Chemical short-range order increases the phonon heat conductivity in a refractory high-entropy alloy

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 4, 2024

Язык: Английский

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Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

Artificial intelligence (AI) in science is a key area of modern research. However, many current machine learning methods lack interpretability, making it difficult to grasp the physical mechanisms behind various phenomena, which hampers progress related fields. This study focuses on Poisson's ratio hexagonal lattice elastic network as varies with structural deformation. By employing Kolmogorov-Arnold Network (KAN), transition network's from positive negative element shifts convex polygon concave was accurately predicted. The KAN provides clear mathematical framework that describes this transition, revealing connection between and geometric properties element, identifying parameters at equals zero. work demonstrates significant potential clarify relationships underpin responses behaviors.

Язык: Английский

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Metamaterials based on solid composites enable continuous and tunable thermal conductivity anisotropy for thermal management applications

Device, Год журнала: 2024, Номер 2(10), С. 100500 - 100500

Опубликована: Авг. 8, 2024

Язык: Английский

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Abnormal suppression of thermal transport by long-range interactions in networks

Chaos An Interdisciplinary Journal of Nonlinear Science, Год журнала: 2024, Номер 34(9)

Опубликована: Сен. 1, 2024

Heat and electricity are two fundamental forms of energy widely utilized in our daily lives. Recently, the study complex networks, there is growing evidence that they behave significantly different at micro-nanoscale. Here, we use a small-world network model to investigate effects reconnection probability p decay exponent α on thermal electrical transport within network. Our results demonstrate efficiency increases by nearly one order magnitude, while falls off cliff three four orders breaking traditional rule shortcuts enhance networks. Furthermore, elucidate phonon localization crucial factor weakening networks characterizing density states, participation ratio, nearest-neighbor spacing distribution. These insights will pave new ways for designing thermoelectric materials with high conductance low conductance.

Язык: Английский

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Machine learning for thermal transport

Journal of Applied Physics, Год журнала: 2024, Номер 136(16)

Опубликована: Окт. 24, 2024

Язык: Английский

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