Papers Computational chemistry
“Computational chemistry” 태그가 달린 논문 122편 · 필터 해제
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
The ability to transfer knowledge from prior experiences to novel tasks stands as a pivotal capability of intelligent agents, including both humans and computational models. This principle forms the basis of transfer lea…
3D GenerationComputational chemistryMeta-LearningMolecular Property Prediction+4Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers
Pretrained molecular encoders have become indispensable in computational chemistry for tasks such as property prediction and molecular generation. However, the standard practice of relying solely on final-layer embedding…
Computational chemistryComputational EfficiencyProperty PredictionChemGraph: An Agentic Framework for Computational Chemistry Workflows
Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains chall…
Computational chemistryGraph Neural NetworkNatural Language UnderstandingTask PlanningAitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations
We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This intelligent assistant platform is equipped with chatbots and AI agents to help …
Computational chemistryRAGRetrieval-augmented GenerationEl Agente: An Autonomous Agent for Quantum Chemistry
Computational chemistry tools are widely used to study the behaviour of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work,…
Computational chemistryAdjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the first on-policy approach that allows signi…
Computational chemistryMachine Learned Force Fields: Fundamentals, its reach, and challenges
Highly accurate force fields are a mandatory requirement to generate predictive simulations. In this regard, Machine Learning Force Fields (MLFFs) have emerged as a revolutionary approach in computational chemistry and m…
Computational chemistryComputational EfficiencyA Transformer Model for Predicting Chemical Reaction Products from Generic Templates
The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which pres…
Computational chemistryEfficient ExplorationvalidIntegrating convolutional layers and biformer network with forward-forward and backpropagation training
Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compounds and accelerating therapeutic development. Traditional machine learn…
Computational chemistryDrug DiscoveryFeature EngineeringMolecular Property Prediction+2Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction
Machine learning (ML) has been playing important roles in drug discovery in the past years by providing (pre-)screening tools for prioritising chemical compounds to pass through wet lab experiments. One of the main ML ta…
AutoMLComputational chemistryDrug DiscoveryProperty PredictionEfficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable …
Computational chemistryPredictionTowards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
The foundation model (FM) paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations and scalable training to perform a variety of computational chemistry tasks. Although M…
Computational chemistryKnowledge DistillationEfficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
Transition states are a critical bottleneck in chemical transformations. Significant efforts have been made to develop algorithms that efficiently locate transition states on potential energy surfaces. However, the compu…
Computational chemistryGraph Neural NetworkThe dark side of the forces: assessing non-conservative force models for atomistic machine learning
The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational chemistry and materials discovery. In t…
Computational chemistryComputational EfficiencyEuclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for ac…
Computational chemistryA large language model-type architecture for high-dimensional molecular potential energy surfaces
Computing high dimensional potential surfaces for molecular and materials systems is considered to be a great challenge in computational chemistry with potential impact in a range of areas including fundamental predictio…
Computational chemistryLanguage ModelingLanguage ModellingLarge Language ModelRiemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy
This study introduces a modified score matching method aimed at generating molecular structures with high energy accuracy. The denoising process of score matching or diffusion models mirrors molecular structure optimizat…
Computational chemistryDenoisingEnergy-GNoME: A Living Database of Selected Materials for Energy Applications
Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystal…
Band GapComputational chemistryPre-trained Molecular Language Models with Random Functional Group Masking
Recent advancements in computational chemistry have leveraged the power of trans-former-based language models, such as MoLFormer, pre-trained using a vast amount of simplified molecular-input line-entry system (SMILES) s…
Computational chemistryDrug DiscoveryGraph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over extended simulations. We hypothesize that …
Computational chemistryDrug Discovery