Work

Generative inverse design for microstructure control in precursors for Li- and Mn-rich layered-oxide cathodes

Year: 2025

Type: article

Abstract: ) from SEM images, the platform accurately predicts SEM-like morphologies resulting from specific co-precipitation conditions, including reaction time-, solution concentration-, and pH-dependent structural changes. We experimentally validated that our optimization pinpoints synthesis parameters yielding user-defined target morphologies of Li- and Mn-rich (LMR) layered oxide materials, with predicted and synthesized structures showing close agreement. This framework offers a practical strategy for data driven materials design, enabling both forward prediction and inverse design of synthesis conditions and paving the way toward autonomous, image-guided microstructure engineering.

Source: Materials Horizons

DOI: 10.1039/d5mh01850a

Authors: Geunho Choi, Changhwan Lee, Jieun Kim, Insoo Ye, Keeyoung Jung, Inchul Park

Institutions: Pohang Iron and Steel (South Korea), Sunchon National University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Ministry of Education, Ministry of Education, India, National Supercomputing Center, Korea Institute of Science and Technology Information

Award: G7269058861