AI-driven materials design: a mini-review
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Materials design is an important component of modern science and technology,
yet traditional approaches rely heavily on trial-and-error and can be
inefficient. Computational techniques, enhanced by modern artificial
intelligence (AI), have greatly accelerated the design of new materials. Among
these approaches, inverse design has shown great promise in designing materials
that meet specific property requirements. In this mini-review, we summarize key
computational advancements for materials design over the past few decades. We
follow the evolution of relevant materials design techniques, from
high-throughput forward machine learning (ML) methods and evolutionary
algorithms, to advanced AI strategies like reinforcement learning (RL) and deep
generative models. We highlight the paradigm shift from conventional screening
approaches to inverse generation driven by deep generative models. Finally, we
discuss current challenges and future perspectives of materials inverse design.
This review may serve as a brief guide to the approaches, progress, and outlook
of designing future functional materials with technological relevance.
