Work

Learning grain boundary segregation behavior through fingerprinting complex atomic environments

Year: 2024

Type: article

Abstract: Describing site-specific segregation in multi-species materials is a computationally complex task that typically requires model simplification, at the expense of atomic accuracy, or limitation to small samples. Here, the relationships between local atomic environments at grain boundaries and their segregation energies are investigated by developing suitable machine learning atomic descriptors.

Source: Communications Materials

DOI: 10.1038/s43246-024-00616-y

Authors: Jacob P. Tavenner, Ankit Gupta, Gregory B. Thompson, Edward M. Kober, Garritt J. Tucker

Institutions: Los Alamos National Laboratory, Colorado School of Mines, Baylor University, University of Alabama

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490