Handoko, A. D., & Made, R. I. (2025). Artificial Intelligence and Generative Models for Materials Discovery: A Review. World Scientific Annual Review of Functional Materials. https://doi.org/10.1142/S2810922825400018
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 last, we are moving quickly towards the artificial intelligence (AI) driven approach, realising the ’inverse design’ capabilities that allowthe 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
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Industry Alignment Fund – Pre-Positioning
Grant Reference no. : M22K8a0048 (Project No. OUNI231001bENT-PP)
This research / project is supported by the Agency for Science, Technology and Research - Industry Alignment Fund – Pre-Positioning
Grant Reference no. : M23L6a0020 (Project No. OUNI231001aENT-PP)
This research / project is supported by the Energy Market Authority (EMA) - Megawatt Hour Scale Sodium-ion Battery System for Solar Storage and Load Shifting
Grant Reference no. : EMA- EP014-ESGC2-0001 (Project No. ESME250101aPUBESS)
This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering Programmatic Fund
Grant Reference no. : M24N4b0034 (Project No.OUNI241001aENTMTC)
This research / project is supported by the Agency for Science, Technology and Research - Seed Funding
Grant Reference no. : C231218004
Description:
For the publisher's version, please refer here: https://doi.org/10.1142/S2810922825400018