Revealing nanostructures in high-entropy alloys via machine-learning accelerated scalable Monte Carlo simulation Article Swipe
Xianglin Liu
,
Kai Yang
,
Yongxiang Liu
,
Fei Zhou
,
Dengdong Fan
,
Zongrui Pei
,
Pengxiang Xu
,
Yonghong Tian
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-025-01762-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-025-01762-8
First-principles Monte Carlo (MC) simulations at finite temperatures are computationally prohibitive for large systems due to the high cost of quantum calculations and poor parallelizability of sequential Markov chains in MC algorithms. We introduce scalable Monte Carlo at eXtreme (SMC-X), a generalized checkerboard algorithm designed to accelerate MC simulation with arbitrary short-range interactions, including machine learning potentials, on modern accelerator hardware. The GPU implementation, SMC-GPU, harnesses massive parallelism to enable billion-atom simulations when combined with machine-learning surrogates of density functional theory (DFT). We apply SMC-GPU to explore nanostructure evolution in two high-entropy alloys, FeCoNiAlTi and MoNbTaW, revealing diverse morphologies including nanoparticles, 3D-connected NPs, and disorder-stabilized phases. We quantify their size, composition, and morphology, and simulate an atom-probe tomography (APT) specimen for direct comparison with experiments. Our results highlight the potential of large-scale, data-driven MC simulations in exploring nanostructure evolution in complex materials, opening new avenues for computationally guided alloy design.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41524-025-01762-8
- https://www.nature.com/articles/s41524-025-01762-8.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 78
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4413356121Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41524-025-01762-8Digital Object Identifier
- Title
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Revealing nanostructures in high-entropy alloys via machine-learning accelerated scalable Monte Carlo simulationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-20Full publication date if available
- Authors
-
Xianglin Liu, Kai Yang, Yongxiang Liu, Fei Zhou, Dengdong Fan, Zongrui Pei, Pengxiang Xu, Yonghong TianList of authors in order
- Landing page
-
https://doi.org/10.1038/s41524-025-01762-8Publisher landing page
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https://www.nature.com/articles/s41524-025-01762-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41524-025-01762-8.pdfDirect OA link when available
- Concepts
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Monte Carlo method, Scalability, High entropy alloys, Computer science, Statistical physics, Nanostructure, Materials science, Physics, Nanotechnology, Mathematics, Alloy, Metallurgy, Statistics, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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78Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2006569455, https://openalex.org/W1167040955, https://openalex.org/W2258120943, https://openalex.org/W2052810501, https://openalex.org/W2941852398, https://openalex.org/W1986188430, https://openalex.org/W4225405705, https://openalex.org/W4388555751, https://openalex.org/W4406472463, https://openalex.org/W4379768497, https://openalex.org/W2083415705, https://openalex.org/W2891365537, https://openalex.org/W3183767639, https://openalex.org/W4296126761, https://openalex.org/W4405269740, https://openalex.org/W4400266871, https://openalex.org/W4310135808, https://openalex.org/W4384154882, https://openalex.org/W3132277775, https://openalex.org/W3119697748, https://openalex.org/W4319162181, https://openalex.org/W4220914069, https://openalex.org/W3113616921, https://openalex.org/W3035839386, https://openalex.org/W2942913009, https://openalex.org/W4405365348, https://openalex.org/W4400119746, https://openalex.org/W4385461613, https://openalex.org/W2517753147, https://openalex.org/W3100160223, https://openalex.org/W3207285451, https://openalex.org/W2124119654, https://openalex.org/W2109197177, https://openalex.org/W2952546068, https://openalex.org/W2983490771, https://openalex.org/W1990141297, https://openalex.org/W2111223281, https://openalex.org/W2191154189, https://openalex.org/W2034377760, https://openalex.org/W3201073812, https://openalex.org/W3188477177, https://openalex.org/W2058085399, https://openalex.org/W2003975937, https://openalex.org/W2408247608, https://openalex.org/W2900812253, https://openalex.org/W3181131756, https://openalex.org/W4362467786, https://openalex.org/W4400248596, https://openalex.org/W4292685679, https://openalex.org/W4379744775, https://openalex.org/W4399376281, https://openalex.org/W4225986862, https://openalex.org/W2979622231, https://openalex.org/W4400945208, https://openalex.org/W2006326929, https://openalex.org/W2896718190, https://openalex.org/W2278970271, https://openalex.org/W2055023495, https://openalex.org/W4206488708, https://openalex.org/W2970097707, https://openalex.org/W4407190020, https://openalex.org/W4396720590, https://openalex.org/W4403214390, https://openalex.org/W2037139490, https://openalex.org/W2003582562, https://openalex.org/W2472450939, https://openalex.org/W2790666950, https://openalex.org/W2765984076, https://openalex.org/W2142501550, https://openalex.org/W2287944230, https://openalex.org/W2175296034, https://openalex.org/W2269580764, https://openalex.org/W2028056984, https://openalex.org/W2015178329, https://openalex.org/W2055520220, https://openalex.org/W4387975432, https://openalex.org/W2898265080, https://openalex.org/W4407376404 |
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