Artificial Intelligence and Generative Models for Materials Discovery: A Review

High-throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly towards the artificial intelligence (AI) driven approach, realizing the ‘inverse design’ capabilities that allow the discovery of new materials given the desired properties. This review aims to discuss different principles of AI-driven generative models that are applicable for materials discovery, including different materials representations available for this purpose. We will also highlight specific applications of generative models in designing new catalysts, semiconductors, polymers, or crystals while addressing challenges such as data scarcity, computational cost, interpretability, synthesizability, and dataset biases. Emerging approaches to overcome limitations and integrate AI with experimental workflows will be discussed, including multimodal models, physics-informed architectures, and closed-loop discovery systems. This review aims to provide insights for researchers aiming to harness AIs transformative potential in accelerating materials discovery for sustainability, healthcare, and energy innovation.

Authors

Albertus Denny Handoko; Riko I Made

Published

Jan 1, 2025

Journal or venue

World Scientific Annual Review of Functional Materials

DOI

10.1142/s2810922825400018

Paper type

journal article

Source

Crossref