EAGER: Type I: Liquid metal embrittlement of engineering alloys by eutectic gallium indium: Data-driven experimental design using sequential learning

非技术性总结EAGER奖支持研究和教育，涉及在MATDAT 18 Datasheet活动中点燃的新合作，重点是使用数据科学的方法在材料科学的挑战性问题上取得进展，例如液态金属脆化的机制。当某些液体金属与特定的固体金属接触时，固体金属的强度和/或延展性会发生灾难性的降低;这被称为“液体金属脆化”。“虽然液态金属脆化已经研究了世纪，但对这种现象缺乏充分的了解。目前还没有办法预测在给定条件下液态金属脆化的发生或严重程度。PI的目标是使用一种方法，计算机可以从许多研究中获得的数据中“学习”，以创建一个模型，该模型可以预测液体金属脆化的严重程度，作为实验条件的函数，包括液体成分，固体成分，温度，变形率和固体金属的微观结构，这在很大程度上是通过强大的光学显微镜可见的。作为这项研究的一部分创建的机器学习模型可以使液态金属在工程应用中的使用成为可能，例如在可拉伸电路中，并允许未来研究负责液态金属脆化的基本物理机制。该项目强调所有年龄段学生的教育和职业发展。具有材料科学背景的研究生和本科生研究人员将接受传统实验室技能和工程师数据科学方法的培训。此外，作为该项目的一部分，还将制定针对初中和高中学生的推广模块，并通过包括工程少数群体方案和北卡罗来纳州州立大学的工程场所在内的方案向更广泛的社区分发。 EAGER奖支持研究和教育，涉及在MATDAT 18 Datasheet活动中启动的新合作，重点是使用数据科学的方法在材料科学的挑战性问题上取得进展，例如液态金属脆化机制。迄今为止，还没有成功地开发出预测液态金属脆化的唯象或机理模型。液态金属脆化的现象非常复杂，脆化行为几乎取决于测试过的所有实验变量，包括温度、应变速率、固体金属晶粒尺寸、固体成分、液体成分等。由于这种复杂的现象学和实验的挑战，独立地改变大量的参与，纯经验的研究液态金属脆化是棘手的。 该项目采用另一种方法来建立预测性液态金属脆化模型：Citrination平台将用于进行顺序学习。在这种方法中，使用初步数据训练初始模型，并用于建议下一轮实验，这些实验将最有可能提高模型的预测能力。PI最近开发了一个基于从文献中提取的数据进行训练的初始模型。该模型被用来建议初步实验，进行并用于完善模型。然而，模型需要进一步迭代以实现预测能力。PI旨在迭代地完善模型。一旦该模型有足够的预测能力，这项工作的第二个目标是测试假设，液态金属脆化不是一个整体的现象，而是由几个不同的机制。这项工作的最后一个主要目标是为每个确定的潜在机制和未来的机制研究的理想候选人确定“原型”系统。如果多重机制的假设得到支持，这可能会调和目前在literary.This奖项反映了NSF的法定使命，并已被认为是值得通过使用该基金会的智力价值和更广泛的影响审查标准进行评估支持看似矛盾的报告液态金属脆化行为。

NONTECHNICAL SUMMARYThis EAGER award supports research and education involving a new collaboration kindled at the MATDAT18 Datathon event focused on using the methods of data science to make progress on challenging problems in materials science, in the mechanisms of case liquid metal embrittlement. When certain liquid metals come into contact with specific solid metals, the solid metals can undergo a catastrophic reduction in strength and/or ductility; this is termed "liquid metal embrittlement." While liquid metal embrittlement has been studied for over a century, a full understanding of the phenomenon is lacking. There is currently no means to predict the occurrence or severity of liquid metal embrittlement under given conditions. The PIs aim to use a method where computers can "learn" from the data obtained from many studies to create a model that can predict the severity of liquid metal embrittlement as a function of the experimental conditions, including liquid composition, solid composition, temperature, deformation rate, and microscopic structure of the solid metal which is largely visible through powerful optical microscopes. The machine learning model created as part of this research may enable the use of liquid metals in engineering applications, such as in stretchable circuits, and allow for future study of the fundamental physical mechanisms responsible for liquid metal embrittlement. The project strongly emphasizes the education and professional development of students of all ages. Graduate and undergraduate researchers with a materials science background will be trained in both conventional laboratory skills and in data science methods for engineers. Additionally, outreach modules targeted to middle and high school students will be developed as part of this project and distributed to the broader community through programs including the Minorities in Engineering Program and The Engineering Place at North Carolina State University. TECHNICAL SUMMARYThis EAGER award supports research and education involving a new collaboration kindled at the MATDAT18 Datathon event focused on using the methods of data science to make progress on challenging problems in materials science, in the mechanisms of case liquid metal embrittlement. To date, no predictive phenomenological or mechanistic models of liquid metal embrittlement have been successfully developed. The phenomenon of liquid metal embrittlement is incredibly complex, with embrittlement behavior shown to depend on nearly every experimental variable ever tested including temperature, strain rate, solid metal grain size, solid composition, liquid composition, and more. Due to this complex phenomenology and the experimental challenge of independently varying the large number of involved, purely empirical studies of liquid metal embrittlement are intractable. This project takes an alternative approach to establish a predictive liquid metal embrittlement model: the Citrination platform will be used to conduct sequential learning. In this approach, an initial model is trained using preliminary data and used to suggest the next round of experiments which will have the greatest likelihood of improving the predictive capability of the model. The PI recently developed an initial model trained on data extracted from the literature. The model was used to suggest preliminary experiments, which were conducted and used to refine the model. However, further iterations are required for the model to achieve predictive capability. The PIs aim to iteratively refine the model. Once the model has sufficient predictive capability, a second objective of this work is to test the hypothesis that liquid metal embrittlement is not a monolithic phenomenon but is composed of several distinct mechanisms. The last main objective of this work is to identify "archetypal" systems for each identified potential mechanism and ideal candidates for future mechanistic study. If the hypothesis of multiple mechanisms is supported, this could reconcile seemingly contradictory reports of liquid metal embrittlement behavior present in the literature.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.