Materials Project
Canonical50papers using it
2022first seen
An open database of computed properties for hundreds of thousands of inorganic materials.
Papers using Materials Project (50)
- DenseGNN: universal and scalable deeper graph neural networks for
high-performance property prediction in crystals and moleculesArtificial Intelligence in Accelerating Materials Discovery: Opportunities and ChallengesA Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationGraphlet Histogram Representation Database of Inorganic CrystalsScreening of material defects using universal machine-learning
interatomic potentialsMultimodal Foundation Models for Material Property Prediction and
DiscoveryMassive Discovery of Low-Dimensional Materials from Universal Computational StrategyThe Northeast Materials Database for Magnetic MaterialsAt-Scale Data-Driven Exploration of High-Voltage Cathode-Active Materials for Sodium BatteriesMachine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networksReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property PredictionPredicting ionic conductivity in solids from the machine-learned potential energy landscapeDiscovery of Correlated Electron Molecular Orbital Materials using Graph RepresentationsAdaptive Slater Koster Parameters: Crossing Oxidation States with Density Functional Tight BindingAI-Driven Discovery of New Materials: Breaking Data Bottlenecks and Transforming Research ParadigmsAQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State BatteriesGenerative Inverse Design of Cold Metals for Low-Power ElectronicsActive Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material SystemsPFT: Phonon Fine-tuning for Machine Learned Interatomic PotentialsConstraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific DiscoveryRepresentation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and DatasetsLearning Magnetic Order Classification from Large-Scale Materials DatabasesCrystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion BatteryBeyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray DiffractionLeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic ModelingAdvancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment predictionMatMMFuse: Multi-Modal Fusion model for Material Property PredictionQuotient Complex Transformer (QCformer) for Perovskite Data AnalysisIntegrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery MaterialsThe Vendiscope: An Algorithmic Microscope For Data CollectionsDeep learning generative model for crystal structure predictionUniversal Machine Learning Kohn-Sham Hamiltonian for MaterialsLLaMP: Large Language Model Made Powerful for High-fidelity Materials
Knowledge Retrieval and DistillationTransferable and Robust Machine Learning Model for Predicting Stability
of Si Anodes for Multivalent Cation BatteriesEfficient Approximations of Complete Interatomic Potentials for Crystal
Property PredictionAccelerating Material Property Prediction using Generically Complete
Isometry InvariantsMatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials
ModelingCrystalformer: Infinitely Connected Attention for Periodic Structure
EncodingDielectric Tensor Prediction for Inorganic Materials Using Latent
Information from Preferred PotentialDeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for
Predicting Properties of Crystalline MaterialsHigh-throughput discovery of metal oxides with high thermoelectric
performance via interpretable feature engineering on small dataMaterial Property Prediction using Graphs based on Generically Complete
Isometry InvariantsVirtual Node Graph Neural Network for Full Phonon PredictionPredicting Miscibility in Binary Compounds: A Machine Learning and
Genetic Algorithm StudyMaterial Property Prediction with Element Attribute Knowledge Graphs and
Multimodal Representation LearningLeveraging Orbital Information and Atomic Feature in Deep Learning ModelS2SNet: A Pretrained Neural Network for Superconductivity DiscoveryAddressing the Accuracy-Cost Tradeoff in Material Property Prediction: A
Teacher-Student StrategyAdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse
Catalysts DesignHigher-Order Equivariant Neural Networks for Charge Density Prediction
in Materials