Abstract Exploring elastocaloric materials with high transformation entropy change (ΔS) is a key mission for the development of elastocaloric refrigeration technology. Here, we show an adaptive design strategy, tightly coupled a machine learning (ML) with theoretical calculations to accelerate the discovery process of multi-component Cu-Al-based shape memory alloys (SMAs) with high ΔS. Based on a linear regression model, Al, Co, Fe, Ni are the elements that are beneficial to the significant promotion of ΔS in the Cu-Al-based alloys. In our results, Cu72.2Al20.2Ni6.2Co0.7B0.7 is discovered with the highest ΔS of 1.88 J/mol K from a potential space of ~500,000 compositions, which is higher than the highest ones found in ternary Cu-Al-Mn and reported experimental value by 9.9% and 17.5%.

中文翻译：

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通过机器学习加速具有高弹性热性能的多组分 Cu-Al 基形状记忆合金的开发

摘要 探索具有高转化熵变（ΔS）的弹热材料是弹热制冷技术发展的关键任务。在这里，我们展示了一种自适应设计策略，将机器学习 (ML) 与理论计算紧密结合，以加速具有高 ΔS 的多组分 Cu-Al 基形状记忆合金 (SMA) 的发现过程。基于线性回归模型，Al、Co、Fe、Ni是有利于显着促进Cu-Al基合金中ΔS的元素。在我们的结果中，发现 Cu72.2Al20.2Ni6.2Co0.7B0.7 的最大 ΔS 为 1.88 J/mol K，来自约 500,000 种成分的潜在空间，高于三元 Cu-Al- Mn 和报告的实验值分别为 9.9% 和 17.5%。