rise_ai_scientists

# *Corral* – Rise of AI Scientists <div align="center">  [](https://lamalab-org.github.io/corral/) [](https://lamalab-org.github.io/corral/docs/) [](https://github.com/lamalab-org/corral) [](https://opensource.org/licenses/MIT) [](https://arxiv.org/abs/2604.18805) [](https://huggingface.co/datasets/jablonkagroup/rise_ai_scientists) *Bibliometric evidence for the rise of AI scientists in chemistry and materials science relative to general AI for chemistry* </div> --- ## 📋 Dataset Summary This dataset is part of the *Corral* collection accompanying the paper [*AI scientists produce results without reasoning scientifically*](https://arxiv.org/abs/2604.18805). It contains the **bibliometric evidence** for the growing relevance and impact of **AI scientists** in chemistry and materials science compared to **general AI for chemistry**. The central finding is that AI scientists are rapidly gaining importance in the scientific landscape: their outputs, tools, and research impact are outpacing those of general AI for chemistry works, signaling a structural shift toward domain-specialist AI in the natural sciences. This resource is designed for **scientometric analysis**, trend forecasting, and benchmarking of AI scientist systems — not for general-purpose model pre-training. ### 🎯 Supported Uses - 📈 Tracking the growth and adoption of AI scientists relative to general AI for chemistry - 📊 Comparing publication and impact metrics across specialist and generalist AI systems - 🔁 Reproducing and extending the growth analyses reported in the accompanying paper - 🔬 Studying authorship patterns and yearly trends in AI-driven scientific discovery --- ## 🧪 About *Corral* [*Corral*](https://lamalab-org.github.io/corral/) is a framework for the *science of agents and agents for science*. It provides a microservice architecture that **decouples agents from environments** via a client–server design (REST API), ensuring flexibility, reproducibility, and robust isolation. - 🌍 **Environments** define the task space, available tools, and observable feedback — from chemistry labs to HPC clusters. - 🤖 **Agents** are modular LLM-based entities supporting scaffolds such as ReAct, ToolCalling, LLMPlanner, and Reflection. - 📝 **Tasks** define problems to solve, complete with scoring functions. Tasks can be chained into TaskGroups for complex multi-stage challenges. *Corral* currently ships **8 environments**, **97 tools**, **115 tasks**, and **786 subtasks** spanning chemistry, physics, and materials science. ### 🌍 Environments | Environment | Description | 🔧 Tools | 📝 Tasks/scope | 🔭 Scopes | ⏱️ Avg. trace length | |---|---|:---:|:---:|:---:|:---:| | 🧫 **Inorganic Qualitative Analysis** | Identify unknown cations in solution through systematic wet-lab procedures (reagent addition, flame tests, pH measurement, centrifugation, etc.). Observations are computed from thermodynamic data. Three scopes progressively increase the number of candidate ions. | 14 | 10 | 3 | 39.4 | | ⚡ **Circuit Inference** | Recover the topology and component values of a hidden resistor network from pairwise resistance measurements. Tools provide series/parallel calculations, delta-wye transforms, and circuit validation. | 9 | 6 | 1 | 15.0 | | 🔭 **Spectroscopic Structure Elucidation** | Determine the molecular structure of an unknown compound by requesting and interpreting spectroscopic data (MS, NMR, HSQC, IR) alongside reference databases for chemical shifts and isotope distributions. | 16 | 20 | 2 | 15.1 | | 🧬 **Retrosynthetic Planning** | Design multi-step synthetic routes to target molecules under cost, step-count, and commercial-availability constraints, using a template catalogue and functional-group detection tools. | 15 | 8 | 3 | 25.5 | | 🤖 **ML-based Property Prediction** | Assemble a complete ML pipeline to predict formation energies of material polymorphs using data from the Materials Project, covering feature engineering, XGBoost training, and cross-validation. | 14 | 3 | 1 | 16.6 | | 🔬 **AFM Experiment Execution** | Analyze and interpret atomic force microscopy data for nanoscale surface characterization, including topographical and mechanical property measurements. | 6 | 1 | 4 | 26.3 | | ⚛️ **Molecular Simulation** | Design and execute molecular dynamics simulations with LAMMPS to predict materials properties, covering the full workflow from crystal structure retrieval to force-field queries and log analysis. | 8 | 2–3 | 2 | 30.4 | | 🏗️ **Adsorption Surface Construction** | Build adsorbate–slab configurations from bulk crystal structures for heterogeneous catalysis studies, integrating Materials Project retrieval, slab generation, and adsorption-site enumeration. | 15 | 3 | 1 | 19.6 | --- ## 🗂️ Dataset Structure ### Configs The dataset is organized into **5 configs**, sourced from [OpenAlex](https://openalex.org/) keyword searches: | Config | Description | |---|---| | `works_all_hits` | All hits returned for the combined keyword search (AI scientists + general AI for chemistry) | | `works_expanded` | All hits from the combined search, expanded with additional metadata and context | | `works_strict` | Hits returned exclusively for the AI scientists keyword search | | `yearly_authors` | Author-level records extracted from the search results | | `yearly_counts` | Yearly disclosure counts for each keyword category | Each config contains all records returned by the respective OpenAlex keyword search, enabling longitudinal and cross-sectional comparisons across the field. ### Data Splits All configs expose a single `train` split. ### Data Instances Each row corresponds to a single record returned by the OpenAlex API for the respective keyword search. --- ## 🏗️ Dataset Creation ### Curation Rationale This dataset was created as part of *Corral* to provide bibliometric evidence for the growing role of AI scientists in chemistry and materials science. It enables quantitative comparison of publication output, author activity, and yearly trends between AI scientist systems and general AI for chemistry, supporting the analyses reported in the accompanying paper. ### Source Data Records are derived from keyword searches on [OpenAlex](https://openalex.org/), an open bibliographic database of scholarly works. Separate searches were performed for AI scientist-specific terms and for general AI for chemistry terms. All returned hits — including paper metadata, author information, and yearly disclosure counts — are collected as-is, without additional filtering beyond the keyword query. --- ## 🔗 Relation to Other Corral Artifacts This dataset is one component of the broader *Corral* release and is best interpreted together with the matching task definitions, execution traces, reports, aggregate results, and reasoning annotations available in the [*Corral* collection](https://huggingface.co/collections/jablonkagroup/corral). --- ## 📄 Citation ```bibtex @article{ríos-garcía2026ai, title = {AI scientists produce results without reasoning scientifically}, author = {Martiño Ríos-García and Nawaf Alampara and Chandan Gupta and Indrajeet Mandal and Sajid Mannan and Ali Asghar Aghajani and N. M. Anoop Krishnan and Kevin Maik Jablonka}, year = {2026}, journal = {arXiv preprint arXiv: 2604.18805} } ``` ## 📜 License This dataset is released under the [MIT License](https://opensource.org/licenses/MIT). ## Changelog ### 2026-04-22 - Initial release of the dataset card.