13 papers with code • 3 benchmarks • 3 datasets
LibrariesUse these libraries to find Band Gap models and implementations
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
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.
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.
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.
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.
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.
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.
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.
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.