QM9
Canonical113papers using it
2022first seen
134k small organic molecules with computed quantum-chemical properties, for molecular-property prediction.
Papers using QM9 (113)
- DenseGNN: universal and scalable deeper graph neural networks for
high-performance property prediction in crystals and moleculesLearning-Order Autoregressive Models with Application to Molecular Graph GenerationRegression with Large Language Models for Materials and Molecular Property PredictionAll-atom Diffusion Transformers: Unified generative modelling of molecules and materialsQuantum mechanical dataset of 836k neutral closed shell molecules with upto 5 heavy atoms from CNOFSiPSClBrGuiding Diffusion Models with Reinforcement Learning for Stable Molecule GenerationA Reinforcement Learning-Driven Transformer GAN for Molecular GenerationMultimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES EmbeddingsClifford Group Equivariant Diffusion Models for 3D Molecular GenerationQUIVER: Quantum-Informed Views for Enhanced Representations in Large ML ModelsGenerative Pseudo-Force Fields for Molecular GenerationAIM: Adaptive Intervention for Deep Multi-task Learning of Molecular PropertiesActive Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural EmbeddingsTriplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph TransformersUncertainty Quantification in Graph Neural Networks with Shallow EnsemblesTransport-Coupled Bayesian Flows for Molecular Graph GenerationFrame-based Equivariant Diffusion Models for 3D Molecular GenerationGeometric Representation Condition Improves Equivariant Molecule GenerationTransferable Machine Learning of Electronic Hamiltonians with Superposition-of-Atomic-Potentials FeaturesWhen Molecular Similarity Works: Property Cliffs Reveal Hidden ErrorsLatent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum StatesAlign Your Structures: Generating Trajectories with Structure Pretraining for Molecular DynamicsSame Graph, Different Likelihoods: Calibration of Autoregressive Graph Generators via Permutation-Equivalent EncodingsMolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular GenerationSurrogate Functionals for Machine-Learned Orbital-Free Density Functional TheoryEnhancing Molecular Property Predictions by Learning from Bond Modelling and InteractionsBayesian Optimization in Chemical Compound Sub-Spaces using Low-Dimensional Molecular DescriptorsInformation Routing in Atomistic Foundation Models: How Task Alignment and Equivariance Shape Linear DisentanglementVecMol: Vector-Field Representations for 3D Molecule GenerationPermutation-Symmetrized Diffusion for Unconditional Molecular GenerationAutotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer LearningHierarchy-Guided Topology Latent Flow for Molecular Graph GenerationImpact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property PredictionEfficient, Equivariant Predictions of Distributed Charge ModelsMulti-objective optimization and quantum hybridization of equivariant deep learning interatomic potentialsQuantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property PredictionMolecular electrostatic potentials from machine learning models for dipole and quadrupole predictionsConstraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific DiscoveryPCEvo: Path-Consistent Molecular Representation via Virtual Evolutionaryqs$GW$ quasiparticle and $GW$-BSE excitation energies of 133,885 moleculesMolSculpt: Sculpting 3D Molecular Geometries from Chemical SyntaxMolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow MatchingMamba-driven multi-perspective structural understanding for molecular ground-state conformation predictionVEDA: 3D Molecular Generation via Variance-Exploding Diffusion with AnnealingPower law attention biases for molecular transformersFlexiFlow: decomposable flow matching for generation of flexible molecular ensembleBeyond Atoms: Evaluating Electron Density Representation for 3D Molecular LearningSpectral Analysis of Molecular Kernels: When Richer Features Do Not Guarantee Better GeneralizationLayer-to-Layer Knowledge Mixing in Graph Neural Network for Chemical Property PredictionInertialAR: Autoregressive 3D Molecule Generation with Inertial FramesQuantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph LearningFast and Interpretable Machine Learning Modelling of Atmospheric Molecular ClustersMolMark: Safeguarding Molecular Structures through Learnable Atom-Level WatermarkingRotational Sampling: A Plug-and-Play Encoder for Rotation-Invariant 3D Molecular GNNsGeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based PretrainingMuAPBEK: An Improved Analytical Kinetic Energy Density Functional for
Quantum ChemistryEquivariant Spherical Transformer for Efficient Molecular ModelingStable and Accurate Orbital-Free DFT Powered by Machine LearningTowards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion ModelingInductive Graph Representation Learning with Quantum Graph Neural NetworksRiemannian Denoising Model for Molecular Structure Optimization with Chemical AccuracyLearning Equivariant Non-Local Electron Density FunctionalsMGNN: Moment Graph Neural Network for Universal Molecular PotentialsHybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug DiscoveryHybrid quantum cycle generative adversarial network for small molecule
generationAccurate GW frontier orbital energies of 134 kilo moleculesFrom Molecules to Materials: Pre-training Large Generalizable Models for
Atomic Property PredictionKernel based quantum machine learning at record rate : Many-body
distribution functionals as compact representationsEquivariant Energy-Guided SDE for Inverse Molecular DesignMachine Learning Many-Body Green's Functions for Molecular Excitation
SpectraDiscovery of structure-property relations for molecules via
hypothesis-driven active learning over the chemical spaceApplication of quantum-inspired generative models to small molecular
datasetsGeometry-Complete Diffusion for 3D Molecule Generation and OptimizationFAENet: Frame Averaging Equivariant GNN for Materials ModelingNavigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule GenerationActive Causal Learning for Decoding Chemical Complexities with Targeted
InterventionsWigner kernels: body-ordered equivariant machine learning without a
basisLearning Joint 2D & 3D Diffusion Models for Complete Molecule GenerationEvolutionary Monte Carlo of QM properties in chemical space: Electrolyte
designMolecular Hessian matrices from a machine learning random forest
regression algorithmSymphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D
Molecule GenerationEquiFlow: Equivariant Conditional Flow Matching with Optimal Transport
for 3D Molecular Conformation PredictionViSNet: an equivariant geometry-enhanced graph neural network with
vector-scalar interactive message passing for moleculesMolecular Geometry-aware Transformer for accurate 3D Atomic System modelingQH9: A Quantum Hamiltonian Prediction Benchmark for QM9 MoleculesFractional Denoising for 3D Molecular Pre-trainingUnified Generative Modeling of 3D Molecules via Bayesian Flow NetworksEfficient molecular conformation generation with quantum-inspired
algorithmAdaptive hybrid density functionalsEnd-to-end AI framework for interpretable prediction of molecular and
crystal propertiest-SMILES: A Scalable Fragment-based Molecular Representation Framework
for De Novo Molecule GenerationRotation-Invariant Random Features Provide a Strong Baseline for Machine
Learning on 3D Point CloudsGaussian Plane-Wave Neural Operator for Electron Density EstimationModular Flows: Differential Molecular GenerationObtaining transferable chemical insight from solving machine-learning
classification problems: Thermodynamical properties prediction, atomic
composition as good as Coulomb matrixCHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular DesignQUBO-inspired Molecular Fingerprint for Chemical Property PredictionOn the Interplay of Subset Selection and Informed Graph Neural NetworksAutonomous data extraction from peer reviewed literature for training
machine learning models of oxidation potentialsUnderstanding the Structure of QM7b and QM9 Quantum Mechanical Datasets
Using Unsupervised LearningReflection-Equivariant Diffusion for 3D Structure Determination from
Isotopologue Rotational Spectra in Natural AbundanceClassifier-free graph diffusion for molecular property targetingAdaptive atomic basis setsData-Efficient Molecular Generation with Hierarchical Textual InversionSE3Set: Harnessing equivariant hypergraph neural networks for molecular
representation learningIn-Context Learning of Physical Properties: Few-Shot Adaptation to
Out-of-Distribution Molecular GraphsFreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine
Learning Force FieldsDistribution Learning for Molecular RegressionHessian QM9: A quantum chemistry database of molecular Hessians in implicit solventsXMOL: Explainable Multi-property Optimization of MoleculesEfficient Sampling for Machine Learning Electron Density and Its
Response in Real SpaceDeconstructing equivariant representations in molecular systemsGUISE: Graph GaUssIan Shading watErmark