Band Gap

3 papers with code · Graphs

Leaderboards

Greatest papers with code

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Phys. Rev. Lett. 2017 txie-93/cgcnn

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.

BAND GAP FORMATION ENERGY

Crystal Graph Neural Networks for Data Mining in Materials Science

Technical report, RIMCS LLC 2019 Tony-Y/cgnn

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.

BAND GAP FORMATION ENERGY MATERIALS SCREENING TOTAL MAGNETIZATION

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

14 Nov 2018soumyasanyal/mt-cgcnn

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.

BAND GAP FORMATION ENERGY MULTI-TASK LEARNING