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
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2. 27 eV and a permittivity of 20. 5, meeting all target metrics of our multi-objective search.
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions
vertaix/llm-prop
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The prediction of crystal properties plays a crucial role in the crystal design process.
Band-gap regression with architecture-optimized message-passing neural networks
The domain of applicability of the ensemble model is analyzed with respect to the crystal systems, the inclusion of a Hubbard parameter in the density functional calculations, and the atomic species building up the materials.
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
divelab/AIRS
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This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors.
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models.
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science
Tony-Y/cgnn
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Technical report, RIMCS LLC 2022
We introduce a large-scale dataset of quantum-mechanically calculated properties of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK).
Periodic Graph Transformers for Crystal Material Property Prediction
divelab/AIRS
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Our Matformer is designed to be invariant to periodicity and can capture repeating patterns explicitly.
Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties
We also find that the radial basis function improves the linear separability of chemical datasets in all 10 datasets tested and provide a framework for the application of this function in the LOCO-CV process to improve the outcome of LOCO-CV measurements regardless of machine learning algorithm, choice of metric, and choice of compound representation.
Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
Before specific differences emerge according to the precise ratios of elements in a given crystal structure, a material can be represented by the set of its constituent chemical elements.
