Work

Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation”

Year: 2025

Type: letter

Abstract: We respond to Schaeben et al.’s 1 comment on our paper, “Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.” While their observations are factually correct, they do not disprove our results. Our method, TACS, preserves the full distribution of crystallographic orientations and is validated with real-world data. We emphasize the importance of empirical validation over theoretical constructs in assessing machine learning methods’ practical performance.

Source: npj Computational Materials

DOI: 10.1038/s41524-025-01562-0

Authors: J. Wanni, C. A. Bronkhorst, D. J. Thoma

Institution: University of Wisconsin–Madison

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

Funder: U.S. Department of Defense