Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys-Reference-Cited by-同舟云学术

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Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

Published:2020-08-21 Issue:7 Volume:153 Page:074705
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Tran Anh¹^ORCID,Tranchida Julien²^ORCID,Wildey Tim¹^ORCID,Thompson Aidan P.²^ORCID

Affiliation:

1. Optimization and Uncertainty Quantification, Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87123, USA

2. Computational Multiscale, Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87123, USA

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0015672

Reference54 articles.

1. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

2. Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

3. Machine learning assisted design of high entropy alloys with desired property

4. Towards an order-

5. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

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