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
Most implemented papers
Inverse design of two-dimensional materials with invertible neural networks
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.
Distributed Representations of Atoms and Materials for Machine Learning
To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required.
Scalable deeper graph neural networks for high-performance materials property prediction
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning
zhichen96/interpretable_ml_metamaterials
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Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems.
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.
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.
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).
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.
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.
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.
