Molecular data & discovery code
Photosensitizer Dataset & Active-Learning Resources
DA and DAD photosensitizer records with quantum-chemical energy labels, molecular building blocks, and code for molecular-space construction and active learning.
Explore the molecular data
The public repository contains 14,164 molecular records in two tables, plus 238 donor, acceptor and bridge records used to define molecular building blocks and connections. The molecular tables pair SMILES representations with quantum-chemical energy labels.
| File | Records | Contents |
|---|---|---|
| Photosensitizers_DA.csv | 8,051 | Donor–acceptor molecular records and energy labels |
| Photosensitizers_DAD.csv | 6,113 | Donor–acceptor–donor molecular records and energy labels |
| Donor_Acceptor_Bridge_Substructures.csv | 238 | Building-block SMILES or connection definitions, with type labels |
These counts refer to the repository files checked on 7 September 2026. Some bridge entries specify connections, such as a single bond, rather than standalone molecules.
Understand the labels
| Column | Meaning | Unit |
|---|---|---|
| Index | Record identifier within its source table | — |
| SMILES | Molecular structure representation | — |
| ST Gap | S1–T1 energy gap | eV |
| HL Gap | HOMO–LUMO energy gap | eV |
| S1 | First singlet excited-state energy | eV |
| T1 | First triplet excited-state energy | eV |
The repository describes ground-state optimization at the B3LYP/6-31G(d) level, with excited-state characteristics calculated using TD-DFT at the same level of theory. These are computational labels. Keep the source table identity alongside each Index when combining DA and DAD records.
Connect the data to the discovery study
The study explored a molecular space of more than seven million molecules and reported 5,357 potential high-performance photosensitizers. Four photosensitizers were subsequently synthesized and evaluated.
The 14,164 labeled records, the 5,357 shortlisted candidates and the experimentally tested compounds describe different sets. The two data tables should not be presented as a catalog of experimentally validated materials or as the complete candidate shortlist.
The paper’s Supporting Information includes a photosensitizer database and substructure-list archive, together with experimental details.
Open the JACS paper and Supporting Information
Work with the code
The repository includes molecular-space construction, graph-convolutional prediction models, active-learning code and analysis scripts. Its prediction Notebook is a useful entry point for understanding how prepared models are used.
- Inspect the prediction Notebook.
- Explore the active-learning implementation.
- Explore molecular-space construction.
Follow the repository’s model-download and environment instructions before running its examples. For reuse, retain the original records and keep any cleaned or reformatted data as a separately documented derivative.
Credit and cite
Code and data were contributed by Jiali Li and Pengfei Cai. Cite the JACS paper for the study and identify the code/data version used in your work. The Zenodo V1.0.0 archive provides a fixed historical release alongside the original GitHub repository.