AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

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 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.