Artificial Intelligence and Generative Models for Materials Discovery -- A Review

TL;DR

This review discusses how AI-driven generative models are revolutionizing materials discovery by enabling inverse design, addressing challenges like data scarcity, and integrating AI with experimental workflows for sustainable innovations.

Contribution

It provides a comprehensive overview of AI generative models in materials discovery, highlighting recent advances, challenges, and future directions for integrating AI with experimental methods.

Findings

Generative models enable inverse design of materials with desired properties.

Challenges include data scarcity, computational costs, and interpretability.

Emerging approaches like multimodal and physics-informed models improve discovery processes.

Abstract

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 AI's transformative potential in accelerating materials discovery for sustainability, healthcare, and energy innovation.