From Small Data Modeling to Large Language Model Screening: A Dual-Strategy Framework for Materials Intelligent Design.

ABSTRACT: Small data in materials present significant challenges to constructing highly accurate machine learning models, severely hindering the widespread implementation of data-driven materials intelligent design. In this study, the Dual-Strategy Materials Intelligent Design Framework (DSMID) is introduced, which integrates two innovative methods. The Adversarial domain Adaptive Embedding Generative network (AAEG) transfers data between related property datasets, even with only 90 data points, enhancing material composition characterization and improving property prediction. Additionally, to address the challenge of screening and evaluating numerous alloy designs, the Automated Material Screening and Evaluation Pipeline (AMSEP) is implemented. This pipeline utilizes large language models with exte

SUBMITTER: Yu Y 

PROVIDER: S-EPMC11615768 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature