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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials
MatMMFuse: Multi-Modal Fusion model for Material Property Prediction
The Vendiscope: An Algorithmic Microscope For Data Collections
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
Unleashing the power of novel conditional generative approaches for new materials discovery
Generative Hierarchical Materials Search
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
Enhancing material property prediction with ensemble deep graph convolutional networks
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
Scalable Diffusion for Materials Generation
Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction
Crystal-GFN: sampling crystals with desirable properties and constraints
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Generative Design of inorganic compounds using deep diffusion language models
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
Wigner kernels: body-ordered equivariant machine learning without a basis
Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
Discovery of 2D materials using Transformer Network based Generative Design
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
Deep Reinforcement Learning for Inverse Inorganic Materials Design
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science
Periodic Graph Transformers for Crystal Material Property Prediction
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Edge-based Tensor prediction via graph neural networks
Semi-supervised teacher-student deep neural network for materials discovery
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