The MatErials Graph Network (MEGNet) is an implementation of DeepMind's graph networks [1] for universal machine learning in materials science.

In this work, we introduce MEGNet, aiming to enhance the single-trial decoding framework of a compact deep neural network inspired by EEGNet, a model widely utilized in …

Dec 12, 2018 · Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet …

Oct 17, 2025 · MEGNet is an automated ICA-based artifact removal system for MEG using spatiotemporal convolutional neural networks. MEGNEt classifies ICs in the following categories:

There are two models available - MEGNet and M3GNet. We create the structure first. This is based on the relaxed structure obtained from the Materials Project. Alternatively, one can use …

Decoding complex patterns associated with task-specific activities embedded within magnetoencephalography (MEG) signals is pivotal for understanding brain functions and …

megnet.activations megnet.callbacks megnet.cli.meg megnet.config megnet.data.crystal megnet.data.graph megnet.data.local_env megnet.data.molecule megnet.data.qm9 …

Nov 16, 2022 · The MatErials Graph Network (MEGNet) is an implementation of DeepMind's graph networks [1] for universal machine learning in materials science.

Indices are always validated on CPU and never validated on GPU. To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

Algorithm description: Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Adapted implementation of kgcnn. Original code from …