Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
出版年份 2018 全文链接
标题
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
作者
关键词
-
出版物
PHYSICAL REVIEW LETTERS
Volume 120, Issue 14, Pages -
出版商
American Physical Society (APS)
发表日期
2018-04-06
DOI
10.1103/physrevlett.120.145301
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Universal fragment descriptors for predicting properties of inorganic crystals
- (2017) Olexandr Isayev et al. Nature Communications
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- Accelerated search for materials with targeted properties by adaptive design
- (2016) Dezhen Xue et al. Nature Communications
- The thermodynamic scale of inorganic crystalline metastability
- (2016) W. Sun et al. Science Advances
- A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
- (2016) Maarten de Jong et al. Scientific Reports
- Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
- (2015) Olexandr Isayev et al. CHEMISTRY OF MATERIALS
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
- (2015) Atsuto Seko et al. PHYSICAL REVIEW LETTERS
- Big Data of Materials Science: Critical Role of the Descriptor
- (2015) Luca M. Ghiringhelli et al. PHYSICAL REVIEW LETTERS
- Charting the complete elastic properties of inorganic crystalline compounds
- (2015) Maarten de Jong et al. Scientific Data
- The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
- (2015) Scott Kirklin et al. npj Computational Materials
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- A high-throughput infrastructure for density functional theory calculations
- (2011) Anubhav Jain et al. COMPUTATIONAL MATERIALS SCIENCE
- Computational screening of perovskite metal oxides for optimal solar light capture
- (2011) Ivano E. Castelli et al. Energy & Environmental Science
- Covalent radii revisited
- (2008) Beatriz Cordero et al. DALTON TRANSACTIONS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More