Beijing Unveils 16 Benchmark Cases in Push for AI-Driven Scientific Research

Municipal authorities in Beijing showcased 16 benchmark cases demonstrating artificial intelligence applications in scientific research at the 2026 AI for Science Conference. Spanning autonomous laboratory operations, pharmaceutical modeling, and shared data infrastructure, the projects reflect Beijing's systematic strategy to modernize academic workflows under a policy roadmap issued in June. Alongside research initiatives from institutions like Tsinghua University and the Chinese Academy of Sciences, Haidian District designated over 2.3 million square meters of industrial and pilot testing facilities in Xisanqi to build an integrated scientific intelligence ecosystem.

The transition of AI for Science (AI4S) from academic theoretical exploration to structured engineering workflows holds major implications for research productivity, potentially shortening cycle times in drug discovery and materials science while reducing reliance on trial-and-error laboratory methods.

Municipal authorities in Beijing unveiled 16 benchmark cases demonstrating artificial intelligence applications in scientific research at the 2026 AI for Science Conference, signaling a structured transition from exploratory studies toward production-grade research systems, according to state media outlet People's Daily. The benchmark cohort stems from an implementation blueprint issued in June by Beijing's municipal leadership group for education, science, technology, and talent, which targeted the systematic deployment of AI across foundational scientific disciplines. Organizers divided the 16 selected projects into five functional tiers: autonomous laboratory architectures, high-value industry scenarios, specialized scientific foundation models, research data repositories, and digital collaboration platforms. In physical laboratory automation, the chemistry department at Tsinghua University developed an autonomous facility designed to replace manual trial-and-error procedures. The platform operates end-to-end without human intervention, connecting algorithmic molecular design directly to automated chemical synthesis and characterization. In therapeutic research, Peking University's School of Pharmaceutical Sciences partnered with computational biotech firm StoneWise to integrate pharmacological wet-lab assets into an end-to-end AI drug discovery pipeline. The initiative also addresses underlying software and computational infrastructure. The Institute of Automation at the Chinese Academy of Sciences and enterprise partner Zhongke Wenge introduced "Panshi," a multimodal scientific foundation model embedding core physical rules and adapted to run on domestic Chinese compute clusters. Upstream data curation is anchored by the Science Data Bank, a national repository managed by the Chinese Academy of Sciences' Computer Network Information Center designed to standardize, archive, and cross-share empirical scientific datasets. To bridge the gap between digital modeling and wet-lab validation, DP Technology and the AI for Science Institute, Beijing, launched the cloud-based Bohr Scientific Research Space Station, which merges literature retrieval, computational simulation, and experimental tracking into a shared workspace. Alongside the research projects, Haidian District formally launched the Zhongguancun AI for Science Innovation Cluster in its Xisanqi sector. The local administration designated 1.6 million square meters of standard industrial space alongside 740,000 square meters specifically reserved for pilot testing facilities, aiming to link early academic discoveries directly to industrial incubation.