This 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.
This 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.
