Year: 2025
Type: article
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.
Source: npj Computational Materials
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
Awards: G122673668, TRAVERSING THE “DEATH VALLEY” SEPARATING SHORT AND LONG TIMES IN NON-EQUILIBRIUM QUANTUM DYNAMICAL SIMULATIONS OF REAL MATERIALS, G1286236842, G1286251239, G1339163862, G1677143136, G1799333409, G1863003419, G2296065515, G2618837358, G2699111372, G30714459, G3083819904, G3136028036, Neural Network-Driven Atomistic Simulations for Predicting Chiroptical Properties in Ligand Induced Chiral Semiconductor Nanocrystals, G3967535168, G4174758256, G419881185, G42916606, STC: Center for Integration of Modern Optoelectronic Materials on Demand, G4501827968, G471774188, G498139845, G498862432, G5076365615, G5094606986, G573974620, G5844539934, G6208766758, G650090426, G6652958823, TRAVERSING THE “DEATH VALLEY” SEPARATING SHORT AND LONG TIMES IN NON-EQUILIBRIUM QUANTUM DYNAMICAL SIMULATIONS OF REAL MATERIALS, G969889393
OpenAlex ID: W4415198730