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
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.
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.
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.
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.
Atomistic Line Graph Neural Network for Improved Materials Property Predictions
usnistgov/alignn
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Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.
Active learning based generative design for the discovery of wide bandgap materials
CompRhys/roost
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Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model.
Learning Extremal Representations with Deep Archetypal Analysis
bmda-unibas/DeepArchetypeAnalysis
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The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information.
Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer
Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by demonstrating band gap estimations.
Crystal Graph Neural Networks for Data Mining in Materials Science
Tony-Y/cgnn
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Technical report, RIMCS LLC 2019
This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.
