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

2025-11-25

AI Summary

This study addresses core bottlenecks in AI-powered scientific discovery—low data trustworthiness, poor model transferability, and the absence of an experimental-computational closed loop—by proposing a tripartite AI-driven research paradigm: “trustworthy data—transferable models—generative systems.” Methodologically, it integrates large foundation models with physics-informed generative modeling (e.g., electronic structure and synthetic feasibility constraints), active learning, and self-driving laboratory technologies to enable end-to-end, interpretable, and reproducible scientific workflows across biology, chemistry, materials science, climate science, and physics. Its key contribution is the first multi-disciplinary framework unifying AI, experimentation, and simulation, significantly enhancing physical consistency of models and autonomous scientific reasoning. This provides a systematic, transparent, efficient, and verifiable pathway toward AI-augmented scientific discovery.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Advancing scientific discovery through AI integration across multiple disciplines
• Addressing bottlenecks in data, methods and infrastructure for AI systems
• Developing transparent AI that accelerates discovery in real-world environments

Innovation

• AI systems integrated into end-to-end scientific workflows
• Large foundation models maintain reproducibility and interpretability
• Active learning and self-driving laboratories close prediction-validation loops