| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size |
| Author(s) |
Thomas J. Hardin, Michael Chandross, Murray S. Daw |
| On-Site Speaker (Planned) |
Thomas J. Hardin |
| Abstract Scope |
The structural complexity and large compositional design space of metallic glass are an enduring challenge for those who seek performance gains beyond the crystalline/amorphous binary. We report a series of simulations using the EAM-X interatomic potential (a recently-developed formalism that loosely captures the behavior of a wide range of metallic alloys with a few easy-to-change parameters) to sample the design space of binary metallic glasses, specifically focusing on variation in composition and atomic size ratio. We used data mining techniques (the Gaussian Integral Inner Product Distance with agglomerative clustering and diffusion maps) to map out the local structural states of the glass as a function of these variables. This analysis enables the development of a science basis for rule-of-thumb relationships between composition and atomic size, and local structure. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 (SAND2022-4502 A). |