Mirabbos Hojamberdiev

arXiv2026

Tuning Optoelectronic Properties and Photoelectrochemical Performance of \b{eta}-TaON via Vanadium Doping

The application of beta-TaON for solar-driven water splitting is hindered by limitations in phase purity, stoichiometry, crystallinity, visible-light absorption, carrier mobility, and high recombination rates. This study investigates the impact of vanadium doping (0-25 at.% V) on the structural, optoelectronic, and photoelectrochemical properties of beta-TaON using both experimental and density functional theory (DFT) approaches. Phase-pure beta-TaON is retained up to 10 at.% V, beyond which secondary phases (Ta2O5 and VN) form, indicating a threshold of ~10 at.% under the applied synthesis conditions. All samples exhibit a porous microstructure. Increasing vanadium content induces a redshift in the absorption edge, reducing the bandgap from 2.72 eV (undoped) to 2.38 eV at 25 at.% V for the main beta-TaON phase, in agreement with DFT results. X-ray photoelectron spectroscopy confirms substitutional incorporation of V5+ for Ta5+ in the beta-TaON lattice. DFT calculations reveal reduced electron effective mass, enhanced n-type conductivity, and favorable band edge shifts enabling spontaneous overall water splitting at <=10 at.% V. Photoelectrochemical measurements show improved photocurrent and more negative onset potentials for 5-10 at.% V, while higher V doping degrades performance due to phase segregation, which likely increases recombination and hinders interfacial charge transport. Vanadium doping (<=10 at.% V) is an effective strategy for tuning the electronic structure and enhancing the optical properties and photoelectrochemical performance of beta-TaON.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review

The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their role in predicting material properties, discovering novel compounds, and optimizing material structures. Key methodologies in this field include deep learning, graph neural networks, Bayesian optimization, and automated generative models (GANs, VAEs). These approaches enable the autonomous design of materials with tailored functionalities. By leveraging AutoML frameworks (AutoGluon, TPOT, and H2O.ai), researchers can automate the model selection, hyperparameter tuning, and feature engineering, significantly improving the efficiency of materials informatics. Furthermore, the integration of AI-driven robotic laboratories and high-throughput computing has established a fully automated pipeline for rapid synthesis and experimental validation, drastically reducing the time and cost of material discovery. This review highlights real-world applications of automated ML-driven approaches in predicting mechanical, thermal, electrical, and optical properties of materials, demonstrating successful cases in superconductors, catalysts, photovoltaics, and energy storage systems. We also address key challenges, such as data quality, interpretability, and the integration of AutoML with quantum computing, which are essential for future advancements. Ultimately, combining AI with automated experimentation and computational modeling is transforming the way materials are discovered and optimized. This synergy paves the way for new innovations in energy, electronics, and nanotechnology.

cond-mat.mtrl-sci↗