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

Organic Solar Cell Molecule Property Prediction

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

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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

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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.