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Computational chemistry
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers
ChemGraph: An Agentic Framework for Computational Chemistry Workflows
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations
El Agente: An Autonomous Agent for Quantum Chemistry
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Machine Learned Force Fields: Fundamentals, its reach, and challenges
A Transformer Model for Predicting Chemical Reaction Products from Generic Templates
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
A large language model-type architecture for high-dimensional molecular potential energy surfaces
Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy
Energy-GNoME: A Living Database of Selected Materials for Energy Applications
Pre-trained Molecular Language Models with Random Functional Group Masking
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky
How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations
AI and Machine Learning Approaches for Predicting Nanoparticles Toxicity The Critical Role of Physiochemical Properties
Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
Open-Source Molecular Processing Pipeline for Generating Molecules
StringNET: Neural Network based Variational Method for Transition Pathways
Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Diffusion Models in $\textit{De Novo}$ Drug Design
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
Molecular relaxation by reverse diffusion with time step prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.