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Materials & Chemistry Datasets

# Awesome Materials & Chemistry Datasets

About

A curated list of the most useful datasets in materials science and chemistry for training machine learning and AI foundation models. This includes experimental, computational, and literature-mined datasets—prioritizing open-access resources and community contributions.

This project aims to: - Catalog the best datasets by domain, type, quality, and size - Support reproducible research in AI for chemistry and materials - Provide a community-driven resource with contributions from researchers and developers

Table of Contents

Contributing

Want to add a new dataset or improve metadata?

  1. Fork the repository
  2. Edit the appropriate dataset list or add a new entry
  3. Submit a pull request with a brief description and download link OR
  4. Submit as an issue

Datasets

Computational Datasets

Dataset Domain Size Type Format
BOOM: Benchmarks for Out-Of-distribution Molecules Small molecules 10 Out-Of-Distribution Tasks (1M+ entries) Computational CSV
MSR-ACC/TAE25 Small molecules 77k CCSD(T)/CBS atomization energies Computational JSON
OMat24 (Meta) Inorganic crystals 110M DFT entries Computational JSON/HDF5
OMol25 (Meta) Molecular chemistry 100M+ DFT calculations Computational LMDB
OMC25 Molecular crystals >27M structures Computational Zarr
Materials Project (LBL) Inorganic crystals 500k+ compounds Computational JSON/API
Open Catalyst 2020 (OC20) Catalysis (surfaces) 1.2M relaxations Computational JSON/HDF5
AFLOW Inorganic materials 3.5M materials Computational REST API
OQMD Inorganic solids 1M+ compounds Computational SQL/CSV
JARVIS-DFT (NIST) 3D/2D materials 40k+ entries Computational JSON/API
Carolina Materials DB Hypothetical crystals 214k structures Computational JSON
NOMAD Various DFT/MD >19M calculations Computational JSON
MatPES DFT Potential Energy Surfaces ~400,000 structures from 300K MD simulations Computational JSON
Vector-QM24 Small organic and inorganic molecules 836k conformational isomers Computational JSON
AIMNet2 Dataset Non-metallic compounds 20M hybrid DFT calculations Computational JSON
RDB7 Barrier height and enthalpy for small organic reactions 12k CCSD(T)-F12 calculations Computational CSV
RDB19-Rad ΔG of activation and of reaction for organic reactions in 40 common solvents 5.6k DFT + COSMO-RS calculations Computational CSV
QCML Small molecules consisting of up to 8 heavy atoms 14.7B Semi-empirical + 33.5M DFT calculations Computational TFDS
QM9 Small organic molecules 134k molecules with quantum properties Experimental SDF/CSV
QM7/QM7b Small molecules 7k molecules with atomization energies Experimental SDF/CSV
QMugs Drug-like molecules 665 k mol / 2 M conf Computational HDF5
C2DB 2-D materials ~4 000 entries Computational JSON/API
ANI-1x / 1ccx Small organic mol 5 M (DFT) + 0.5 M (CCSD) Computational HDF5
CoRE MOF 2019 Metal-organic frameworks 14 763 structures Computational CIF/JSON
QMOF Database Metal-organic frameworks 20k+ structures (DFT) Computational CIF/JSON
Catalysis-Hub Surface reactions >100 k energies Computational JSON/API
ODAC23 MOF + COâ‚‚/Hâ‚‚O adsorption 38 M DFT calcs Computational HDF5
MOFX-DB Gas adsorption in MOFs 3 M isotherm pts Computational CSV/HDF5
LeMat-Bulk Inorganic materials (bulk) 6.7M structures (5.9M materials) Computational HuggingFace Dataset
LeMat-Traj Inorganic materials (trajectories) 113M structures Computational HuggingFace Dataset
NeurIPS Open Polymer Prediction 2025 Polymers ~1,500 test polymers with MD-derived properties Computational CSV
Carbon Data Carbon materials 22.9M atoms, 546 trajectories Computational EXTXYZ
MSR-ACC/TAE25 Small molecules (up to Ar) 76,879 total atomization energies Computational HDF5/CSV
DFT Solvation Energy Dataset Small molecules 651,290 solvation energies in 5 solvents Computational CSV/JSON
MD Simulated Monomer Properties Small molecules 410 molecules with thermodynamic properties Computational CSV/JSON
Multimodal Spectroscopic Dataset Molecular spectroscopy 790k molecules with simulated spectra Computational HDF5/JSON
PubChemQCR Small molecules (relaxation) 3.5M trajectories / 300M conformations Computational HuggingFace Dataset
MP-ALOE Universal MLIPs (89 elements) ~1M r2SCAN DFT calculations Computational JSONL/MACE
Alexandria DB Inorganic (1D–3D) >5 M DFT calcs (PBE) Computational JSON/OPTIMADE/LMDB
Quantum‑Chemical Bonding DB (LOBSTER) Solid‑state bonding analysis 1,520 compounds Computational JSON
MultixcQM9 (OpenQDC) Small molecules (QM9, multi‑XC) 133k molecules Computational Torch/NumPy
SPICE (OpenQDC) Drug‑like molecules 1 M conformers (energies & forces) Computational Torch/ASE
Matbench v0.1 Benchmarks (13 tasks) 10 datasets Benchmark/Comp CSV/HDF5
Matbench Discovery Stability, κ, structures Multiple files Benchmark/Comp CSV/ZIP
Materials Cloud Archives Various DFT/MD workflows 1,000+ datasets Computational HDF5/JSON/CIF
MS25 MLIP benchmark (6 material systems) Multi-system benchmark suite Computational/Benchmark HDF5
RadonPy Polymer Properties Data Polymer ~1070 MD-calculated Properties Computational CSV
SHNITSEL Data Organic Molecules 418,870 Post-HF-calculated Ground- and Excited-states Properties Computational XARRAY
Frustrated Lewis Pairs Database Small Molecules 146 Metal-free FLPs Computational HTML
AQCat25 Catalysis 13.5M frames / 5K materials Computational Parquet/ASE DB
OMol25 Electronic Structures Molecular chemistry 4M+ calculations Computational Raw DFT outputs
Unrestricted CCSD(T) Dataset For Organic Molecule Reactions Organic reactions 3119 configurations Computational
MC-PDFT-OPESf Reaction kinetics Diels-Alder reaction Computational
Quantum Cluster Database Nanoclusters 63,015 clusters Computational CSV/JSON
The Cambridge Cluster Database Mixed Clusters Multiple Files Computational Multiple Types
Battery Electrolyte Solvation/Ionization Organic molecules Thousands of molecules Computational

Experimental Datasets

Dataset Domain Size Type Format
Crystallography Open Database (COD) Crystal structures ~525k entries Experimental CIF/SMILES
NIST ICSD (subset) Inorganic structures ~290k structures Experimental CIF
CSD (Cambridge) Organic crystals ~1.3M structures Experimental CIF
opXRD Crystal structures 92552 (2179 labeled) Experimental JSON
MDR SuperCon Superconductivity legacy superconductor database w/ material composition, structure, properties, and processes Mixed
ChEMBL Bioactive molecules 2.3M+ compounds with bioactivity data Experimental JSON/SDF
MoleculeNet Molecular properties 700k+ compounds across 17 datasets Mixed CSV/SDF
ESOL Aqueous solubility 1,128 compounds with solubility data Experimental CSV
FreeSolv Hydration free energy 643 molecules with experimental data Experimental CSV
Lipophilicity Octanol/water distribution 4,200 compounds with logD values Experimental CSV
PCBA Bioassay screening 400k+ compounds, 128 bioassays Experimental CSV
HIV Antiviral screening 41k compounds with HIV inhibition data Experimental CSV
BACE Beta-secretase inhibitors 1,522 compounds with IC50 data Experimental CSV
BBBP Blood-brain barrier permeability 2,053 compounds with permeability data Experimental CSV
Tox21 Toxicity screening 8k compounds, 12 toxicity targets Experimental CSV
ToxCast High-throughput toxicity 8k compounds, 600+ assays Experimental CSV
SIDER Drug side effects 1,427 drugs with adverse reactions Experimental CSV
ClinTox Clinical trial toxicity 1,491 compounds with FDA approval status Experimental CSV
PDBbind Protein-ligand binding 19k complexes with binding affinities Experimental PDB/SDF
BindingDB Protein-ligand binding 2.8M+ binding data points Experimental CSV/SDF
ProtBENCH Drug-target interactions Protein family-specific datasets Experimental CSV
PDBench Protein sequence design 595 protein structures, 40 architectures Experimental PDB
PDB-Struct Structure-based protein design Comprehensive protein design benchmark Experimental PDB
HTEM-DB Thin-film composition libraries 140 k+ samples Experimental JSON/API
OCx24 Electrocatalyst inks 572 samples (+DFT) Experimental CSV
Polymer Genome Polymers 20 k polymers Experimental + Comp CSV/JSON
CoRE MOF 2024 Metal-organic frameworks 40k+ experimental MOFs Experimental CIF
SAIR Protein-ligand binding 1M+ complexes, 5.2M structures, 2.5TB Experimental 3D/CSV
Anion Solvation DB Anion solvation ~26k properties Mixed CSV
BigSolDB Organic molecule solubility ~54k exp. values Experimental CSV
StarryData2 Experimental properties Figshare dump (2023/2024) Experimental CSV/JSON
CRIPT Polymer Data Polymers (synthesis, properties) Growing community DB Mixed JSON/API
Catechol Benchmark Solvent selection / Reaction yield 1200+ process conditions Experimental CSV
Leeds Solubility Data Solubility 2.3k measurements Experimental CSV
BigSolDB 2.0 Solubility 103k+ values Experimental CSV/XLSX
OpenExp Chemical reactions 274k pairs Experimental Varies
Battery Imaging Library (BIL) Battery imaging 80+ scans, >500B voxels Experimental Various

LLM Training Datasets

Dataset Domain Size Type Format
ChemPile Chemistry 75B+ tokens LLM Training Mixed
SmolInstruct Small molecules 3.3M samples LLM Training JSON
CAMEL Chemistry 20K problem-solution pairs LLM Training JSON
ChemNLP Chemistry Extensive, many combined datasets LLM Training JSON
ChemQA Chemistry Multimodal QA dataset LLM Training JSON
ChemLLMBench Chemistry 8 chemistry tasks benchmark LLM Training JSON
ChemistryQA Chemistry 4,500 questions across 200 topics LLM Training JSON
MaScQA Materials Science 640 QA pairs LLM Training XLSX
SciCode Research Coding in Physics, Math, Material Science, Biology, and Chemistry 338 subproblems LLM Training JSON
ChemData 700K Chemistry (9 core tasks) 730K Q-A instruction pairs LLM Training JSON
MatSci-Instruct (HoneyBee) Materials science ≈55K verified instructions LLM Training JSON
MoleculeQA Molecular properties & safety 62K multiple-choice QA pairs LLM Training JSON
BioInstruct 25K Biomedical / biochemistry 25K GPT-4 generated instructions LLM Training JSON
Lab-Bench Biology 2,400+ questions for biology agents LLM Training JSON
ChemBench 4K Chemistry competency benchmark 4,100 single-choice questions LLM Training JSON
GPQA Diamond Biology, Physics, Chemistry 448 multiple-choice questions LLM Training JSON
MaCBench Chemistry and materials science Vision-language tasks LLM Training JSON
ChemBench Chemistry 2,700+ question-answer pairs LLM Training JSON
MatText Materials property prediction 2M structures LLM Training HuggingFace Dataset
SciAssess Scientific literature analysis Benchmark for LLMs in science LLM Training JSON
ZINC20-ML Drug-like molecules (SMILES) ≈1B molecules LLM Training SMILES
PMC Open Access Subset Biomedical full-text 3.4M+ articles LLM Training XML
MatScholar Task-Schema QA (MatSci-NLP) Materials science (7 NLP tasks) Tens of thousands of examples LLM Training JSON
Mol-Instructions Chemistry molecular, protein, and biochemical instructions LLM Training HuggingFace Dataset
USPTO-LLM Chemical reactions 247K reactions LLM Training JSON/Graph
ChemRxivQuest Chem literature QA 970 QA pairs LLM Training JSON
USPTO-Lowe Patent reactions 1.8 M reactions Literature-mined RXN/SMILES
MolTextNet Small molecules with text 2.5M molecule-text pairs LLM Training HuggingFace Dataset
MolOpt-Instructions Molecule optimization 1.18M instruction-based optimization tasks LLM Training HuggingFace Dataset
TextEdge Crystal properties Crystal text descriptions with properties LLM Training JSON
LAMBench-TrainingSet-v1 Materials structures 19.8M structures for Large Atom Models LLM Training Various
LLM4Mat Materials property prediction 1.9M crystal structures, 45 properties, 3 modalities LLM Training Various
LLM-EO Transition metal complexes / Optimization 1.37M TMC space explored LLM Training GitHub
Flavor Analysis and Recognition Transformer Molecular taste prediction Multi-class taste classification dataset LLM Training SMILES/JSON
SCQA (Solar Cell QA) Solar cells 47K QA pairs LLM Training JSON
ScienceQA K–12 science, multimodal MCQs w/ lectures & explanations 21,208 Qs LLM Training/Eval JSON
SciBench College-level scientific problem solving (math/chem/phys) Open & closed sets LLM Eval PDF/JSON
MegaScience Scientific reasoning (7 disciplines) 1.25M instances (650k reasoning questions from 12k textbooks) LLM Training HuggingFace Dataset
Mat-Instructions Inorganic materials ~30k instructions LLM Training JSON
Open Materials Guide (OMG) Materials synthesis 17K synthesis recipes LLM Training JSON
ChemDFM Chemistry 34B tokens / 2.7M instructions LLM Training HuggingFace
ChemTable Chemistry Tables Large-scale benchmark LLM Training/Benchmark JSON
ChemCoTBench Molecular reasoning Annotated datasets LLM Training/Benchmark HuggingFace Dataset

Literature-mined & Text Datasets

Dataset Domain Size Type Format
PubChem Molecules & data 119M compounds Literature SMILES/SDF
Open Reaction Database (ORD) Synthetic reactions ~1M reactions Experimental/Lit JSON
PatCID (IBM) Chemical image data 81M images / 13M mols Literature PNG/SMILES
MatScholar NLP corpus (materials) 5M+ abstracts Literature JSON/Graph
Matbench (metadata/text tasks) Text/meta ML tasks 13 tasks Literature/Benchmark CSV
OpenQDC Hub QM molecules & reactions 1.5 B geometries Literature/Computational Python API/NPZ
L2M3 - Large Language Model MOF Miner Metal-organic frameworks from >40k articles Literature-mined CSV

🌊 Computational Fluid Dynamics, PDE & Engineering Datasets

Dataset Domain Size Type Format
PDEBench PDE solving / Scientific ML Multiple datasets Benchmark / Simulation HDF5/PyTorch
BLASTNet Fluid mechanics / Reacting flows 17 TB Simulation / CFD HDF5/NPY
Johns Hopkins Turbulence DB (JHTDB) DNS/LES turbulence (9 canonical flows) ≈ 350 TB Simulation Web API / HDF5 cutouts
Airfoil CFD 2k 1,830 airfoils × 25 AoA × 3 Re ~6 GB (250 k cases) Simulation HDF5
PDEArena (collection) 2-D Navier–Stokes, Shallow-Water, 3-D Maxwell ≈ 100 GB (4 datasets) Simulation Torch / HDF5
WeatherBench 2 Global weather reanalysis (ERA5, 1979-2023) ≈ 5 TB Reanalysis NetCDF/Zarr
UT Austin Channel-DNS Suite Incompressible channel flow Reτ 180 – 5200 ≈ 10 TB Simulation Binary / ASCII
Compressible TPC DNS DB Compressible channel flow (25 M, Reτ*) ~2 GB Simulation TXT tables
Curated RANS ↔ DNS Dataset 29 geometries, 4 RANS models w/ DNS/LES labels 1.1 GB Simulation HDF5/CSV
NASA Common Research Model (CRM) Aircraft CRM geom. + wind-tunnel & CFD results Multi-GB Mixed (Exp + Sim) CAD / CSV / Tecplot
Darcy-Flow (FNO) 2-D porous-media pressure fields (∇·k∇u = f) ≈ 1 GB (10 k samples) Simulation HDF5
HiFi-TURB LES/DNS High-fidelity LES/DNS for complex 3D flows Multi-case suite Simulation (DNS/LES) HDF5/NetCDF
NASA High Lift Prediction Workshop (HLPW) High-lift aircraft configurations Multi-GB Mixed (exp + CFD) CAD/CSV/Tecplot
High-Speed TBL DNS DB Compressible turbulent boundary layers DNS database Simulation HDF5
ML Turbulence (Kaggle) RANS Reynolds stress tensor data ~GB scale Benchmark/Simulation CSV/HDF5

Proprietary Datasets (for reference)

Dataset Domain Size Use Case Notes
CAS Registry Chemical substances 250M+ substances Industry standard for molecule indexing
Reaxys (Elsevier) Reactions & properties Millions of reactions Rich curated literature reaction data
Citrine Informatics DB Experimental materials Private Materials ML platform w/ industry data
CSD (Cambridge) Organic crystals 1.3M+ Gold-standard X-ray structures
PoLyInfo Polymers & properties 500k+ data points / Experimental Polymer properties from literature sources

Dataset Resources

  • The Materials Data Facility - Over 100 TB of open materials data. #TODO list some of these in the tables above
  • Foundry-ML search Foundry - 61 structured datasets ready for download through a Python client #TODO list some of these in the tables above

TODO

  • Add all OpenQDC datasets https://www.openqdc.io/datasets
  • A dataset on solubilities of gases in polymers (15 000 experimental measurements of 79 gases' uptakes (0.01–50 wt%) in 102 different polymers, pressures from 1 × 10−3 to 7 × 102 bar and temperatures from 233 to 508 K, includes nearly 500 solvent–polymer systems). Optimized structures of various repeating units are included. Should it be of interest for you, it is available here: Data
  • Add Materials Cloud Datasets
  • Classify Atomly. A bit challenging with non-English
  • Look into adding NOMAD for experimental data as well
  • Add A Quantum-Chemical Bonding Database for Solid-State Materials Part 1: https://zenodo.org/records/8091844 Part 2: https://zenodo.org/records/8092187
  • Add QM datasets. http://quantum-machine.org/datasets/
  • Find link for | ChemRxivQuest | Chemistry literature QA | 970 curated QA pairs | LLM Training | JSON | CC BY 4.0 | Open | ChemRxivQuest |
  • Find new link for USPTO-Reactions | USPTO Reactions | Organic reactions | 1.8M reactions | Literature | RXN/SMILES | Open | Open |
  • Find dataset link for | SciCUEval | Multidomain scientific comprehension (bio/chem/phys/matsci) | 10 sub-datasets | LLM Eval | JSON/PDF | Open | Open |
  • Find dataset for | MatSciKB | Materials science KB | 38.5k entries (20k papers, 3.6k Wikipedia, 1.9k textbooks, 10.5k datasets) | Literature | Structured text | Open | Open |

License

This project is licensed under the MIT License. Each dataset listed has its own license, noted in the table. Always check the source's license before using the data in your project.

Acknowledgements

The primary effort of Ben Blaiszik on this project was performed under financial assistance award 70NANB24H049 / MML24-1001 from the National Institute of Standards and Technology (NIST).

Thanks to the open data and research communities including: - Meta AI FAIR - The Materials Data Facility / Foundry-ML - NIST JARVIS and Materials Project - LBL, MIT, CCDC, FIZ Karlsruhe - Contributors to Open Catalyst, PubChem, ORD, and AFLOW - Developers of open chemistry toolkits (RDKit, Open Babel)

Citation

If this repository was helpful in your work, feel free to cite or star the repo. You can also reference the underlying dataset publications linked above.

Changelog

This Changelog is autogenerated, there may be errors.

October 2025

Added 18 new datasets focusing on catalysis, reaction kinetics, cluster chemistry, experimental solubility, literature mining, and foundation models to enhance resources for computational chemistry and machine learning applications.

🧮 Computational Datasets (7 datasets)

  • AQCat25: The AQCat25 dataset provides a large and diverse collection of 13.5 million DFT calculation trajectories, encompassing approximately 5K materials and 47K intermediate-catalyst systems. It is designed to complement existing large-scale datasets by providing calculations at higher fidelity and including critical spin-polarized systems, which are essential for accurately modeling many industrially relevant catalysts.
  • OMol25 Electronic Structures Dataset: The OMol25 Electronic Structures dataset includes the raw DFT outputs, electronic densities, wavefunctions, and molecular orbital information for over 4M million high-accuracy quantum chemical calculations. We see this as a transformative opportunity to develop higher quality partial charges, partial spins, and advanced electronic features to unlock the next generation of physics-informed ML models.
  • Unrestricted CCSD(T) Dataset For Organic Molecule Reactions: Dataset of 3119 organic molecule configurations at gold-standard quantum accuracy with automated workflows for unrestricted CCSD(T) calculations. Includes a transferable MLIP trained on UCCSD(T) data, showing significant improvements in force and activation energy accuracy.
  • MC-PDFT-OPESf: This work combines multi-configuration pair-density functional theory (MC-PDFT) as an accurate and efficient multireference electronic structure method with on-the-fly probability enhanced sampling flooding (OPESf) as an enhanced sampling method capable of accelerating reactive transitions. MC-PDFT–OPESf provides reaction rates in agreement with experiments at a fraction of the computational cost required by conventional unbiased ab-initio calculations.
  • Quantum Cluster Database: A database of 63015 low-energy atomically precise nanoclusters for 55 elements across the periodic table, including main group and transition metal elements.
  • Cambridge Cluster Database: A collection of results from global optimizations for a variety of cluster systems, including Lennard-Jones, metal, molecular, and ionic clusters. The database is continuously updated with new results from published papers.
  • Battery Electrolyte Solvation/Ionization: This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities for thousands of organic molecules from QM9, EGP, GDB17, and ZINC.

🧪 Experimental Datasets (5 datasets)

  • Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning, providing the first-ever transient flow dataset for machine learning benchmarking, covering over 1200 process conditions. This dataset focuses on solvent selection, a task that is particularly difficult to model theoretically.
  • BNNLab/Solubility_data: Leeds Solubility Data: Curated solubility data in organic solvents and water and descriptors for solubility prediction.
  • BigSolDB 2.0: A comprehensive dataset of 103,944 experimentally measured solubility values of 1,448 organic compounds in 213 solvents reported in 1,595 literature peer-reviewed articles.
  • OpenExp: Features 274,439 pairs of chemical reactions and their corresponding step-by-step instructions of experimental procedures. This dataset, compiled from the USPTO-Applications and ORD databases.
  • Battery Imaging Library (BIL): An open, curated collection of multi-modal and multi-length scale battery imaging datasets, featuring over 80 scans and 500 billion voxels of data from single particles to full cells.

📚 LLM Training Datasets (5 datasets)

  • Mat-Instructions: A large-scale inorganic material instruction dataset with ~30k instruction-response pairs, designed to unlock the potential of LLMs in materials science.
  • Open Materials Guide (OMG): A dataset of 17K high-quality, expert-verified synthesis recipes from open-access literature, which forms the basis for the AlchemyBench benchmark for LLM-guided synthesis prediction.
  • ChemDFM: A pioneering LLM for chemistry trained on 34B tokens from chemical literature and textbooks, and fine-tuned using 2.7M instructions. As a result, it can understand and reason with chemical knowledge in free-form dialogue.
  • ChemTable: A large-scale benchmark of real-world chemical tables curated from the experimental sections of literature. ChemTable supports table recognition and table understanding tasks to advance scientific reasoning in chemistry.
  • ChemCoTBench: A reasoning framework that bridges molecular structure understanding with arithmetic-inspired operations to formalize chemical problem-solving into transparent, step-by-step workflows for tasks like molecular optimization and reaction prediction.

📖 Literature-mined & Text Datasets (1 dataset)

  • L2M3 (Large Language Model MOF Miner): A database of MOF synthesis conditions and properties extracted from over 40,000 research articles using LLMs, enabling analysis of synthesis-structure-property relationships.

August 2025

Enhanced scientific reasoning capabilities and machine learning interatomic potential benchmarking with 6 new high-quality datasets for AI scientists and materials researchers.

🧮 Computational Datasets (3 datasets)

  • OMC25: A collection of over 27 million molecular crystal structures containing 12 elements and up to 300 atoms in the unit cell. The dataset was generated from dispersion-inclusive density functional theory (DFT) relaxation trajectories of over 230,000 randomly generated molecular crystal structures of around 50,000 organic molecules.
  • MS25: Comprehensive benchmark dataset for evaluating machine learning interatomic potentials (MLIPs) across 6 diverse materials systems including MgO surfaces, liquid water, zeolites, catalytic Pt surface reactions, high-entropy alloys, and disordered Zr-oxides. Evaluates 5 MLIP architectures (MACE, NequIP, Allegro, MTP, Torch-ANI) with focus on derived physical observables beyond traditional energy/force metrics. Demonstrates that equivariant MLIPs offer 1.5-2× improvements over nonequivariant models in complex systems, while highlighting the importance of explicit validation of physical properties rather than relying solely on error metrics.
  • Added the * Frustrated Lewis Pairs Database ** of 146 Metal-free FLPs

📚 LLM Training Datasets (4 datasets)

  • MaCBench: A comprehensive benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks across three core aspects: data extraction, experimental understanding, and results interpretation. Reveals fundamental limitations in spatial reasoning and cross-modal information synthesis in leading models.
  • ChemBench: A cutting-edge framework to evaluate the chemical knowledge and reasoning capabilities of large language models (LLMs). It includes over 2,700 curated question-answer pairs across diverse chemistry topics and uniquely encodes chemical semantics, enabling models to process and reason about molecules and equations.
  • MatText: A comprehensive benchmarking framework spanning multiple representations and model scales, which finds that LLMs consistently fail to capture coordinate information while excelling at category patterns. This geometric blindness persists regardless of model size, dataset scale, or text representation strategy.
  • MegaScience: Large-scale scientific reasoning dataset featuring 1.25 million high-quality instances across 7 scientific disciplines. Includes TextbookReasoning component with 650k reasoning questions extracted from 12,000 university-level textbooks, providing truthful reference answers for training AI scientists. Developed through systematic ablation studies and comprehensive evaluation across 15 benchmarks, demonstrating superior performance and training efficiency compared to existing open-source scientific datasets.

July 2025

Expanded the collection into new scientific domains with 31 new datasets, introducing benchmarks for physics-based machine learning, adding comprehensive quantum mechanics datasets, expanding materials science resources, and enhancing scientific evaluation benchmarks.

🌊 Computational Fluid Dynamics, PDE & Engineering Datasets (15 datasets)

  • PDEBench: A comprehensive benchmark suite for scientific machine learning featuring a wide range of Partial Differential Equations. It provides large, ready-to-use datasets for challenging physics problems, supporting both forward and inverse modeling.
  • BLASTNet: A 17 TB collection of high-fidelity fluid mechanics simulation datasets for ML applications in automotive, propulsion, and energy sectors. It includes code and pre-trained models for tasks like turbulence modeling and spatio-temporal prediction.
  • JHTDB: multi-terabyte DNS/LES portal with isotropic, channel, MHD, boundary-layer and atmospheric datasets.
  • Airfoil CFD 2k: DOE/NREL benchmark: 1,830 shapes × 250 k RANS simulations; HDF5 + AWS mirror.
  • PDEArena: Hugging-Face org offering Navier–Stokes, Shallow-Water & Maxwell tensors; MIT license.
  • WeatherBench 2: ERA5-derived Zarr cubes for data-driven medium-range forecasting; MIT.
  • UT Austin DNS Suite: public HTTP server with ReÏ„ 180–5200 channel data & statistics.
  • Compressible TPC DNS DB: 25 Reynolds–Mach cases, plain-text statistics (Mendeley Data).
  • Curated RANS ↔ DNS: Scientific Data descriptor + Kaggle mirror for ML turbulence closures.
  • NASA CRM: open CAD, grids, wind-tunnel Cp & force/moment datasets for the community benchmark.
  • Darcy Flow (FNO): canonical permeability→pressure dataset used in FNO/PINO papers.
  • HiFi-TURB LES/DNS: EU-funded project providing high-fidelity Large Eddy Simulation and Direct Numerical Simulation datasets for complex 3D turbulent flows, supporting advanced turbulence modeling and AI/ML applications in computational fluid dynamics.
  • NASA High Lift Prediction Workshop (HLPW): Multi-phase workshop datasets featuring high-lift aircraft configurations with comprehensive experimental validation data, CAD geometries, and CFD solutions for aerodynamic modeling and validation.
  • High-Speed TBL DNS DB: Specialized database of Direct Numerical Simulation data for compressible turbulent boundary layers, providing detailed flow field information for high-speed aerodynamic applications and turbulence model development.
  • ML Turbulence (Kaggle): Community-contributed dataset featuring RANS Reynolds stress tensor data with ground truth labels, providing a standardized benchmark for machine learning approaches to turbulence modeling.

🧮 Computational Datasets (7 datasets)

  • PubChemQCR: A massive dataset of molecular relaxation trajectories for ~3.5 million small molecules, containing over 300 million conformations with energy and force labels. It is the largest public dataset of its kind, designed to accelerate the development of machine learning interatomic potentials (MLIPs).
  • MP-ALOE: Nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation, covering 89 elements. Created using active learning and primarily consisting of off-equilibrium structures, MP-ALOE is designed for training universal machine learning interatomic potentials (UMLIPs) with strong performance on thermochemical properties, force prediction, and physical soundness under extreme conditions.
  • Alexandria DB: Massive computational materials database containing over 5 million DFT calculations using PBE functional for 1D-3D inorganic materials. Provides OPTIMADE-compliant API access and LMDB format for high-performance materials screening and property prediction workflows.
  • Quantum-Chemical Bonding DB (LOBSTER): Specialized dataset providing detailed bonding analysis for 1,520 solid-state compounds using LOBSTER methodology. Enables understanding of chemical bonding in crystalline materials through projected crystal orbital Hamilton populations and related descriptors.
  • MultixcQM9 & SPICE (OpenQDC): Enhanced quantum chemistry datasets within the OpenQDC framework. MultixcQM9 provides multi-exchange correlation functional data for 133k small molecules, while SPICE offers 1 million conformers with energies and forces for drug-like molecules, both optimized for machine learning applications.
  • Matbench v0.1 & Discovery: Comprehensive benchmarking suites for materials property prediction featuring 13 standardized tasks across 10 datasets. Matbench Discovery specifically targets stability prediction, thermal conductivity, and structure generation with rigorous evaluation protocols.
  • Materials Cloud Archives: Centralized repository of over 1,000 computational datasets from various DFT and molecular dynamics workflows. Provides standardized access to diverse materials science calculations with comprehensive metadata and version control.

📚 LLM Training Datasets (5 datasets)

  • LLM-EO (Evolutionary Optimization): A framework that integrates LLMs into evolutionary algorithms for optimizing transition metal complexes. This approach leverages the chemical knowledge of LLMs to surpass traditional genetic algorithms, enabling flexible, multi-objective optimization without complex mathematical formulations.
  • Flavor Analysis and Recognition Transformer: A state-of-the-art machine learning model dataset for predicting molecular taste from chemical structures. Built on ChemBERTa transformer architecture, it classifies molecules across four taste categories (sweet, bitter, sour, umami) with >91% accuracy, enabling interpretability through gradient-based visualizations and applications in flavor compound discovery and rational food design.
  • SCQA (Solar Cell QA): Domain-specific question-answering dataset containing 47,268 QA pairs about solar cell properties, auto-generated using ChemDataExtractor. Fine-tuning language models on this dataset achieves F1-scores exceeding general-English QA datasets by 10-20%, demonstrating the value of domain-specific training data for specialized scientific applications.
  • ScienceQA: Comprehensive K-12 science education dataset with 21,208 multimodal multiple-choice questions including lectures and explanations. Supports development of educational AI systems and scientific reasoning capabilities in language models.
  • SciBench: College-level scientific problem-solving benchmark covering mathematics, chemistry, and physics with both open and closed evaluation sets. Enables systematic assessment of LLM performance on advanced scientific reasoning tasks.

🧪 Experimental Datasets (4 datasets)

  • Anion Solvation DB: Comprehensive compilation of 26,000+ solvation properties including 8,241 experimental pKa values across 8 solvents, 5,536 computed gas-phase acidities, and over 12,000 solvation energies for anions and neutral compounds computed using COSMO-RS. Bridges experimental and computational approaches for understanding anion behavior in different solvation environments.
  • BigSolDB: Extensive experimental solubility database containing 54,273 measured solubility values across temperature range 243.15-403.15 K in various organic solvents and water. Features diverse chemical space coverage with interactive t-SNE exploration tool and comprehensive statistical analysis for QSPR model development.
  • StarryData2: Large-scale experimental properties dataset from Figshare spanning 2023-2024, providing comprehensive experimental measurements across diverse materials and chemical systems for machine learning model validation and training.
  • CRIPT Polymer Data: Community-driven polymer database featuring synthesis procedures, characterization data, and properties. Enables standardized data sharing and collaborative research in polymer science through structured JSON API access.

June 2025

Added 28 new high-quality datasets spanning polymer science, drug discovery, carbon materials, spectroscopy, MOF databases, foundation model training, and materials knowledge bases:

🧮 Computational Datasets (15 datasets)

  • NeurIPS Open Polymer Prediction 2025: Kaggle competition dataset for predicting 5 key polymer properties (Tg, FFV, Tc, density, Rg) from SMILES structures using MD simulation ground truth. Includes ~1,500 test polymers.
  • Carbon Data: 22.9 million atom dataset with synthetic energy labels from C-GAP-17 potential, featuring 546 carbon trajectories across diverse densities and temperatures. Captures nanotubes, graphitic films, diamond, and amorphous carbon environments.
  • MSR-ACC/TAE25: Microsoft Research's comprehensive dataset of 76,879 total atomization energies computed at CCSD(T)/CBS level using W1-F12 protocol. Exhaustively covers chemical space for elements up to argon with sub-chemical accuracy (±1 kcal/mol).
  • DFT Solvation Energy Dataset: 651,290 computed solvation energies for 130,258 molecules from QM9 dataset across 5 solvents (acetone, ethanol, acetonitrile, DMSO, water). Achieves 0.5 kcal/mol MAE for small molecules with accompanying ML models and web interface.
  • MD Simulated Monomer Properties: GPU-accelerated molecular dynamics dataset of thermodynamic properties for 410 molecules, generated through active learning pipeline. Includes validation against experimental data and automated simulation workflow.
  • Multimodal Spectroscopic Dataset: Comprehensive spectroscopic dataset with simulated 1H-NMR, 13C-NMR, HSQC-NMR, Infrared, and Mass spectra for 790k molecules from patent reactions. Enables multimodal foundation model development for structure elucidation and functional group prediction.
  • QMugs: 665k drug-like molecules with ~2M conformers, featuring quantum mechanical properties at both semi-empirical (GFN2-xTB) and DFT (ωB97X-D/def2-SVP) levels.
  • C2DB (Computational 2D Materials Database): ~4,000 two-dimensional materials with computed structural, electronic, magnetic, and optical properties.
  • ANI-1x / ANI-1ccx: 5 million DFT and 500k CCSD(T) calculations for organic molecules, supporting machine learning potential development.
  • CoRE MOF 2019: 14,763 computation-ready metal-organic frameworks with solvent and charge balancing, suitable for high-throughput screening.
  • QMOF Database: Comprehensive database of quantum-chemical properties for 20,000+ metal-organic frameworks derived from high-throughput periodic density functional theory calculations.
  • Catalysis-Hub Surface Reactions: Over 100,000 adsorption and reaction energies on catalytic surfaces, accessible via a Python/GraphQL API.
  • ODAC23 (Open DAC 2023): 38 million DFT calculations of COâ‚‚/Hâ‚‚O adsorption on 8,400 MOFs, aimed at direct-air-capture sorbent discovery.
  • MOFX-DB: Over 3 million simulated adsorption data points across 160,000 MOFs and 286 zeolites for various gases.
  • Enhanced QCML dataset entry with more comprehensive description of coverage and properties

🧪 Experimental Datasets (5 datasets)

  • SAIR (Structurally Augmented IC50 Repository): Largest public protein–ligand binding dataset with over 1 million complexes and 5.2 million cofolded 3D structures (2.5TB total). Combines experimental binding affinities from ChEMBL/BindingDB with Boltz-1x predicted structures.
  • CoRE MOF 2024: Updated database of over 40,000 experimentally reported metal-organic frameworks from literature through early 2024. Includes pre-computed material properties for high-throughput material-process screening and carbon-capture applications.
  • HTEM-DB (High-Throughput Experimental Materials Database): More than 140,000 composition–process–property data points from combinatorial sputtering experiments, with optical, electrical, and structural measurements.
  • OCx24 (Open Catalyst Experiments 2024): 572 synthesized catalyst inks evaluated with matched XRF/XRD and DFT adsorption energies, bridging the gap between simulation and laboratory data.
  • Khazana / Polymer Genome: Approximately 20,000 polymers with DFT-calculated properties and experimental dielectric data, supporting machine learning on soft materials.

📚 LLM Training Datasets (5 datasets)

  • MolTextNet: 2.5 million high-quality molecule-text pairs from ChEMBL35, featuring GPT-4o-mini generated descriptions 10x longer than existing datasets. Integrates structural features, computed properties, bioactivity data, and synthetic complexity for multimodal molecular modeling.
  • MolOpt-Instructions: 1.18 million instruction-based molecule optimization tasks for fine-tuning LLMs on drug discovery. Supports interactive human-machine dialogue for molecule optimization through the DrugAssist framework, enabling expert feedback integration and iterative refinement.
  • TextEdge: Benchmark dataset for predicting crystal properties from natural language text descriptions. Demonstrates superior performance of LLM-based approaches over traditional GNN methods, with improvements of 8% on band gap prediction and 65% on unit cell volume prediction.
  • LAMBench-TrainingSet-v1: Massive training dataset for Large Atom Models (LAMs) containing 19.8 million valid structures from the OpenLAM Initiative. Includes 1 million structures on the convex hull for advancing generative modeling and materials science applications.
  • LLM4Mat: Comprehensive benchmark dataset for evaluating LLMs in materials property prediction, containing 1.9M crystal structures from 10 data sources with 45 distinct properties. Features three input modalities (crystal composition, CIF, text description) with 4.7M, 615.5M, and 3.1B tokens respectively.

📖 Literature-mined & Text Datasets (3 datasets)

  • MatSciKB: Comprehensive materials science knowledge base with 38,469 curated entries across 16 categories. Integrates ArXiv papers (20,384), Wikipedia articles (3,620), textbooks (1,930), datasets (10,473), formulas (57), and GPT-generated examples (2,005) with efficient CRUD operations for research applications.
  • ChemRxivQuest: 970 curated question–answer pairs spanning 17 chemistry subfields, designed for retrieval-augmented generation and factuality assessments.
  • USPTO-Lowe Reactions (1976–2016): 1.8 million atom-mapped reactions extracted from US patents, serving as a benchmark for reaction prediction and retrosynthesis models.

📚 Enhanced Literature & Benchmark Resources (2 datasets)

  • Matbench (metadata/text tasks): Extended benchmarking suite providing 13 standardized tasks for text-based and metadata-driven materials property prediction. Enables systematic evaluation of natural language processing approaches in materials science applications.
  • OpenQDC Hub: Comprehensive quantum chemistry database aggregating 1.5 billion molecular geometries and quantum mechanical properties. Provides unified Python API access to diverse quantum chemistry datasets with standardized formats for large-scale machine learning applications.

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Earlier Updates

For changes made earlier than the changelog entries, please see the repository commit history.

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