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Computational chemistry

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코드

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

https://github.com/ACEsuit/mace

공식 코드

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

https://github.com/ASK-Berkeley/MLFF-distill

공식 코드

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

https://github.com/Data-reindeer/MOCO

공식 코드

Improving Molecular Pretraining with Complementary Featurizations

https://github.com/DeepGraphLearning/ConfGF

공식 코드

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/Navantock/SE3Set

공식 코드

SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning

https://github.com/Open-Catalyst-Project/ocp

공식 코드

Spherical Channels for Modeling Atomic Interactions

https://github.com/PaddlePaddle/PaddleHelix

공식 코드

GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions

https://github.com/adrianasupady/fafoom

공식 코드

First-principles molecular structure search with a genetic algorithm

https://github.com/ajfisch/conformal-cascades

공식 코드

Efficient Conformal Prediction via Cascaded Inference with Expanded Admission

https://github.com/ajfisch/conformal-fp

공식 코드

Conformal Prediction Sets with Limited False Positives

https://github.com/ajfisch/few-shot-cp

공식 코드

Few-shot Conformal Prediction with Auxiliary Tasks

https://github.com/chao1224/geom3d

공식 코드

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

https://github.com/choderalab/espaloma

공식 코드

End-to-End Differentiable Molecular Mechanics Force Field Construction

https://github.com/cohortshapley/cohortintgrad

공식 코드

Model free variable importance for high dimensional data

https://github.com/dralgroup/mlatom

공식 코드

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

https://github.com/eepperly/randomly-pivoted-cholesky

공식 코드

Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky

https://github.com/gcorso/diffdock

공식 코드

Modeling Molecular Structures with Intrinsic Diffusion Models

https://github.com/gcorso/torsional-diffusion

공식 코드

Torsional Diffusion for Molecular Conformer Generation

https://github.com/jia-975/geodiff-ip

공식 코드

Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations

https://github.com/jia-975/torsionaldiff-ip

공식 코드

Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations

https://github.com/khaledkah/morered

공식 코드

Molecular relaxation by reverse diffusion with time step prediction

https://github.com/lauyikfung/t-rex

공식 코드

T-Rex: Text-assisted Retrosynthesis Prediction

https://github.com/leichen2018/GNN-Substructure-Counting

공식 코드

Can Graph Neural Networks Count Substructures?

https://github.com/luispintoc/unlocking-chemical-insights

공식 코드

Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers

https://github.com/mdsunivie/deeperwin

공식 코드

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

https://github.com/microsoft/sphnet

공식 코드

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity

https://github.com/n-gao/globe

공식 코드

Generalizing Neural Wave Functions

https://github.com/nec-research/picps-ml4sci

공식 코드

Physics-Informed Weakly Supervised Learning for Interatomic Potentials

https://github.com/openmm/openmmforcefields

공식 코드

End-to-End Differentiable Molecular Mechanics Force Field Construction

https://github.com/pnnl/chemreasoner

공식 코드

Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design

https://github.com/ran1812/utgdiff

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Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model

https://github.com/tarungog/torsionnet_paper_version

공식 코드

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

https://github.com/tsa87/semi-jtvae

공식 코드

Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

https://github.com/ur-whitelab/chemcrow-public

공식 코드

ChemCrow: Augmenting large-language models with chemistry tools

https://github.com/ur-whitelab/chemcrow-runs

공식 코드

ChemCrow: Augmenting large-language models with chemistry tools

https://github.com/wengong-jin/nips17-rexgen

공식 코드

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

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