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86 entries in Materials Science

Showing 48 of 86

ABC-1MADSKAILabOne million CAD-quality 3D shapes drawn from the ABC dataset — the foundation training corpus for the Make-A-Shape and WaLa generative models.CAD Geometry CorpusExplore
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
atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1-1foundry-mlSimulated STM images for 2D materials with unique chemical compositions — supports ML on atomic-resolution microscopy.STM ImagingExplore
atomvison_simulated_atomistic_stem_images_2d_materials_unique_chemical_compositions_structure_bafoundry-mlSimulated STEM images for 2D materials — paired with structure metadata for training ML models on electron microscopy.STEM ImagingExplore
ChemBenchjablonkagroupManually curated benchmark of 3,000+ chemistry and materials science questions across spectroscopy, reactivity, synthesis, and property prediction for evaluating LLMs.Chemistry BenchmarkExplore
crosslab-nmcGenData-ResearchCrossLab-NMC is a full-text, provenance-linked corpus for evaluating scientific claims against existing experimental evidence. It contains 2,005 specific-capacity measurements from 227 open-access battery papers across labs, predominantly on NMC cathodes, each linked to its source passage and to the material and experimental conditions needed to determine whether two experiments are comparable.Materials science (energy storage devices)Explore
dataset_concrete_compressive_strengthfoundry-mlConcrete compressive-strength dataset — mix-design and test data for ML-based civil-engineering material modelling.Construction MaterialsExplore
dataset_debyet_aflowfoundry-mlDebye temperature data from the AFLOW database — fundamental thermal-vibrational descriptor for ML materials property prediction.Thermal 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_metallicglass_dmaxfoundry-mlMaximum glass-forming diameter (Dmax) data for bulk metallic glasses — for ML screening of casting feasibility.Metallic GlassExplore
dataset_metallicglass_rcfoundry-mlCritical cooling rate (Rc) data for metallic glasses — supports ML prediction of glass-forming ability.Metallic GlassExplore
dataset_metallicglass_rc_llmfoundry-mlLLM-extracted critical cooling rate data for metallic glasses — text-mined complement to the structured Rc dataset.Metallic GlassExplore
dataset_mg_alloyfoundry-mlMagnesium alloy dataset — composition and property data for ML modelling of lightweight structural alloys.Alloy PropertiesExplore
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
dataset_rpv_ttsfoundry-mlReactor pressure-vessel (RPV) transition-temperature shift dataset — supports ML prediction of irradiation embrittlement.Reactor MaterialsExplore
dataset_thermalcond_aflowfoundry-mlThermal conductivity values from the AFLOW database — supports ML-based screening of thermal management materials.Thermal PropertiesExplore
dataset_thermalexp_aflowfoundry-mlThermal expansion coefficients from the AFLOW database — for ML thermal-mechanical modelling of inorganic materials.Thermal PropertiesExplore
dielectric_constant_v1-1foundry-mlDielectric-constant values for inorganic compounds — supports ML screening of high-k materials for capacitors and devices.Dielectric PropertiesExplore
diffusion_v1-4foundry-mlDiffusion-coefficient dataset for inorganic systems — supports ML modelling of solid-state ion transport and electrolyte design.Diffusion CoefficientsExplore
direct_electron_detectorceleritas_xs_simulated_readout_images_electron_counting_model_v1-1foundry-mlSimulated readout images from a Celeritas XS direct-electron detector — training data for electron-counting models in cryo-EM and STEM.Electron MicroscopyExplore
double_perovskite_bandgap_v1-1foundry-mlComputed band gaps for double-perovskite compounds — supports ML-based screening for photovoltaic and optoelectronic applications.Electronic StructureExplore
elastic_tensor_v1-1foundry-mlElastic tensor data for inorganic materials — supports ML prediction of bulk and shear moduli.Mechanical PropertiesExplore
electromigration_v1-1foundry-mlElectromigration data for interconnect materials — supports ML prediction of failure rates in microelectronic devices.Failure MechanismsExplore
elwood_md_v1-2foundry-mlElwood molecular-dynamics simulation set — trajectory and energy data for ML molecular-property prediction.Molecular DynamicsExplore
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_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_stan_segmentation_v1-1foundry-mlSegmentation dataset (STAN) for materials microscopy images — supports ML feature extraction from electron-microscopy data.Microscopy SegmentationExplore
heusler_magnetization_v1-1foundry-mlMagnetisation data for Heusler-alloy compounds — supports ML discovery of half-metallic and magnetocaloric materials.Magnetic PropertiesExplore
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

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