Physical Review B, volume 101, issue 14, publication number 144505

Predicting novel superconducting hydrides using machine learning approaches

Hutcheon Michael J. ¹

Shipley Alice M. ¹

Needs Richard J. ¹

Needs R. J. ¹

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Theory of Condensed Matter Group, Cavendish Laboratory, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom |

Publication type: Journal Article

Publication date: 2020-04-22

American Physical Society (APS)

Journal: Physical Review B

Quartile SCImago

Quartile WOS

Impact factor: 3.7

ISSN: 24699950, 24699969, 10980121, 1550235X

DOI: 10.1103/PhysRevB.101.144505

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Abstract

The search for superconducting hydrides has, so far, largely focused on finding materials exhibiting the highest possible critical temperatures (${T}_{c}$). This has led to a bias toward materials stabilized at very high pressures, which introduces a number of technical difficulties in experiment. Here we apply machine learning methods in an effort to identify superconducting hydrides that can operate closer to ambient conditions. The output of these models informs subsequent crystal structure searches, from which we identify stable metallic candidates prior to performing electron-phonon calculations to obtain ${T}_{c}$. Hydrides of alkali and alkaline earth metals are identified as especially promising; of particular note, a ${T}_{c}$ of up to 115 K is calculated for ${\mathrm{RbH}}_{12}$ at 50 GPa, which extends the operational pressure-temperature range occupied by hydride superconductors toward ambient conditions.

By date By citations

Citations by journals

	1 2 3 4 5 6
Physical Review B	Physical Review B, 6, 23.08% Physical Review B 6 publications, 23.08%
Current Opinion in Solid State and Materials Science	Current Opinion in Solid State and Materials Science, 1, 3.85% Current Opinion in Solid State and Materials Science 1 publication, 3.85%
Physical Review Letters	Physical Review Letters, 1, 3.85% Physical Review Letters 1 publication, 3.85%
Journal of Physics Condensed Matter	Journal of Physics Condensed Matter, 1, 3.85% Journal of Physics Condensed Matter 1 publication, 3.85%
Journal of Applied Physics	Journal of Applied Physics, 1, 3.85% Journal of Applied Physics 1 publication, 3.85%
Materials	Materials, 1, 3.85% Materials 1 publication, 3.85%
Nature Communications	Nature Communications, 1, 3.85% Nature Communications 1 publication, 3.85%
Nature Chemistry	Nature Chemistry, 1, 3.85% Nature Chemistry 1 publication, 3.85%
npj Computational Materials	npj Computational Materials, 1, 3.85% npj Computational Materials 1 publication, 3.85%
Reviews of Modern Physics	Reviews of Modern Physics, 1, 3.85% Reviews of Modern Physics 1 publication, 3.85%
Patterns	Patterns, 1, 3.85% Patterns 1 publication, 3.85%
Superconductor Science and Technology	Superconductor Science and Technology, 1, 3.85% Superconductor Science and Technology 1 publication, 3.85%
Chemistry of Materials	Chemistry of Materials, 1, 3.85% Chemistry of Materials 1 publication, 3.85%
Nanoscale	Nanoscale, 1, 3.85% Nanoscale 1 publication, 3.85%
Journal of Materials Chemistry A	Journal of Materials Chemistry A, 1, 3.85% Journal of Materials Chemistry A 1 publication, 3.85%
Computational Materials Science	Computational Materials Science, 1, 3.85% Computational Materials Science 1 publication, 3.85%
Superconductivity	Superconductivity, 1, 3.85% Superconductivity 1 publication, 3.85%
Journal of Chemical Physics	Journal of Chemical Physics, 1, 3.85% Journal of Chemical Physics 1 publication, 3.85%
Materials Today Physics	Materials Today Physics, 1, 3.85% Materials Today Physics 1 publication, 3.85%
	1 2 3 4 5 6

Citations by publishers

	1 2 3 4 5 6 7 8
American Physical Society (APS)	American Physical Society (APS), 8, 30.77% American Physical Society (APS) 8 publications, 30.77%
Elsevier	Elsevier, 5, 19.23% Elsevier 5 publications, 19.23%
Springer Nature	Springer Nature, 3, 11.54% Springer Nature 3 publications, 11.54%
IOP Publishing	IOP Publishing, 2, 7.69% IOP Publishing 2 publications, 7.69%
American Institute of Physics (AIP)	American Institute of Physics (AIP), 2, 7.69% American Institute of Physics (AIP) 2 publications, 7.69%
Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 2, 7.69% Royal Society of Chemistry (RSC) 2 publications, 7.69%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 1, 3.85% Multidisciplinary Digital Publishing Institute (MDPI) 1 publication, 3.85%
American Chemical Society (ACS)	American Chemical Society (ACS), 1, 3.85% American Chemical Society (ACS) 1 publication, 3.85%
	1 2 3 4 5 6 7 8

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Hutcheon M. J. et al. Predicting novel superconducting hydrides using machine learning approaches // Physical Review B. 2020. Vol. 101. No. 14. 144505

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Hutcheon M. J., Shipley A. M., Needs R. J., Needs R. J. Predicting novel superconducting hydrides using machine learning approaches // Physical Review B. 2020. Vol. 101. No. 14. 144505

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RIS Copy

TY - JOUR

DO - 10.1103/PhysRevB.101.144505

UR - https://doi.org/10.1103%2FPhysRevB.101.144505

TI - Predicting novel superconducting hydrides using machine learning approaches

T2 - Physical Review B

AU - Hutcheon, Michael J.

AU - Shipley, Alice M.

AU - Needs, Richard J.

AU - Needs, R. J.

PY - 2020

DA - 2020/04/22 00:00:00

PB - American Physical Society (APS)

IS - 14

VL - 101

SN - 2469-9950

SN - 2469-9969

SN - 1098-0121

SN - 1550-235X

ER -

BibTex

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BibTex Copy

@article{2020_Hutcheon,

author = {Michael J. Hutcheon and Alice M. Shipley and Richard J. Needs and R. J. Needs},

title = {Predicting novel superconducting hydrides using machine learning approaches},

journal = {Physical Review B},

year = {2020},

volume = {101},

publisher = {American Physical Society (APS)},

month = {apr},

url = {https://doi.org/10.1103%2FPhysRevB.101.144505},

number = {14},

doi = {10.1103/PhysRevB.101.144505}

}

Found error?

Publisher

American Physical Society (APS)

Journal

Physical Review B

Quartile SCImago

Quartile WOS

Impact factor

3.7

ISSN

24699950 (Print)
24699969 (Electronic)
10980121 (Print)
1550235X (Electronic)