Linear Models and Quantum Descriptors for DSSC Efficiency Prediction

E.F.S. Mattos developed predictive linear regression models for dye-sensitized solar cell (DSSC) efficiency using quantum molecular descriptors. The models, validated against standard metrics, evaluate organic dyes based on imidazole, BODIPY, and squaraine. The dataset includes the models and supporting data in DOCX and XLSX formats, totaling 611.4 KB.

Use Cases

• Predict DSSC efficiency based on quantum molecular descriptors derived from dye electronic structure.
• Identify key electronic characteristics for efficiency enhancement, such as HOMO-LUMO gap control.
• Evaluate the performance of specific organic dye families (imidazole, BODIPY, squaraine) for solar cell applications.
• Validate linear regression models for material property prediction using standard metrics.

Strengths

• Models satisfy all standard validation metrics, indicating robustness.
• The dataset includes quantum molecular descriptors derived from molecular electronics structure and excited states.
• The study focuses on three specific organic dye families (imidazole, BODIPY, squaraine).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is 611.4 KB, indicating a limited scope.

Provenance

Source

figshare

Collection Method

Models developed using linear regression on quantum molecular descriptors.

Freshness

Last updated 2026-04-27 08:48:14; freshness should be verified.