🧑‍🔬 Dongzhan Zhou

🏛️ Affiliations

🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2024–2026 · 27 paperscurrent🔬Beijing Academy of Artificial IntelligenceBeijing · China2026 · 1 papercurrent🤝China Building Materials AcademyBeijing · China2017–2025 · 18 papers🎓Beijing Jiaotong UniversityBeijing · China2014–2018 · 13 papers🎓The University of SydneySydney · Australia2019–2023 · 8 papersEmployment

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. ChemLLM: A Chemical Large Language Model (2024 · 82 citations)
   General-purpose LLMs are increasingly used for chemistry, but none had been built specifically for the field.
2. LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning (2024 · 49 citations)
   LLaMA-Berry is a framework that helps LLMs solve hard math problems, especially at Olympiad level, without retraining the base model. It tackles two weaknesses. Stepwise generation gets little feedback along the way. Iterative self-correction tends to stall in poor local solutions.
3. MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses (2024 · 36 citations)
   The paper asks whether LLMs can independently propose new, valid chemistry hypotheses when given only a research background (a question and/or a short survey) from any subfield.
4. ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2025 · 25 citations)
   Scientific hypothesis generation has had no dedicated benchmark for LLMs. ResearchBench addresses this by building on a decomposition from MOOSE-Chem. In that view, a hypothesis comes from combining a research background with one or more inspirations. Discovery can then be split into three sub-tasks: finding…
5. From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery (2025 · 19 citations)
   AI's role in research is moving from narrow predictive tools toward systems that can run much of the scientific cycle on their own.
6. Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale (2026 · 3 citations)
   Intern-S1-Pro, from Shanghai AI Laboratory, is presented as the first scientific multimodal foundation model with one trillion parameters.

🤝 Co-authors here

12 of 31 co-authors with a page, the most papers together first.

• Wanli Ouyang ×4
• Tong Xie ×3
• Yuqiang Li ×3
• Ben Gao ×2
• Di Zhang ×2
• Erik Cambria ×2
• Jiatong Li ×2
• Shufei Zhang ×2
• Zonglin Yang ×2
• Bowen Zhou
• Hang Yan
• Jinjie Ni

🔗 Cited by

Papers of our library cite their works 158 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

Updated 3 November 2026

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