Overcoming Roadblocks for Implementing AI/ML Methods for Materials Advancement

Published

April 6, 2026

Author(s)

James Warren, Francesca Tavazza, Austin McDannald, Aaron Kusne, Howard Joress, David Hoogerheide, Brian DeCost, Kamal Choudhary, Debra Audus

Abstract

The development of novel materials with tailored properties is a complex, multi-objective optimization problem that has long been a challenge in materials research. The integration of artificial intelligence (AI) and machine learning (ML) techniques has shown great promise in accelerating materials discovery, design, and development by uncovering hidden correlations between processing, structure, and properties. Autonomous experimentation (AE) platforms, also known as self-driving laboratories (SDLs), have emerged as a powerful tool in this endeavor, enabling the rapid and efficient acquisition of critical data through a closed-loop feedback process. In this review, we explore the applications of AI/ML techniques to materials research and development through the lens of SDLs, and examine the challenges and opportunities associated with the development and deployment of SDLs. We provide a detailed analysis of the components of an SDL, including AI-driven decision-making, experimental data generation, and knowledge representation, and discuss the current barriers to industrial adoption.

Citation

Annual Review of Materials Research

Volume

56

Pub Type

Journals

Download Paper

https://doi.org/10.1146/annurev-matsci-072924-102951

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Keywords

AI, Autonomous experimentation, artificial intelligence, material science, material discovery, machine learning

Citation

Warren, J. , Tavazza, F. , McDannald, A. , Kusne, A. , Joress, H. , Hoogerheide, D. , DeCost, B. , Choudhary, K. and Audus, D. (2026), Overcoming Roadblocks for Implementing AI/ML Methods for Materials Advancement, Annual Review of Materials Research, [online], https://doi.org/10.1146/annurev-matsci-072924-102951, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960397 (Accessed May 19, 2026)

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