AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
It provides a forward-looking view for researchers in fields like biology, chemistry, and materials science, identifying bottlenecks and charting directions for more transparent and powerful AI systems, but it is incremental as it builds on existing trends without introducing new methods.
This roadmap outlines how AI and machine learning are enhancing scientific discovery across multiple domains by extending researchers' capabilities to probe, predict, and design, highlighting shared themes like trustworthy data and integrated workflows.
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 maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.