About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
Presentation Title |
Phase Identification by Neural Networks Trained from Experimental and Theoretical Structure Data |
Author(s) |
Nam Le, Michael Pekala, Alexander New, Eddie Gienger, Janna Domenico, Christine Piatko, Elizabeth Pogue, Tyrel M. McQueen, Christopher Stiles |
On-Site Speaker (Planned) |
Nam Le |
Abstract Scope |
X-ray diffraction (XRD) is a critical tool in high-throughput materials screening, whether to confirm synthesis of target phases or to identify new phases in unexplored systems. However, inverting XRD patterns to estimate which phases are present requires significant human interpretation. Fortunately, machine learning models have shown promise in breaking this bottleneck by automating phase identification. Previous examples have been restricted to phase identification within composition spaces of 4 to 5 elements and relied on databases of experimentally determined structures such as the Inorganic Crystal Structure Database. We will present the applicability to broader discovery settings in unexplored spaces by expanding to phases among 21 elements and synthesizing patterns using computationally determined structures from the Materials Project. These findings will be useful across high-throughput materials discovery settings where properties are strongly tied to particular phases. |