The platform will integrate curated molecular datasets, descriptor pipelines, and multi-objective optimization models to predict key semiconductor-relevant properties, including HOMO/LUMO energies, band-gap proxies, reorganization energy, charge-transport surrogates, and stability/processability proxies. Candidate molecules will be generated and ranked using uncertainty-aware models and Pareto optimization to balance performance, novelty, and synthetic plausibility.

A major outcome is a reproducible computational framework that shortens discovery cycles, improves hit rates compared with brute-force screening, and delivers prioritized candidate lists for future external experimental validation. The project also produces reusable data assets, benchmark protocols, and model documentation to support long-term digital R&D in molecular electronics.

The platform will integrate curated molecular datasets, descriptor pipelines, and multi-objective optimization models to predict key semiconductor-relevant properties, including HOMO/LUMO energies, band-gap proxies, reorganization energy, charge-transport surrogates, and stability/processability proxies. Candidate molecules will be generated and ranked using uncertainty-aware models and Pareto optimization to balance performance, novelty, and synthetic plausibility.

A major outcome is a reproducible computational framework that shortens discovery cycles, improves hit rates compared with brute-force screening, and delivers prioritized candidate lists for future external experimental validation. The project also produces reusable data assets, benchmark protocols, and model documentation to support long-term digital R&D in molecular electronics.