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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Formation Energy On Materials Project

평가 지표

MAE

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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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Formation Energy On Materials Project | SOTA | HyperAI초신경