Papers with Code 아카이브

Formation Energy

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 14
보존된 평가 기록: 77

On the QM9 dataset the numbers reported in the table are the mean absolute error in eV on the target variable U0 divided by U0's chemical accuracy, which is equal to 0.043.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AIRI-Institute/conditional-crystal-generation

공식 코드

Unleashing the power of novel conditional generative approaches for new materials discovery

https://github.com/AbhiroopBhattacharya/MatMMFuse

공식 코드

MatMMFuse: Multi-Modal Fusion model for Material Property Prediction

https://github.com/MilesZhao/PGCGM

공식 코드

Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints

https://github.com/SINGROUP/dscribe

공식 코드

DScribe: Library of Descriptors for Machine Learning in Materials Science

https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN

공식 코드

A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems

https://github.com/YKQ98/Matformer

공식 코드

Periodic Graph Transformers for Crystal Material Property Prediction

https://github.com/alexhernandezgarcia/gflownet

공식 코드

Crystal-GFN: sampling crystals with desirable properties and constraints

https://github.com/atomistic-machine-learning/SchNet

공식 코드

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

https://github.com/atomistic-machine-learning/schnetpack

공식 코드

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

https://github.com/brain-research/mpnn

공식 코드

Neural Message Passing for Quantum Chemistry

https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet

공식 코드

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

https://github.com/imatge-upc/CartNet

공식 코드

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation

https://github.com/janosh/dielectrics

공식 코드

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials

https://github.com/janosh/matbench-discovery

공식 코드

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

https://github.com/janosh/pymatviz

공식 코드

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

https://github.com/jlparkI/benchmarking_xGPR

공식 코드

Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability

https://github.com/jlparki/xgpr

공식 코드

Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability

https://github.com/kdmsit/crysgnn

공식 코드

CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials

https://github.com/klicperajo/dimenet

공식 코드

Directional Message Passing for Molecular Graphs

https://github.com/lantunes/skipatom

공식 코드

Distributed Representations of Atoms and Materials for Machine Learning

https://github.com/lrcfmd/Site-Net-DeepInfoMax

공식 코드

Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics

https://github.com/materialsvirtuallab/maml

공식 코드

Discovery of 2D materials using Transformer Network based Generative Design

https://github.com/onurboyar/Crystal-LSBO

공식 코드

Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization

https://github.com/peterbjorgensen/msgnet

공식 코드

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

https://github.com/peterbjorgensen/vorosym

공식 코드

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

https://github.com/shuix007/HMGNN

공식 코드

Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties

https://github.com/soumyasanyal/mt-cgcnn

공식 코드

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

https://github.com/torchmd/torchmd-net

공식 코드

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials

https://github.com/usccolumbia/materialsuq

공식 코드

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/usccolumbia/tsdnn

공식 코드

Semi-supervised teacher-student deep neural network for materials discovery

https://github.com/xqh19970407/invdesflow-al

공식 코드

InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials

https://github.com/yyt-2378/automat

공식 코드

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

https://github.com/zetayue/MXMNet

공식 코드

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

https://github.com/ACEsuit/mace

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

https://github.com/AJS1NGH/Predicting-Molecular-Properties-Challenge

Neural Message Passing for Quantum Chemistry

https://github.com/CoderPat/OpenGNN

Neural Message Passing for Quantum Chemistry

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.