Papers with Code 아카이브

Atomistic Description

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코드

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

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/jfpuget/nvidia-pcqm4mv2

공식 코드

Heterogenous Ensemble of Models for Molecular Property Prediction

https://github.com/jinhojsk515/SPMM

공식 코드

Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model

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/johnathan-xie/sma

공식 코드

Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning

https://github.com/jpfolch/catechol_solvent_selection

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The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning

https://github.com/jw9730/tokengt

공식 코드

Pure Transformers are Powerful Graph Learners

https://github.com/karolismart/dropgnn

공식 코드

DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks

https://github.com/kdmsit/crysgnn

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

https://github.com/keiradams/chiro

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Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

https://github.com/kimsu55/toxexpert

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Learning Topology-Specific Experts for Molecular Property Prediction

https://github.com/klicperajo/dimenet

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Directional Message Passing for Molecular Graphs

https://github.com/lantunes/skipatom

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Distributed Representations of Atoms and Materials for Machine Learning

https://github.com/lars-research/3d-pgt

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Automated 3D Pre-Training for Molecular Property Prediction

https://github.com/lars-research/pacia

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PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

https://github.com/larsbratholm/champs_kaggle

공식 코드

A community-powered search of machine learning strategy space to find NMR property prediction models

https://github.com/learningmatter-mit/NeuralForceField

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Molecular machine learning with conformer ensembles

https://github.com/lenscloth/grpe

공식 코드

GRPE: Relative Positional Encoding for Graph Transformer

https://github.com/lhirschfeld/ChempropUncertaintyQuantification

공식 코드

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

https://github.com/lihan97/kpgt

공식 코드

KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction

https://github.com/linusbao/MoSE

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Homomorphism Counts as Structural Encodings for Graph Learning

https://github.com/liugangcode/InfoAlign

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Learning Molecular Representation in a Cell

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/lukecavabarrett/pna

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Principal Neighbourhood Aggregation for Graph Nets

https://github.com/luoyk1999/molecular-homology

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Improving Self-supervised Molecular Representation Learning using Persistent Homology

https://github.com/materialsvirtuallab/maml

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Discovery of 2D materials using Transformer Network based Generative Design

https://github.com/max-veit/velociraptor

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Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

https://github.com/maxischuh/twinbooster

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TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction

https://github.com/maxxxzdn/implicit-steerable-kernels

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Implicit Convolutional Kernels for Steerable CNNs

https://github.com/mehmetfdemirel/aware

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Attentive Walk-Aggregating Graph Neural Networks

https://github.com/meliao/rotation-invariant-random-features

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Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds

https://github.com/microsoft/DVMP

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Dual-view Molecule Pre-training

https://github.com/mims-harvard/TDC

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Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

https://github.com/mir-group/allegro

공식 코드

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics

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