这个由DMREF资助的项目旨在通过研究生产过程和材料的微观结构(肉眼看不见的内部结构)之间的关系，彻底改变新型工程合金的创造。这种关系是材料科学生命周期中一个关键但较少探索的部分，但直接将加工与性能联系起来。利用尖端机器学习和数据科学，项目组将建立一个名为Dragons(数据驱动的优化纳米结构高温合金递归AI-Powered Generator)的平台，以发现这些联系，从而能够对材料生产进行更细微的控制。龙将规定理想的加工条件，以实现特定的材料微结构。该项目具有广泛的意义，有可能推动电子、医疗保健、能源和交通等依赖新材料的不同行业的进步。此外，该项目的教育推广活动旨在通过提供进入这些领域的更多样化、包容性和可持续的途径来激励下一代科学家和工程师。因此，这项研究具有催化科学进步、促进经济增长和提高教育成果的潜力。DMREF项目的重点是利用机器学习和数据科学来促进对新材料生产中加工-微结构关系的理解，新材料是材料基因组倡议(MGI)的一个关键但未被探索的方面。研究团队将开发一个数据驱动的平台，名为数据驱动的优化纳米结构高温合金(Dragons)递归人工智能发电机(Dragons)，以揭开多相、异质纳米结构材料(HNMs)创建过程中固有的复杂关系的神秘面纱。Dragons将利用预测模型根据给定的加工条件解释微结构属性，并以互惠的方式提供生成预定义微结构所需的加工参数。该研究小组利用磁控溅射和热处理(MS+HT)方面的专业知识，致力于设计复杂的镍基高温合金的异质设计。迭代研究框架包括合成和微结构设计、微结构表征、原子模拟和中尺度建模，每个周期都将改进Dragons，促进工艺描述符和微结构特征之间更紧密的联系。这项工作的更广泛影响跨越了重塑工程合金开发的潜力，并促进了与NIST科学家的合作。此外，旨在培养材料工程领域多样化、熟练劳动力的教育计划强调了该项目对社会的承诺。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。

This Designing Materials to Revolutionize and Engineer our Future (DMREF) funded project aims to revolutionize the creation of novel engineering alloys by focusing on the relationship between the process of production and the material's resultant microstructure - the internal structure invisible to the naked eye. This relationship is a crucial but less-explored segment of the material science lifecycle, yet directly connects processing to performance. Using cutting-edge machine learning and data science, the project team will build a platform, named as DRAGONS (Data-driven Recursive AI-powered Generator of Optimized Nanostructured Superalloys), to discover these connections, enabling more nuanced control over material production. DRAGONS will prescribe ideal processing conditions to achieve a specific material microstructure. This project carries broad significance, with the potential to drive advancements in diverse sectors that rely on novel materials including electronics, healthcare, energy, and transportation. Additionally, the project's educational outreach activities aim to inspire the next generation of scientists and engineers by providing a more diverse, inclusive, and sustainable pathway into these fields. Hence, this research carries potential to catalyze scientific advancement, foster economic growth, and enhance educational outcomes.This DMREF project focuses on harnessing machine learning and data science to advance understanding of the processing-microstructure relationships in the production of novel materials, a key, yet underexplored facet of the Materials Genome Initiative (MGI). The research team will develop a data-driven platform, named as Data-driven Recursive AI-powered Generator of Optimized Nanostructured Superalloys (DRAGONS), to demystify the complex relationships inherent in the creation of multi-phase, heterogeneous nanostructured materials (HNMs). DRAGONS will utilize predictive models to interpret microstructure attributes based on given processing conditions and, in a reciprocal manner, provide processing parameters required to generate a predefined microstructure. Capitalizing on expertise in magnetron sputtering and heat treatment (MS+HT), the research team aims to engineer intricate heterogeneous designs in Ni-based superalloys. An iterative research framework encompasses synthesis and microstructural design, microstructure characterization, atomistic simulation, and mesoscale modeling, and each cycle will refine DRAGONS, fostering stronger links between processing descriptors and microstructure features. The broader impacts of this work span the potential to reshape engineered alloy development and to foster collaborations with NIST scientists. Furthermore, educational programs targeted at developing a diverse, skilled workforce in materials engineering underscore the project's commitment to society.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.