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Band Gap

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TitleStatusHype
CrystalFormer-RL: Reinforcement Fine-Tuning for Materials DesignCode2
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Crystal Graph Neural Networks for Data Mining in Materials ScienceCode1
Establishing baselines for generative discovery of inorganic crystalsCode1
Learning Extremal Representations with Deep Archetypal AnalysisCode1
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materialsCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Periodic Graph Transformers for Crystal Material Property PredictionCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Inverse design of two-dimensional materials with invertible neural networksCode1
Distributed Representations of Atoms and Materials for Machine LearningCode1
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred PotentialCode1
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach—0
AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation—0
Tunable electronic properties of germanene and two-dimensional group-III phosphides heterobilayers—0
Accurate predictive model of band gap with selected important features based on explainable machine learning—0
A Shared-Aperture Dual-Band sub-6 GHz and mmWave Reconfigurable Intelligent Surface With Independent Operation—0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials—0
Capturing long-range interaction with reciprocal space neural network—0
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction—0
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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