A5038822652- Perovskite Materials and Applications
- Machine Learning in Materials Science
- Electron and X-Ray Spectroscopy Techniques
- Chalcogenide Semiconductor Thin Films
- Quantum Dots Synthesis And Properties
- Advancements in Semiconductor Devices and Circuit Design
- Molten salt chemistry and electrochemical processes
- Semiconductor materials and interfaces
- Corrosion Behavior and Inhibition
- Advanced machining processes and optimization
- Manufacturing Process and Optimization
- Injection Molding Process and Properties
- CO2 Reduction Techniques and Catalysts
Purdue University West Lafayette
2022-2024
Idaho National Laboratory
2021
First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by expense using large supercells and advanced levels theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate predictions. Here, we develop a framework for prediction screening native functional impurities chemical space group IV, III–V, II–VI zinc blende semiconductors,...
Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset perovskite alloys can be used train accurate surrogate models for property prediction and subsequently perform inverse design using genetic algorithm (GA). Our consists decomposition energies, bandgaps,...
A high-throughput computational dataset of halide perovskite alloys is generated from 494 unique compositions, using multiple DFT functionals. The further applied to screen promising perovskites with high stability, suitable band gap and excellent PV efficiency.
Here, we develop a framework for the prediction and screening of native defects functional impurities in chemical space Group IV, III-V, II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density theory (DFT) data. Using an innovative approach sampling partially optimized defect configurations from DFT calculations, generate one largest computational datasets to date, containing many types vacancies, self-interstitials,...
Novel halide perovskites with improved stability and optoelectronic properties can be designed via composition engineering at cation and/or anion sites. Data-driven methods, especially high-throughput first principles computations subsequent analysis based on unique materials descriptors, are key to achieving this goal. In work, we report a density functional theory (DFT) dataset of 495 $ABX_3$ perovskite compounds, various atomic molecular species considered A, B X sites, different amounts...
Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset perovskite alloys can be used train accurate surrogate models for property prediction and subsequently perform inverse design using genetic algorithm (GA). Our consists decomposition energies, band gaps,...