Albertus Denny Handoko; Riko I Made · 2025 · World Scientific Annual Review of Functional Materials
Paper
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.
Analysis
This review explores the application of AI-driven generative models in accelerating materials discovery, moving from experimental to AI-driven approaches for inverse design.
Discovery
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Source record