Yuxi Yang , Haoyang Fu , Weihong Gao , Wenlong Su , Bin Sun , Xiaoyang Yi , Ting Zheng , Xianglong Meng
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引用次数: 0
摘要
形状记忆合金(SMA)具有通过耦合激发多种热效应来提高固态制冷技术效率的潜力。为了实现高弹性镍锰基 SMA,本文利用机器学习预测绝热温度变化,并通过第一原理计算阐明其机理。根据最优 XGB 回归模型,预测通过定向凝固的 Ni50Mn33Ti17 SMA 的绝热温度变化最高,达到 10 K(测试温度 = 298 K,施加应力 = 300 MPa)。此外,根据第一性原理计算,马氏体转变后的体积变化率达到 2.375%,这有望提供足够的熵,从而获得出色的弹性热效应。这项研究为设计和优化镍锰基 SMA 的弹性热稳定性提供了一条可行的途径。
Data-driven high elastocaloric NiMn-based shape memory alloy optimization with machine learning
Shape memory alloys (SMAs) have the potential to improve the efficiency of solid-state refrigeration technology through coupled excitation of multiple thermal effects. Aiming to achieve high elastocaloric NiMn-based SMAs, this paper utilized machine learning to predict the adiabatic temperature change and first-principle calculations to elucidate the mechanism. Based on the optimal XGB Regressor model, the Ni50Mn33Ti17 SMA through directional solidification is predicted to have the highest adiabatic temperature change of 10 K (test temperature = 298 K, applied stress = 300 MPa). In addition, the volume change ratio after martensitic transformation reaches 2.375 % with first-principles calculations, which is expected to provide sufficient entropy and thus obtain an excellent elastocaloric effect. This study provides an available pathway to design and optimize the elastocaloric property of NiMn-based SMAs.
期刊介绍:
Materials Letters has an open access mirror journal Materials Letters: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
Materials Letters is dedicated to publishing novel, cutting edge reports of broad interest to the materials community. The journal provides a forum for materials scientists and engineers, physicists, and chemists to rapidly communicate on the most important topics in the field of materials.
Contributions include, but are not limited to, a variety of topics such as:
• Materials - Metals and alloys, amorphous solids, ceramics, composites, polymers, semiconductors
• Applications - Structural, opto-electronic, magnetic, medical, MEMS, sensors, smart
• Characterization - Analytical, microscopy, scanning probes, nanoscopic, optical, electrical, magnetic, acoustic, spectroscopic, diffraction
• Novel Materials - Micro and nanostructures (nanowires, nanotubes, nanoparticles), nanocomposites, thin films, superlattices, quantum dots.
• Processing - Crystal growth, thin film processing, sol-gel processing, mechanical processing, assembly, nanocrystalline processing.
• Properties - Mechanical, magnetic, optical, electrical, ferroelectric, thermal, interfacial, transport, thermodynamic
• Synthesis - Quenching, solid state, solidification, solution synthesis, vapor deposition, high pressure, explosive