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Band Gap

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TitleStatusHype
CrystalFormer-RL: Reinforcement Fine-Tuning for Materials DesignCode2
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred PotentialCode1
Inverse design of two-dimensional materials with invertible neural networksCode1
Learning Extremal Representations with Deep Archetypal AnalysisCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Distributed Representations of Atoms and Materials for Machine LearningCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Periodic Graph Transformers for Crystal Material Property PredictionCode1
Crystal Graph Neural Networks for Data Mining in Materials ScienceCode1
Establishing baselines for generative discovery of inorganic crystalsCode1
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materialsCode1
Scalable deeper graph neural networks for high-performance materials property predictionCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Using Scalable Computer Vision to Automate High-throughput Semiconductor CharacterizationCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials propertiesCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving LaboratoriesCode0
Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable TargetsCode0
Efficient Approximations of Complete Interatomic Potentials for Crystal Property PredictionCode0
Band gap prediction for large organic crystal structures with machine learningCode0
MatMMFuse: Multi-Modal Fusion model for Material Property PredictionCode0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution LayerCode0
Band-gap regression with architecture-optimized message-passing neural networksCode0
Element selection for functional materials discovery by integrated machine learning of elemental contributions to propertiesCode0
Quantum Dot Solar cells—0
Radar Cross Section Reduction of Microstrip Patch Antenna using Metamaterial Techniques—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
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
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