Search for: All records

Abstract The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics of nanoscale materials. We present “DeepPseudopot”, a machine-learned atomistic pseudopotential model that extends the SEPM framework by combining a flexible neural network representation of the local pseudopotential with parameterized non-local and spin-orbit coupling terms. Trained on bulk quasiparticle band structures and deformation potentials from GW calculations, the model captures many-body and relativistic effects with very high accuracy across diverse semiconducting materials, as illustrated for silicon and group III-V semiconductors. DeepPseudopot’s accuracy, efficiency, and transferability make it well-suited for data-driven in silico design and discovery of novel optoelectronic nanomaterials. 

Colloidal quantum dots, with their size-tunable optoelectronic properties and scalable synthesis, enable applications in which inexpensive high-performance semiconductors are needed. Synthesis science breakthroughs have been key to the realization of quantum dot technologies, but important group III–group V semiconductors, including colloidal gallium arsenide (GaAs), still cannot be synthesized with existing approaches. The high-temperature molten salt colloidal synthesis introduced in this work enables the preparation of previously intractable colloidal materials. We directly nucleated and grew colloidal quantum dots in molten inorganic salts by harnessing molten salt redox chemistry and using surfactant additives for nanocrystal shape control. Synthesis temperatures above 425°C are critical for realizing photoluminescent GaAs quantum dots, which emphasizes the importance of high temperatures enabled by molten salt solvents. We generalize the methodology and demonstrate nearly a dozen III-V solid-solution nanocrystal compositions that have not been previously reported.