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Band Gap

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Showing 26–50 of 60 papers

TitleStatusHype
Conversion Efficiency of Strained Wurtzite ZnSnN2/InxGa1-xN Cylindrical Quantum Dot Solar Cell Under Influence of Built-in Electric Field—0
Curvature-informed multi-task learning for graph networks—0
Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors—0
Design Topological Materials by Reinforcement Fine-Tuned Generative Model—0
Edge-based Tensor prediction via graph neural networks—0
Energy-GNoME: A Living Database of Selected Materials for Energy Applications—0
Engineering Effective Hamiltonians for Magnetic Resonance—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning—0
Machine Learning guided high-throughput search of non-oxide garnets—0
Machine-learning techniques for the optimal design of acoustic metamaterials—0
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
Plasmonic excitations and coupling in atomic wires—0
Power Cycling Test Bench for Accelerated Life Testing for Reliability Assessment of SiC-MOSFET in Extreme Offshore Environment—0
Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics—0
Prediction of superconducting properties of materials based on machine learning models—0
Principal Component Analysis Applied to Gradient Fields in Band Gap Optimization Problems for Metamaterials—0
PyNanospacing: TEM image processing tool for strain analysis and visualization—0
Quantum Dot Solar cells—0
Radar Cross Section Reduction of Microstrip Patch Antenna using Metamaterial Techniques—0
SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving LaboratoriesCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
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