Papers with Code 아카이브

Computational chemistry

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/whitead/synspace

공식 코드

ChemCrow: Augmenting large-language models with chemistry tools

https://github.com/yeskendirk/conditioning-gnns

공식 코드

An Exploration of Conditioning Methods in Graph Neural Networks

https://github.com/yingtaoluo/Nanofiltration-Membrane-Deep-Learning

공식 코드

Deep Spatial Learning with Molecular Vibration

https://github.com/yuyangw/iMolCLR

공식 코드

Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

https://github.com/zpengmei/xxmd

공식 코드

Beyond MD17: the reactive xxMD dataset

https://gitlab.com/zaverkin_v/gmnn

공식 코드

Transfer learning for chemically accurate interatomic neural network potentials

https://github.com/Abdulk084/Chemception

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

https://github.com/Bunseki2/DeepL

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

https://github.com/DeepGraphLearning/CGCF-ConfGen

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/MindCode-4/code-11/tree/main/An-Exploration-of-Conditioning-Methods

An Exploration of Conditioning Methods in Graph Neural Networks

https://github.com/MindCode-4/code-6/tree/main/An-Exploration-of-Conditioning-Methods

An Exploration of Conditioning Methods in Graph Neural Networks

https://github.com/MinkaiXu/CGCF-ConfGen

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/MinkaiXu/ConfVAE-ICML21

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/atomicarchitects/phonax

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

https://github.com/deepqmc/deepqmc

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?

https://github.com/fromjade/dlmpnn

Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

https://github.com/galkampel/HyperNetworks

Molecule Property Prediction and Classification with Graph Hypernetworks

https://github.com/instadeepai/mlip

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

https://github.com/kntkb/openmmforcefields

End-to-End Differentiable Molecular Mechanics Force Field Construction

https://github.com/lab-cosmo/pet-mad

The dark side of the forces: assessing non-conservative force models for atomistic machine learning

https://github.com/lucidrains/equiformer-pytorch

Spherical Channels for Modeling Atomic Interactions

https://github.com/luost26/CGCF-ConfGen

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/mouthful/ClofNet

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/teddykoker/nequip-eqx

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

https://github.com/wollschl/uncertainty_for_molecules

Uncertainty Estimation for Molecules: Desiderata and Methods

https://github.com/yashpatel5400/clean_idp_rl

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

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