Critical assessment of regression-based machine learning methods for polymer dielectrics-Reference-Cited by-同舟云学术

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Critical assessment of regression-based machine learning methods for polymer dielectrics

Published:2016-12 Issue: Volume:125 Page:123-135
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Mannodi-Kanakkithodi Arun,Pilania Ghanshyam,Ramprasad Rampi

Funder

Multidisciplinary University Research Initiative

Office of Naval Research

Extreme Science and Engineering Discovery Environment

National Energy Research Scientific Computing Center

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference21 articles.

1. A. Mannodi-Kanakkithodi, G. Pilania, T.D. Huan, T. Lookman, R. Ramprasad, Machine learning strategy for accelerated design of polymer dielectrics, Sci. Rep. 6 (20952). http://dx.doi.org/10.1038/srep20952.

2. G. Pilania, C.C. Wang, X. Jiang, S. Rajasekaran, R. Ramprasad, Accelerating materials property predictions using machine learning, Sci. Rep. 3 (2810). http://dx.doi.org/10.1038/srep02810.

3. G. Pilania, A. Mannodi-Kanakkithodi, B.P. Uberuaga, R. Ramprasad, J.E. Gubernatis, T. Lookman, Machine learning bandgaps of double perovskites, Sci. Rep. 6 (19375). http://dx.doi.org/10.1038/srep19375.

4. Accelerated materials property predictions and design using motif-based fingerprints;Huan;Phys. Rev. B,2015

5. Machine learning in materials science: recent progress and emerging applications;Mueller,2016

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