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Formation Energy On Materials Project

Métriques

MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MAE	Paper Title	Repository
Matformer	21.2	Periodic Graph Transformers for Crystal Material Property Prediction
SchNet	35	SchNet - a deep learning architecture for molecules and materials
PotNet	18.8	Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
MEGNet	28	Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
SchNet	31.8	Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
CGCNN	39	Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
CartNet	17.47	A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
MT-CGCNN	41	MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
SchNet-edge-update	22.7	Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

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