Building a Global Infrastructure for AI-Driven Innovation
Jun Jiang a
a University of Science and Technology of China, China
Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)
I4 Digital Discovery: From Energy Materials to Devices
Barcelona, Spain, 2026 March 23rd - 27th
Organizers: Shoichi Matsuda and Magda Titirici
Invited Speaker, Jun Jiang, presentation 684
Publication date: 15th December 2025

AI-driven scientific innovation is often blocked by scarce, fragmented, and biased data. To solve this, a global infrastructure of cloud-connected, autonomous laboratories has been proposed, generating high-quality, reproducible datasets via robotic experiments. This vision is structured into five hierarchical levels: G1 (Process Automation), G2 (Theory-Experiment Iterative Loop), G3 (Large Model-Driven), G4 (Multi-Platform, Multi-Task), G5 (Autonomous Scientific Discovery).
Our G4-level large-model-driven autonomous platform coordinates domain-specific small AI models and robotic experiments, enabling intelligent scheduling and real-time fine-tuning of pre-trained models based on experimental data, thereby creating a dynamic, synergistic feedback loop. Major breakthroughs in novel material creation (e.g., catalysts, polymers, COFs, and proteins) have compressed discovery cycles from over a century to mere months.
The global infrastructure transforms isolated scientific efforts into a collaborative and efficient exploration engine. It democratizes access to high-quality data, breaks down geographical and institutional barriers to innovation, ultimately accelerates industrial-scale scientific and technological advancements.

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