Langhui Tech is deeply engaged in STEM AI data services, building 18 high-quality datasets covering five major STEM disciplines: Math, Physics, Chemistry, Biology, and Computer Science. With 2M+ research papers, 500K+ experimental data, and a 1,000+ research expert network, we provide professional, rigorous, and high-quality corpora for research large model training, empowering autonomous AI Scientist research.
From AI Scientist autonomous research to AlphaProof mathematical proofs, from AlphaFold 3 structure prediction to Galactica research large models, AI is accelerating a paradigm shift in scientific discovery
Sakana AI's AI Scientist system end-to-end completes literature review, hypothesis generation, experimental design, paper writing, and peer review. The first AI-generated paper was accepted by ICLR 2025, opening a new paradigm of autonomous research.
Google DeepMind's AlphaProof achieved silver-medal level at the International Mathematical Olympiad (IMO), breaking through the limits of large models in formal mathematical reasoning and solving multiple competition-level problems.
DeepMind released AlphaFold 3, predicting structures of proteins, DNA, RNA, and small molecule complexes, covering 200M+ protein structures, revolutionarily accelerating drug discovery and life science research.
GitHub Copilot's upgraded version supports research code generation, numerical computation, and visualization across the full pipeline, achieving SOTA in scientific computing, machine learning, and data analysis scenarios, significantly boosting research efficiency.
Meta's upgraded Galactica science large model integrates cross-disciplinary knowledge from arXiv, Nature, Science, and more, supporting literature review, formula reasoning, and experimental design across all STEM disciplines.
Nature 2026 reported AI-driven experimental design and automation platforms that automatically optimize experimental parameters and predict reaction results, boosting R&D efficiency 10x, with large-scale applications in chemical synthesis and biological experiments.
Built on trusted data as the foundation, constructing full-pipeline data infrastructure for AI research
Covering five core disciplines — Math, Physics, Chemistry, Biology, and Computer Science — from undergraduate to doctoral levels, including textbooks, exercises, lecture notes, and courseware corpora.
Top journal paper corpora from arXiv, Nature, Science, Cell, and others, covering 2M+ research papers, with structured annotations for abstracts, citations, and figures.
Full pipeline of experimental records, data annotation, and result verification, with 500K+ multimodal experimental data covering chemical synthesis, biological assays, and physical measurement scenarios.
Partnering with university professors, researchers, and PhDs to build a 1,000+ research expert review network, ensuring professional and rigorous subject annotation.
Covers core knowledge points and curriculum systems across 5 disciplines
Expert review and AI-assisted dual quality control
Full coverage of arXiv/Nature/Science/Cell
Reviewed by university professors/researchers/PhDs
18 high-quality datasets covering all STEM research scenarios
Math competition problems from IMO, Putnam, CMO, and more, including questions, solutions, knowledge points, and difficulty grading, supporting mathematical reasoning large model training.
Formalized mathematical theorem proof corpora, including Lean, Coq, Isabelle, and other proof assistant formats, supporting AlphaProof-class reasoning models.
LaTeX mathematical formula and symbol recognition corpora, including formula images, LaTeX source code, and semantic structure annotations, supporting formula OCR and generation.
Mechanics, electromagnetism, optics, and thermodynamics experiment records and data, including experimental procedures, parameters, results, and error analysis annotations.
Classical mechanics and electromagnetism problems and solutions, with structured reasoning chain annotations for force analysis, electric and magnetic fields, and circuits.
Quantum mechanics principles, calculations, and simulation data, with structured annotations for wave functions, operators, and entangled states, supporting quantum research AI.
SMILES molecular formulas, 3D structures, and property data, covering organic, inorganic, and biological molecules, supporting molecular design and property prediction.
Chemical reaction equations, reaction mechanisms, and product prediction data, with annotations for reaction types, conditions, yields, and stereochemistry.
Synthesis experiment records, operating procedures, and product characterization data, with multimodal annotations supporting experiment automation AI training.
Species classification, morphological features, and ecological habit data, covering animals, plants, and microorganisms, supporting biodiversity research.
DNA, RNA, and protein sequence and structure data, with functional annotations and mutation information, supporting AlphaFold-class structure prediction models.
Ecosystem, population, and environmental factor data, with annotations for species relationships, food chains, and biodiversity, supporting ecological research.
Multi-language code corpora in Python, Java, C++, and more, with comments, documentation, and unit tests, supporting code generation large model training.
Algorithm problem banks from LeetCode, Codeforces, and more, including questions, solutions, complexity analysis, and algorithm classifications, supporting code reasoning models.
Programming language tutorials, documentation, and tech blog corpora, including syntax, APIs, and best practices, supporting programming assistants and documentation generation AI.
From ready-to-use trusted datasets to industrial-grade custom annotation, to certified expert review networks — Langhui STEM data's three pipelines collaboratively support the full lifecycle of research large models
18 standardized STEM datasets with unified JSON format, covering Math/Physics/Chemistry/Biology/Computer Science — buy and use immediately, ready for training out of the box.
On-demand custom research data annotation solutions, with self-developed AI-assisted annotation platforms and expert review processes, supporting large-scale industrial data production.
Partnering with university professors, researchers, and PhDs to build a 1,000+ research expert review network, ensuring professional and rigorous subject annotation.
Six typical AI application scenarios based on Langhui STEM datasets, empowering the full research pipeline
AI-assisted paper retrieval, literature review, and knowledge graph construction, boosting research efficiency 10x, covering 2M+ research papers.
AI-assisted paper writing, formula typesetting, and citation management, providing full-pipeline support from draft to final manuscript, accelerating academic publication.
AI-driven experimental plan design, parameter optimization, and result prediction, accelerating R&D 10x, applied in chemical synthesis and biological experiments.
Automated research code generation, full-pipeline assistance for numerical computation, visualization, and machine learning, significantly improving research efficiency.
Automated experimental data analysis, statistical modeling, and visualization, supporting deep mining of multimodal experimental data.
Cross-disciplinary knowledge association and discovery, mining potential research hypotheses, accelerating scientific discovery and the birth of new theories.
Contact Langhui STEM data experts to get the complete dataset catalog, trial access, and customized solutions, accelerating your research AI deployment