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A GPU-Accelerated Open-Source Python Package for Calculating Powder Diffraction, Small-Angle-, and Total Scattering with the Debye Scattering Equation
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A foundation model for atomistic materials chemistry.
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A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database
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Scaling deep learning for materials discovery
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MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
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IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
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JARVIS-Leaderboard: a large scale benchmark of materials design methods
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Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions
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CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
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High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials
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Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis
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DiGress: Discrete Denoising diffusion for graph generation
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Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity
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CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
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The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis
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MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
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Remember to Correct the Bias When Using Deep Learning for Regression!
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A database of low-energy atomically precise nanoclusters
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Crystal graph attention networks for the prediction of stable materials
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Crystal Diffusion Variational Autoencoder for Periodic Material Generation
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A geometric-information-enhanced crystal graph network for predicting properties of materials
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Global Self-Attention as a Replacement for Graph Convolution
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Atomistic Line Graph Neural Network for improved materials property predictions
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Validation of the Crystallography Open Database using the Crystallographic Information Framework
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GraphEBM: Molecular Graph Generation with Energy-Based Models
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Benchmarking graph neural networks for materials chemistry
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A Systematic Review of Metal Oxide Applications for Energy and Environmental Sustainability
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The Open Catalyst 2020 (OC20) Dataset and Community Challenges
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Graph Representation Learning
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Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models
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Denoising Diffusion Probabilistic Models
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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
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Graph convolutional neural networks with global attention for improved materials property prediction.
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Predicting materials properties without crystal structure: deep representation learning from stoichiometry
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Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features
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IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
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Fast Graph Representation Learning with PyTorch Geometric
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Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
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ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
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How Powerful are Graph Neural Networks?
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MolGAN: An implicit generative model for small molecular graphs
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Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database
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Dynamic Graph CNN for Learning on Point Clouds
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Graph Attention Networks
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Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.
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Inductive Representation Learning on Large Graphs
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MoleculeNet: a benchmark for molecular machine learning
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Exploring Structural Diversity and Fluxionality of Ptn (n = 10–13) Clusters from First-Principles
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Geometric Deep Learning: Going beyond Euclidean data
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Semi-Supervised Classification with Graph Convolutional Networks
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A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
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The Cambridge Structural Database
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COD::CIF::Parser: an error-correcting CIF parser for the Perl language
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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
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ZINC 15 – Ligand Discovery for Everyone
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
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Computing stoichiometric molecular composition from crystal structures
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Adam: A Method for Stochastic Optimization
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Quantum chemistry structures and properties of 134 kilo molecules
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Deep learning in neural networks: An overview
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
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Learning to Discover Social Circles in Ego Networks
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Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
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Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration
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Crystallography Open Database – an open-access collection of crystal structures
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VESTA: a three-dimensional visualization system for electronic and structural analysis
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The Problem with Determining Atomic Structure at the Nanoscale
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Automatic Generation of Complementary Descriptors with Molecular Graph Networks
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CIF (Crystallographic Information File): A Standard for Crystallographic Data Interchange
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Electronic spectroscopy and photophysics of Si nanocrystals. Relationship to bulk c-Si and porous Si
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Machine Intelligence Applied to Chemical Systems: A Graph Theoretical and Learning Machine Study of Second-Order Effects in Low Resolution Mass Spectra
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Atomic Radii in Crystals
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LVII. On the mathematical theory of isomers
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Molecular screening for solid–solid phase transitions by machine learning
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Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
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ICML 2018 workshop on Representation Learning on Graphs and Manifolds
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PyTorch:AnImperativeStyle,High-PerformanceDeepLearningLibrary
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Theatomicsimulationenvironment—aPythonlibraryforworkingwithatoms
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NeuralMessagePassingforQuantumChemistry
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Schnet:Acontinuous-filterconvolutionalneuralnetworkformodelingquantuminteractions
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Predictingmulticellularfunctionthrough multi-layertissuenetworks
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Solid State Chemistry and its Applications (2 ed.)
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Nanomaterials, Nanotechnologies and Design : An Introduction for Engineers and Architects
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The Graph Neural Network Model
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The American Mineralogist crystal structure database
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2023.DeepStruc:towardsstructuresolution frompairdistributionfunctiondatausingdeepgenerativemodels
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KDD ’24, August 25–29, 2024, Barcelona, Spain
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and xrd , nd , xPDF , and nPDF using the _get_all function with parameters shown in Table 7
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into supercells, ensuring a padding of at least 5 Ångströms (Å) beyond the largest nanoparticle diameter. Supercells were centered on the most central metal atom. Atom connectivity was determined
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Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
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[ A.4 Scattering data simulation Scattering data simulation (step 5 in Figure 1) was performed using DebyeCalculator [45]. saxs and sans were calculated using the iq
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2023. mendeleev - A Python package with properties of chemical elements, ions, isotopes and methods to manipulate and visualize periodic table
