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

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 4, 2024

Language: Английский

---

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

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 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.

Language: Английский

---

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

Device, Journal Year: 2024, Volume and Issue: 2(10), P. 100500 - 100500

Published: Aug. 8, 2024

Language: Английский

---

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

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2024, Volume and Issue: 34(9)

Published: Sept. 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.

Language: Английский

---

Machine learning for thermal transport

Journal of Applied Physics, Journal Year: 2024, Volume and Issue: 136(16)

Published: Oct. 24, 2024

Language: Английский

---