Semiconductor quantum dots (QDs) exhibit exciting photophysical properties for various applications, including energy conversion, lighting, and, more recently, quantum communication. The photophysics underpinning these applications at ambient conditions strongly depend on their stoichiometry and dynamic surfaces, making material design challenging. In this talk/poster, I will illustrate our attempt to explore complex structure-property-performance relationships through cutting-edge computational and graph neural network (GNN) modeling of these nanomaterials. Our detailed study shows that surface passivating ligands substantially alter the surface phonon modes of QDs and, consequently, their emission properties.[1-2] Strategic ligand passivation appears to eliminate the transient trap-states, improving the optical properties. The phonon-mediated charge-carrier relaxation dynamics, as revealed by non-adiabatic molecular dynamics, further show that the size of surface ligands controls crystal vibrations and electron–phonon coupling, thereby impacting the optoelectronics of semiconductor QDs.[3-5] We further combine ab initio methods and Atomistic Line Graph Neural Networks (ALIGNN) to predict femtosecond time-resolved electronic properties in technologically relevant Cd₂₈Se₁₇X₂₂ QDs (X = Cl, OH). These models reveal weaker vibronic coupling in Cl-passivated QDs, highlighting ligand-dependent electron-phonon interactions. ALIGNN models with ensemble learning trained on only ~10-17% of available data accurately predict bandgap and gap above the conduction band edge (ΔEgap) (MAE < 2.8 meV) across long MD trajectories. Transfer learning extends accurate electronic structure predictions to new trajectory segments with minimal retraining. The Feature Nullification Analysis framework uniquely links transient electronic properties, especially trap state formation, to atomic environments. While bandgap dynamics depend on localized atomic sites, ΔEgap stems from distributed ones. Such a scalable, atom-resolved methodology efficiently probes long-timescale quantum dynamics, offering atom-resolved insights for designing optoelectronic nanomaterials. 

References: 

1.         K Samanta et al. ACS Nano, 18, 24941 (2024)

2.         K Samanta et al. Chem Mater, 36, 5120 (2024)

3.         S Gumber et al. J Mater Chem A, 11, 8256 (2023)

4.         M Bhati et al. Nanoscale, 15, 7176 (2023)

5.         P Deswal et al., Nanoscale, 15, 17055, (2023)

6.          K Samanta et al. ACS Materials Lett (2025)