Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties-Reference-Cited by-同舟云学术

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Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Published:2018-04-06 Issue:14 Volume:120 Page:
ISSN:0031-9007
Container-title:Physical Review Letters
language:en
Short-container-title:Phys. Rev. Lett.

Author:

Xie Tian,Grossman Jeffrey C.

Funder

Toyota Research Institute

National Energy Research Scientific Computing Center

Extreme Science and Engineering Discovery Environment

Publisher

American Physical Society (APS)

Subject

General Physics and Astronomy

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevLett.120.145301/fulltext

Reference35 articles.

1. Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization

2. Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

3. Accelerated search for materials with targeted properties by adaptive design

4. Universal fragment descriptors for predicting properties of inorganic crystals

5. Big Data of Materials Science: Critical Role of the Descriptor

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