Accelerating optimal elemental configuration search in crystal using Ising machine

This research demonstrates that Ising machines can effectively solve optimal elemental configuration searches in crystals, with Au-Cu alloys serving as an example. The energy function is derived using the cluster expansion method in the form of a QUBO function, enabling efficient problem-solving via Ising machines. Using the third-generation Fujitsu's Digital Annealer, we have successfully obtained reasonable solutions for crystal structures consisting of over <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mn>10</a:mn><a:mspace width="0.16em"/><a:mn>000</a:mn></a:mrow></a:math> atoms. Notably, we have also obtained plausible solutions for optimization problems with constrained solutions, such as situations where the composition ratio of atomic species is predetermined. These findings suggest that Ising machines can be valuable tools for addressing materials science challenges. Published by the American Physical Society 2024

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Publication Details

Journal
Physical Review Research
Published
2024-09-20
DOI
https://doi.org/10.1103/physrevresearch.6.033321
Citations
5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.59
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article

Accelerating optimal elemental configuration search in crystal using Ising machine

Satoru Ohuchi, Kazuhide Ichikawa, Tomoyasu Yokoyama, Koki Ueno
5 citations
Physical Review Research
Machine Learning in Materials Science
0.59
article

Accelerating optimal elemental configuration search in crystal using Ising machine

Satoru Ohuchi, Kazuhide Ichikawa, Tomoyasu Yokoyama, Koki Ueno
article en
5 citations

Abstract

This research demonstrates that Ising machines can effectively solve optimal elemental configuration searches in crystals, with Au-Cu alloys serving as an example. The energy function is derived using the cluster expansion method in the form of a QUBO function, enabling efficient problem-solving via Ising machines. Using the third-generation Fujitsu's Digital Annealer, we have successfully obtained reasonable solutions for crystal structures consisting of over <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mn>10</a:mn><a:mspace width="0.16em"/><a:mn>000</a:mn></a:mrow></a:math> atoms. Notably, we have also obtained plausible solutions for optimization problems with constrained solutions, such as situations where the composition ratio of atomic species is predetermined. These findings suggest that Ising machines can be valuable tools for addressing materials science challenges. Published by the American Physical Society 2024

Physical Review ResearchVol. 6(3)
Panasonic (Japan) (JP)
Openalex Percentile: Top 34%
Machine Learning in Materials Science
0.59
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Accelerating optimal elemental configuration search in crystal using Ising machine — Satoru Ohuchi, Kazuhide Ichikawa, et al. · Physical Review Research (2024) | TGRS Research Map | TGRS