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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
Establishing baselines for generative discovery of inorganic crystalsCode1
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred PotentialCode1
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materialsCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
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
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Distributed Representations of Atoms and Materials for Machine LearningCode1
Inverse design of two-dimensional materials with invertible neural networksCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Learning Extremal Representations with Deep Archetypal AnalysisCode1
Crystal Graph Neural Networks for Data Mining in Materials ScienceCode1
MatMMFuse: Multi-Modal Fusion model for Material Property PredictionCode0
Design Topological Materials by Reinforcement Fine-Tuned Generative Model—0
Accurate predictive model of band gap with selected important features based on explainable machine learning—0
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction—0
Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors—0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics—0
AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation—0
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach—0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials—0
Energy-GNoME: A Living Database of Selected Materials for Energy Applications—0
Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable TargetsCode0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving LaboratoriesCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
A Shared-Aperture Dual-Band sub-6 GHz and mmWave Reconfigurable Intelligent Surface With Independent Operation—0
Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning—0
PyNanospacing: TEM image processing tool for strain analysis and visualization—0
Band-gap regression with architecture-optimized message-passing neural networksCode0
Radar Cross Section Reduction of Microstrip Patch Antenna using Metamaterial Techniques—0
Efficient Approximations of Complete Interatomic Potentials for Crystal Property PredictionCode0
Using Scalable Computer Vision to Automate High-throughput Semiconductor CharacterizationCode0
Capturing long-range interaction with reciprocal space neural network—0
Quantum Dot Solar cells—0
Prediction of superconducting properties of materials based on machine learning models—0
Machine Learning guided high-throughput search of non-oxide garnets—0
Curvature-informed multi-task learning for graph networks—0
Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials propertiesCode0
Element selection for functional materials discovery by integrated machine learning of elemental contributions to propertiesCode0
Edge-based Tensor prediction via graph neural networks—0
Tunable electronic properties of germanene and two-dimensional group-III phosphides heterobilayers—0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art—0
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