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Formation Energy
Formation Energy On Materials Project
Formation Energy On Materials Project
Metrics
MAE
Results
Performance results of various models on this benchmark
Columns
Model Name
MAE
Paper Title
MT-CGCNN
41
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
CGCNN
39
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
SchNet
35
SchNet - a deep learning architecture for molecules and materials
SchNet
31.8
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
MEGNet
28
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
SchNet-edge-update
22.7
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Matformer
21.2
Periodic Graph Transformers for Crystal Material Property Prediction
PotNet
18.8
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
CartNet
17.47
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
0 of 9 row(s) selected.
Previous
Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Formation Energy
Formation Energy On Materials Project
Formation Energy On Materials Project
Metrics
MAE
Results
Performance results of various models on this benchmark
Columns
Model Name
MAE
Paper Title
MT-CGCNN
41
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
CGCNN
39
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
SchNet
35
SchNet - a deep learning architecture for molecules and materials
SchNet
31.8
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
MEGNet
28
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
SchNet-edge-update
22.7
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Matformer
21.2
Periodic Graph Transformers for Crystal Material Property Prediction
PotNet
18.8
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
CartNet
17.47
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
0 of 9 row(s) selected.
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Next