Band Gap
22 papers with code • 4 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Band Gap
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Materials Project
OQMD v1.2
OQM9HK
WBM
Latest papers with no code
Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning
Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties.
PyNanospacing: TEM image processing tool for strain analysis and visualization
This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice.
Radar Cross Section Reduction of Microstrip Patch Antenna using Metamaterial Techniques
The L-structured metamaterial antenna implemented has a 29. 37% larger bandwidth than the reference patch antenna with a gain of 2. 94dB with a return loss of -28. 28dB.
Capturing long-range interaction with reciprocal space neural network
The structure information in real space is firstly transformed into reciprocal space and then encoded into a reciprocal space potential or a global descriptor with full atomic interactions.
Quantum Dot Solar cells
There remains wide interest in solar cells being made using inexpensive materials and simple device manufacturing techniques to harvest ever-increasing amounts of energy.
Prediction of superconducting properties of materials based on machine learning models
Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials.
Machine Learning guided high-throughput search of non-oxide garnets
Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc.
Curvature-informed multi-task learning for graph networks
Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics.
Edge-based Tensor prediction via graph neural networks
Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT).
Tunable electronic properties of germanene and two-dimensional group-III phosphides heterobilayers
Although their normal bandgap, which significantly changes with SOC, is an indirect one, whilst tunning the interlayer distance band gap jumps from unsymmetrical point to symmetrical Dirac cones and becomes direct on K points.
