Accelerating materials discovery through active learning: Methods, challenges and opportunities
Year: 2025
Type: article
Abstract: The convergence of large-scale experimentation and computation has transformed materials science into a discipline where data plays a central role. However, the vast design space and high costs of experiments and simulations still slow progress. In contrast to traditional approaches, active learning (AL) identifies which experiments or simulations will provide the most valuable information. This minimizes unnecessary work and cost. By prioritizing the most informative data points, AL enables more efficient and targeted exploration, particularly when resources are limited. This approach accelerates breakthroughs in complex systems, such as superconductors, catalysts, and batteries. This review introduces a framework that categorizes AL in both experimental and computational settings. It highlights the importance of integrating domain knowledge and addresses interpretability challenges. By providing a pragmatic roadmap, it makes a strong case for adopting AL, especially to maximize impact in resource-constrained materials research.To facilitate provide additional details, we have made the supplementary materials available in the repository at https://github.com/kg4sci/awsome-AL.
Source: The Innovation Informatics
Authors: Yujie Ma, Yang Gao, Ludi Wang, Ming Chen, Wenjuan Cui, Bin Wang, Yi Du
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G4344828322
OpenAlex ID: W7104176330