Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.
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Summary
A crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials.
- Type
- article
- Published
- 2017-10-27
- Cited by
- 2,192
- References
- 45
- Access
- Open access
- OpenAlex
- https://openalex.org/W2766856748
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13838309
Keywords
Computer science, Convolutional neural network, Crystal (programming language), Representation (politics), Graph
References
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- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
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- Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies.
- The Inorganic Crystal Structure Database (ICSD)—Present and Future
- An Interpretation of Bond Lengths and a Classification of Bonds.
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- Quantum Monte Carlo simulations of solids
- Voronoi–dirichlet polyhedra in crystal chemistry: theory and applications
- Dropout: a simple way to prevent neural networks from overfitting
- Covalent radii revisited.
- Crystal structure representations for machine learning models of formation energies
- A high-throughput infrastructure for density functional theory calculations
- Big data of materials science: critical role of the descriptor.
- Phase Transitions in Solid Solutions of PbZrO 3 and PbTiO 3 (II) X-ray Study
- Improved Photocatalytic Performance under Solar Light Irradiation by Integrating Wide-band-gap Semiconductors, SnO2, SnTaO3 and Sn2Ta2O7
- The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
- An Explanation of Chemical Variations within Periodic Major Groups
Cited by
- AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
- New tolerance factor to predict the stability of perovskite oxides and halides
- Autonomous data-driven design of inorganic materials with AFLOW.
- Quantum Machine Learning in Chemical Compound Space.
- Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
- Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior
- Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
- Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
- Quantum-chemical insights from interpretable atomistic neural networks
- Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
- Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
- Harnessing the Materials Project for machine-learning and accelerated discovery
- Machine learning material properties from the periodic table using convolutional neural networks
- Atomic-position independent descriptor for machine learning of material properties
- Machine-learning the configurational energy of multicomponent crystalline solids
- Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
- Graph Convolutional Neural Networks for Polymers Property Prediction
- MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
- Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials
- NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing
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