Hugging Face's logo

Datasets:
Tommy-DING
/
organic-solar-cell-molecule-property-prediction

Tasks:
Graph Machine Learning
Modalities:
Tabular
Languages:
English
Tags:
chemistry
organic-solar-cells
molecular-property-prediction
photovoltaics
tabular
graph-machine-learning
License:
Dataset card Data Studio Files
xet
 Community
organic-solar-cell-molecule-property-prediction / README.md
Tommy-DING's picture
Tommy-DING
Update README.md
76c7e34 verified
preview code
|
Raw History Blame Contribute Delete
5.35 kB
metadata 
pretty_name: Organic Solar Cell Molecule Property Prediction
license: other
task_categories:
  - graph-ml
tags:
  - chemistry
  - organic-solar-cells
  - molecular-property-prediction
  - photovoltaics
  - tabular
  - graph-machine-learning
  - ringformer
language:
  - en

Organic Solar Cell Molecule Property Prediction

OSC Examples

Summary

This Hugging Face dataset repository publishes raw CSV tables for organic solar cell (OSC) molecule/device property prediction, used in the paper:

“RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction” (AAAI 2025).

It includes five datasets: CEPDB, HOPV, PFD, NFA, and PD.

Files in this repo

  • CEPDB.csv
  • HOPV.csv
  • PFD.csv
  • NFA.csv
  • PD.csv

Important

  • This repository contains raw/tabular CSV files only.
  • The processed ring-graph data and the code to generate it are provided in the RingFormer GitHub repository:
    https://github.com/TommyDzh/RingFormer

Dataset Overview

DATASET #GRAPHS AVG. # NODES AVG. # EDGES AVG. # RINGS
CEPDB 2.2M 27.6 33.3 6.7
HOPV 350 42.7 49.3 7.5
PFD 1055 77.1 84.2 8.2
NFA 654 118.2 133.0 15.8
PD 277 80.7 88.2 8.5

Schema (Columns)

CEPDB.csv

Header: smiles, PCE (%), Voc (V), Jsc, HOMO (eV), LUMO (eV)

  • smiles: molecule SMILES
  • PCE (%): power conversion efficiency (percent)
  • Voc (V): open-circuit voltage (volts)
  • Jsc: short-circuit current density (unit as provided in the original file; commonly mA/cm²)
  • HOMO (eV): HOMO energy level (eV; typically negative)
  • LUMO (eV): LUMO energy level (eV; typically negative)

HOPV.csv

Header: smiles,doi,inchlKEY,construction,architecture,complement,HOMO,LUMO,electrochemical_gap,optical_gap,PCE,V_OC,J_SC,fill_factor

  • smiles: molecule SMILES
  • doi: reference DOI
  • inchlKEY: InChIKey (kept as-is from the original file, including spelling)
  • construction: material category (e.g., polymer)
  • architecture: device architecture (e.g., bulk)
  • complement: complementary material (e.g., PC61BM/PC71BM)
  • HOMO, LUMO: energy levels (commonly in eV; values typically negative)
  • electrochemical_gap, optical_gap: gap-related quantities (commonly in eV)
  • PCE: power conversion efficiency (commonly %)
  • V_OC: open-circuit voltage (commonly V)
  • J_SC: short-circuit current density (commonly mA/cm²; see original file for units)
  • fill_factor: fill factor (raw data may store it in percent-like values)

Missing values can appear as nan.

PFD.csv

Header: Nickname,PCE_max(%),PCE_ave(%),Voc,Jsc,FF,Mw,Mn,PDI,Monomer,HOMO,LUMO,bandgap,SMILES

  • Nickname: material/polymer nickname
  • PCE_max(%), PCE_ave(%): max/average PCE (percent)
  • Voc, Jsc, FF: device metrics
  • Mw, Mn, PDI: molecular weight related fields
  • Monomer: monomer-related field (kept as-is)
  • HOMO, LUMO, bandgap: electronic properties
  • SMILES: structure SMILES

NFA.csv and PD.csv

Both share the same header (kept exactly as in the original CSV, including quotes/hyphens): PCE_max(%),PCE_ave(%),Jsc(mA/cm2),FF,Voc(V),HOMO_n(eV),'-LUMO_n(eV),Eg_n(eV),n(SMILES),M (g/mol),HOMO_n(eV),'-LUMO_n(eV),Eg_n(eV),p(SMILES),Mw (kg/mol),Mn(kg/mol),PDI

This can be interpreted as a paired-material record (n / p) with device performance:

  • PCE_max(%), PCE_ave(%), Jsc(mA/cm2), FF, Voc(V): device performance
  • First group (... n(SMILES)): n-side material properties and SMILES
  • M (g/mol): molecular mass field (unit in header)
  • Second group (... p(SMILES)): p-side material properties and SMILES
    (column names are not renamed in the raw file)
  • Mw (kg/mol), Mn(kg/mol), PDI: molecular-weight related fields (units in headers)

How to Use

This repository provides raw CSVs. Example:

import pandas as pd

df_cepdb = pd.read_csv("CEPDB.csv")
df_hopv = pd.read_csv("HOPV.csv")
df_pfd  = pd.read_csv("PFD.csv")
df_nfa  = pd.read_csv("NFA.csv")
df_pd   = pd.read_csv("PD.csv")

For ring-graph construction and training, use the scripts in the RingFormer GitHub repository (e.g., generate_ring_graphs.py, train.py as described in the upstream README).

Citation

If you use this dataset, please cite:

@inproceedings{ding2025ringformer,
  title={RingFormer: a ring-enhanced graph transformer for organic solar cell property prediction},
  author={Ding, Zhihao and Zhang, Ting and Li, Yiran and Shi, Jieming and Zhang, Chen Jason},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={1},
  pages={155--163},
  year={2025}
}

License

This repository republishes the original CSV tables used by RingFormer for reproducibility and re-processing. Please refer to the upstream RingFormer GitHub repository for licensing/usage terms of the data. If the upstream repository does not clearly specify a license for the data, please verify compliance before downstream use.