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72 entries in Chemistry

Showing 48 of 72

aqcat25-datasetSandboxAQ13.5M DFT calculation trajectories for heterogeneous catalysis and ML potential training.Computational ChemistryExplore
AtompackLeMaterialAtompack — Hub-hosted public atomistic ML datasets distributed in the Atompack format, part of the broader LeMaterial effort to standardise atomistic data exchange.Atomistic ML FormatExplore
B3DBmaomlabBlood-Brain Barrier Database (B3DB) — curated permeability measurements for compounds, supporting CNS drug-discovery ML benchmarks.BBB PermeabilityExplore
BixBenchfuturehouseBenchmark with 205 reproducible research questions paired with data capsules for AI evaluation.Research BenchmarkExplore
ChAFFmaomlabChAFF — chemistry dataset for ML benchmarking on filtered/curated molecular properties, part of the Maom Lab pharmacology suite.Chemistry DatasetExplore
ChemBenchjablonkagroupManually curated benchmark of 3,000+ chemistry and materials science questions across spectroscopy, reactivity, synthesis, and property prediction for evaluating LLMs.Chemistry BenchmarkExplore
chempile-captionjablonkagroupImage-to-text dataset of chemistry figures (molecular structures, reaction schemes, plots) with expert captions for training multimodal chemistry models.Chemistry CaptioningExplore
chempile-codejablonkagroupCurated chemistry-relevant code (RDKit, ASE, simulation tooling) drawn from The Stack — supports training models that can read and write computational chemistry workflows.Chemistry Code CorpusExplore
chempile-educationjablonkagroupEducational chemistry corpus — multiple-choice and open-ended items spanning introductory through graduate chemistry for assessing model educational capability.Chemistry Education CorpusExplore
chempile-instructionjablonkagroupInstruction-tuning corpus for chemistry — curated Q&A and dialogue traces drawn from chemical literature and educational sources for training chemistry-specialist LLMs.Chemistry Instruction CorpusExplore
chempile-liftjablonkagroupChemPile-LIFT — large-scale language-modelling dataset combining curated chemistry literature and structured chemical knowledge for foundation-model pretraining.Chemistry PretrainingExplore
chempile-mliftjablonkagroupCurated lift-off subset of the ChemPile corpus for instruction-tuning and benchmarking chemistry language models across synthesis, property prediction, and reaction tasks.MolecularExplore
chempile-paperjablonkagroupLarge corpus of peer-reviewed chemistry papers and preprints for pre-training and fine-tuning chemistry language models.Scientific LiteratureExplore
chempile-reasoningjablonkagroupMulti-step chemistry reasoning corpus — open-domain QA, NLI, and multiple-choice items with chains of reasoning for training and evaluating chemical reasoning models.Chemistry Reasoning CorpusExplore
CycPepMPDBLiteFoldCycPeptMPDB — public dataset of experimentally measured membrane permeability for cyclic peptides, the standard resource for peptide-permeability modelling.Peptide PropertiesExplore
dataset_exfoliationefoundry-mlExfoliation energy dataset for 2D materials — supports ML-driven discovery of layered compounds suitable for monolayer isolation.2D MaterialsExplore
dataset_li_conductivityfoundry-mlLithium-ion conductivity dataset for solid electrolytes — supports ML discovery of next-generation battery materials.Battery MaterialsExplore
dataset_perovskite_asrfoundry-mlArea-specific resistance (ASR) data for perovskite electrodes — used in solid-oxide fuel cell ML modelling.Perovskite PropertiesExplore
dataset_perovskite_conductivityfoundry-mlIonic and electronic conductivity measurements for perovskite materials — supports ML screening for solid-oxide fuel cell electrolytes.Perovskite PropertiesExplore
dataset_perovskite_formationefoundry-mlFormation energies for perovskite compounds — supports ML screening for stability and synthesisability.Perovskite PropertiesExplore
dataset_perovskite_habsfoundry-mlHot-air-balance (HABS) data for perovskite materials — thermal-stability characterisation supporting durability ML.Perovskite PropertiesExplore
dataset_perovskite_stability_updatedfoundry-mlCurated perovskite stability data (updated release) for benchmarking ML models on photovoltaic-material durability prediction.Perovskite PropertiesExplore
dataset_perovskite_tecfoundry-mlThermal expansion coefficients for perovskite materials — curated for ML thermal-property prediction.Perovskite PropertiesExplore
diffusion_v1-4foundry-mlDiffusion-coefficient dataset for inorganic systems — supports ML modelling of solid-state ion transport and electrolyte design.Diffusion CoefficientsExplore
double_perovskite_bandgap_v1-1foundry-mlComputed band gaps for double-perovskite compounds — supports ML-based screening for photovoltaic and optoelectronic applications.Electronic StructureExplore
drug-target-activityeve-bioDrug-target interaction measurements for 1,397 FDA-approved small molecule drugs.Drug DiscoveryExplore
elwood_md_v1-2foundry-mlElwood molecular-dynamics simulation set — trajectory and energy data for ML molecular-property prediction.Molecular DynamicsExplore
EmeraldBaytahoebioEmerald Bay — single-cell perturbation dataset of 1.8M+ transcriptomic profiles spanning 52 cell lines × 91 drug treatments (plus combinations), generated on Tahoe’s MOSAIC high-throughput platform with paired transcriptional and drug-phenotype readouts over a five-day culture.Single-Cell PerturbationExplore
ether0-benchmarkfuturehouseChemistry reasoning benchmark covering SMILES-based tasks including reaction prediction, retrosynthesis, and molecular property estimation for evaluating chemistry LLMs.Chemistry BenchmarkExplore
excess-propertiesmist-modelsExcess-property dataset for binary/ternary chemical mixtures — used to fine-tune MIST mixtures models on thermodynamic deviations from ideal mixing.Mixture PropertiesExplore
foundry_aflow_band_gaps_v1-1foundry-mlBand-gap values from the AFLOW high-throughput materials database, formatted for ML model training and evaluation.Electronic StructureExplore
foundry_g4mp2_solvation_v1-2foundry-mlHigh-accuracy G4MP2 solvation-energy data — supports ML for quantum-chemical accuracy on aqueous and organic systems.Solvation EnergiesExplore
foundry_moses_v1-1foundry-mlFoundry mirror of MOSES — molecular sets benchmark for evaluating generative chemistry models on drug-like molecule generation.Molecular GenerationExplore
foundry_mp_band_gaps_v1-1foundry-mlBand-gap values curated from the Materials Project for ML benchmarking on inorganic electronic structure.Electronic StructureExplore
foundry_oqmd_band_gaps_v1-1foundry-mlBand-gap values from the Open Quantum Materials Database (OQMD), prepared for ML benchmarking on inorganic crystal electronic structure.Electronic StructureExplore
foundry_osdb_v1-1foundry-mlOrganic Semiconductor Database (OSDB) curated for ML — supports property prediction and screening of organic optoelectronic materials.Organic SemiconductorsExplore
foundry_qmc_ml_v1-1foundry-mlQuantum Monte Carlo (QMC) reference data for ML benchmarking — high-accuracy electronic structure calculations on small molecules.Quantum ChemistryExplore
LeMat-BulkLeMaterialPrimary bulk materials database aggregating 1M+ crystal structures with DFT-computed formation energies, band gaps, and elastic properties for materials discovery.Materials ScienceExplore
LeMat-Bulk-DFT-HullLeMaterialDFT convex hull reference data for materials stability analysis.Materials ScienceExplore
LeMat-Bulk-DFT-Hull-AllLeMaterialComplete DFT convex hull dataset for bulk materials discovery.Materials ScienceExplore
LeMat-Bulk-MLIP-Formation-EnergiesLeMaterialFormation energies for LeMat-Bulk computed with multiple ML interatomic potentials (MLIPs) — used to benchmark MLIPs and to derive MLIP-based stability estimates.Materials ScienceExplore
LeMat-Bulk-MLIP-HullLeMaterialConvex hull data for bulk materials from MLIP calculations.Materials ScienceExplore
LeMat-BulkUniqueLeMaterialDeduplicated companion to LeMat-Bulk — unique inorganic crystal structures aggregated across source databases, intended as a curated training set for generative materials models.Materials ScienceExplore
LeMat-GenBench-embeddingsLeMaterialPrecomputed embeddings for the LeMat-GenBench benchmark — drop-in features for evaluating generative materials models on crystal-structure prediction and downstream tasks.Materials EmbeddingsExplore
LeMat-RhoLeMaterialLeMat-Rho — ~69,000 inorganic crystal structures computed at the r2SCAN meta-GGA level in VASP, with charge densities, Bader charges, forces, stresses, and energies for ML training and electronic-structure analysis.Materials ScienceExplore
LeMat-SynthLeMaterialLeMat-Synth — curated dataset of materials synthesis procedures and conditions, supporting work on synthesizability prediction and synthesis-route generation.Materials SynthesisExplore
LeMat-Synth-PapersLeMaterialLeMat-Synth-Papers — curated corpus of materials synthesis literature, intended for training and evaluating models that extract synthesis procedures and recipes from text.Materials LiteratureExplore
LeMat-TrajLeMaterialLarge-scale molecular dynamics trajectory dataset for training machine learning interatomic potentials across diverse bulk material compositions.Materials ScienceExplore

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