HyperAIHyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAIHyperAI

Formation Energy On Materials Project

Metrics

MAE

Results

Performance results of various models on this benchmark

Model Name
MAE
Paper TitleRepository
Matformer21.2Periodic Graph Transformers for Crystal Material Property Prediction-
SchNet35SchNet - a deep learning architecture for molecules and materials-
PotNet18.8Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction-
MEGNet28Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals-
SchNet31.8Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials-
CGCNN39Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties-
CartNet17.47A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation-
MT-CGCNN41MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction-
SchNet-edge-update22.7Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials-
0 of 9 row(s) selected.
Formation Energy On Materials Project | SOTA | HyperAI