Datasets:
polyVERSE polymer property database
Polymer properties compiled by the Ramprasad group, one config per property family; gated under the GTRC noncommercial license.
Part of the Molecular Datasets collection.
Get started
from datasets import load_dataset
dataset = load_dataset("liuganghuggingface/moldata-polyverse", "anion_aging")
Where the data comes from
- Original data: https://github.com/Ramprasad-Group/polyVERSE/tree/main/Other/Solvent_Diffusivity_Sorption_MTL_NCM
- Original study: AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes
- Original study: Active-learning thermoset formulations
- Original study: AI-driven design of fluorine-free polymers for sustainable and high-performance anion exchange membranes
- Original study: polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Tables
| Config | Contents | Grade | Splits | Rows | Targets |
|---|---|---|---|---|---|
anion_aging |
Anion-exchange membrane conductivity aging | care | full | 2,150 | value (regression; units not specified) |
anion_uptake |
Anion-exchange membrane conductivity, uptake and swelling | care | full | 1,018 | value (regression; units not specified) |
atomization |
polyVERSE atomization enthalpy | ready | full | 390 | value Atomization enthalpy (regression; eV/atom) |
bandgap_chain |
polyVERSE chain band gap | ready | full | 4,209 | bandgap_chain Band gap (single chain) (regression; units not specified) |
bandgap_crystal |
polyVERSE crystalline band gap | ready | full | 236 | bandgap_crystal Band gap (crystal) (regression; units not specified) |
charge_injection |
Polymer charge injection barriers | ready | full | 1,826 | eib Electron injection barrier (regression; units not specified) |
chi |
Flory–Huggins interaction parameters | care | full | 1,586 | chi Flory–Huggins interaction parameter (χ) (regression; dimensionless) |
cohesive |
polyVERSE cohesive energy density | care | full | 294 | value_COE Cohesive energy density (regression; cal/cm^3) |
electrical |
polyVERSE electrical conductivity | ready | full | 398 | log10 of electrical_conductivity (S/cm) Electrical conductivity (regression; units not specified) |
electron_affinity |
polyVERSE electron affinity | ready | full | 368 | value Electron affinity (regression; units not specified) |
gas_transport |
polyVERSE gas transport and expanded polymer–solvent transport | care | full | 7,826 | value (regression; units not specified) |
gas_transport_long |
polyVERSE gas transport and expanded polymer–solvent transport | care | full | 6,788 | value (log) (regression; units not specified) |
ionization |
polyVERSE ionization energy | ready | full | 370 | value Ionization energy (regression; units not specified) |
melt_viscosity |
Polymer melt viscosity | care | full | 1,959 | Melt_Viscosity Melt Viscosity (regression; Poise) |
rop_enthalpy |
Ring-opening polymerization enthalpy | care | full | 459 | roe_kj/mol Ring-opening polymerization enthalpy (regression; kJ/mol) |
solar |
Polymer organic solar cells | care | full | 422 | PCE Power conversion efficiency (regression; %), Jsc Short-circuit current density (regression; mA/cm^2), Voc Open-circuit voltage (regression; V), FF Fill factor (regression; fraction) |
solvent_diffusion |
Solvent diffusivity in polymers | care | full | 3,044 | Target Diffusivity Value(log10) Solvent diffusivity (regression; units not specified) |
solvent_sorption |
Solvent uptake and sorption in polymers | care | full | 2,277 | Uptake Sorption(log10) Solvent uptake (regression; units not specified) |
data/ contains rows with readable molecular structures; identical observations are collapsed. observations/ contains all source rows.
Source label columns are text, including values such as <10. SMILES_original retains the input representation; SMILES_canonical provides a canonical representation. structure_key groups molecular components, and structure_status records parsing outcomes.
Files and modalities
| Modality | Where | Content |
|---|---|---|
| source files | files/thermosets/LICENSE (0 MB) |
Original source file in its native format. |
| source files | files/thermosets/README.md (0 MB) |
Original source file in its native format. |
| source files | files/thermosets/data.csv (0 MB) |
Original source file in its native format. |
Download an individual file:
from huggingface_hub import hf_hub_download
path = hf_hub_download("liuganghuggingface/moldata-polyverse", "files/thermosets/LICENSE", repo_type="dataset")
Data quality
Each config is graded on its own (Grade column above); metadata.json lists each config's quality tags.
Dataset notes
- Repeated structures at different conditions or compositions are not duplicate measurements.
- Repository code license does not supersede the GTRC data terms.
- The expanded transport release includes solvents and nonstandard polymer pseudoatoms ([g], [t], [d], [e]); this collection reads them as attachment points ([*]).
- gas_transport_long.csv and the expanded data.csv share some measurements.
- property identifies gas/solvent, transport endpoint, and experimental versus simulated origin.
- Aug=0 identifies original measurements; Aug=1 identifies fitted augmentation.
- Sample_Type and Weight 1/Weight 2 describe blends and copolymers.
- Literature records were extracted with LLM assistance and include original DOI and device-processing context.
- Experimental Selector and Simulated Selector identify measured and MD-derived labels.
License
GTRC General Public Use License Agreement; noncommercial resale restrictions; retain complete license
Citation
Cite the original study and data source:
- AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes
- polyVERSE dataset citation
- AI-driven design of fluorine-free polymers for sustainable and high-performance anion exchange membranes
- polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
- Novel high voltage polymer insulators using computational and data-driven techniques
- Estimation of the Flory-Huggins interaction parameter of polymer-solvent mixtures using machine learning
- An Informatics Approach for Designing Conducting Polymers
- Gas permeability, solubility and diffusivity
- A physics-enforced neural network to predict polymer melt viscosity
- Accelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning
- From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models
- Solvent uptake and sorption in polymers
- https://doi.org/10.1038/s41467-023-40257-2
- https://doi.org/10.1038/s41524-025-01681-8
- Machine-Guided Discovery of Acrylate Photopolymer Compositions
- Downloads last month
- 35