Band Gap
18 papers with code • 4 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Band Gap
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Materials Project
OQMD v1.2
OQM9HK
Most implemented papers
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
txie-93/cgcnn
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Phys. Rev. Lett. 2017
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
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.
Periodic Graph Transformers for Crystal Material Property Prediction
YKQ98/Matformer
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Our Matformer is designed to be invariant to periodicity and can capture repeating patterns explicitly.
Band gap prediction for large organic crystal structures with machine learning
Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations.
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.
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.
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.
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.
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.
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.
