AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua, Stephan Roche, O. Anatole von Lilienfeld, Konstantin S. Novoselov, Xavier Bresson, Gianmarco Mengaldo, Xudong Chen, Terence J. O'Kane, Emily R. Lines, Matthew J. Allen, Amandine E. Debus, Clayton Miller

TL;DR
This paper presents a comprehensive roadmap for integrating AI into scientific research, emphasizing data trustworthiness, model transferability, and end-to-end workflows to accelerate discovery across multiple scientific domains.
Contribution
It offers a forward-looking, multidisciplinary overview of AI-enabled science, highlighting key themes, current challenges, and future directions for transparent and effective AI systems in research.
Findings
Large foundation models enhance scientific prediction and understanding.
Active learning and self-driving labs improve experimental validation.
Identifies bottlenecks in data, methods, and infrastructure for AI in science.
Abstract
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Scientific Computing and Data Management · Neural Networks and Reservoir Computing
