Data quantity governance for machine learning in materials science.

ABSTRACT: Data-driven machine learning (ML) is widely employed in the analysis of materials structure-activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model paramet

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10265966 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature