Representation of compounds for machine-learning prediction of physical properties-Reference-Cited by-同舟云学术

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Representation of compounds for machine-learning prediction of physical properties

Published:2017-04-19 Issue:14 Volume:95 Page:
ISSN:2469-9950
Container-title:Physical Review B
language:en
Short-container-title:Phys. Rev. B

Author:

Seko Atsuto,Hayashi Hiroyuki,Nakayama Keita,Takahashi Akira,Tanaka Isao

Funder

Japan Science and Technology Agency

Japan Society for the Promotion of Science

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevB.95.144110/fulltext

Reference62 articles.

1. Predicting Crystal Structures with Data Mining of Quantum Calculations

2. Predicting crystal structure by merging data mining with quantum mechanics

3. Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

4. Data Mined Ionic Substitutions for the Discovery of New Compounds

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

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2. Predicting Model for Device Density of States of Quantum-Confined SiC Nanotube with Magnetic Dopant: An Integrated Approach Utilizing Machine Learning and Density Functional Theory;Silicon;2024-09-10

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