Exploring Quantum-Dot Engineered Solid-State Photon Upconversion in PbS:$Yb^{3+},Er^{3+}$/CuBiO Using Density Functional Theory and Machine Learning Methods for Water Splitting

TL;DR

This paper combines density functional theory and machine learning to analyze a quantum-dot heterostructure, revealing its promising properties for water splitting and hydrogen production through enhanced optical and electronic features.

Contribution

It introduces a novel computational framework integrating DFT and machine learning to optimize and analyze quantum-dot heterostructures for photocatalytic water splitting.

Findings

Optimized heterostructure exhibits a 0.431 eV indirect bandgap for visible-light absorption.

Machine learning models accurately predict photon absorption rates with low error.

Internal electric field and charge transfer enhancements improve photocatalytic efficiency.

Abstract

This study presents a comprehensive numerical analysis of a quantum-dot-engineered heterostructure, PbS:$Yb^{3+},Er^{3+}$/CuBiO, optimized for water splitting applications. Using density functional theory (DFT) coupled with machine learning, the study explores the electronic, optical, and catalytic properties of the material. The optimized PbS structure exhibited a direct bandgap of 1.191 eV, while co-doping with Yb and Er transitioned the material to a metallic state, enhancing charge carrier mobility and electron-hole separation. The final heterostructure displayed an indirect bandgap of 0.431 eV, favorable for visible-light absorption. Key findings include an internal electric field strength of 6.3 Debye, efficient charge transfer confirmed by Bader analysis, and strong optical absorption at 2.4 eV. Machine learning models, including DNN and LSTM, were employed to predict photon absorption rates, achieving mean squared errors as low as 0.0004. The synergistic effects of enhanced internal electric fields, optimized band structures, and strong photon absorption underscore the materials' potential for efficient hydrogen production. This study bridges advanced computational methods and machine learning, offering a framework for designing high-performance photocatalysts in renewable energy applications.