噪音感知主动学习开发高温形状记忆合金,具有较大的潜热
Yuan Tian1, Bin Hu2, Pengfei Dang3
1Materials Genome Institute, Shanghai University, Shanghai, 200444, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 3, 2024
概括
一个新的噪声感知主动学习策略加速了用于高温热能存储 (TES) 的形状记忆合金 (SMA) 的设计. 这种方法有效地识别了使用杂实验数据增强潜热的SMA.
科学领域:
- 材料科学 材料科学 材料科学
- 热力学是一种热力学.
- 数据科学数据科学数据科学
背景情况:
- 形状记忆合金 (SMA) 对于高温热能存储 (TES) 是有前途的,因为它们在相位转换过程中具有很大的潜热量.
- 设计具有理想性质的SMA,如高潜热和转换温度,由于庞大的组成空间和杂的实验数据,具有挑战性.
研究的目的:
- 开发一种数据驱动的方法,以加快基于NiTi的SMA设计,用于高温TES应用.
- 通过一种新的噪音意识主动学习策略,应对材料设计中的噪音实验数据的挑战.
主要方法:
- 提出了一种对噪音有意识的积极学习策略,包括一个对噪音有意识的Kriging模型.
- 通过在一系列噪声超参数中最小化模型误差来估计最佳噪声水平.
- 这种方法被用来搜索杂的基于NiTi的SMA的组件空间.
主要成果:
- 该策略导致发现了一个SMA,其潜在热量为-36.08 Jg−1,在四个额外的实验中,超过了以前的最佳值9.2%.
- 新发现的合金表现出高奥氏体表面温度481.71°C和相对较小的hysteresis.
- 这在识别高温环境中潜热TES的SMA方面取得了重大进展.
结论:
- 噪音感知主动学习方法有效地加速了使用噪音数据的材料设计.
- 这种方法有助于发现具有高性能高温热能存储的SMA.
- 开发的策略适用于数据驱动的材料设计,可用于各种面临噪音数据挑战的领域.
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