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Formation Energy

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Neural Message Passing for Quantum Chemistry

ICML 2017 Microsoft/gated-graph-neural-network-samples

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

DRUG DISCOVERY FORMATION ENERGY GRAPH REGRESSION NODE CLASSIFICATION

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

NeurIPS 2017 atomistic-machine-learning/schnetpack

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.

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

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

8 Jun 2018toshi-k/kaggle-champs-scalar-coupling

Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials.

FORMATION ENERGY

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

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

15 May 2019peterbjorgensen/msgnet

The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set.

FORMATION ENERGY MATERIALS SCREENING