Artificial neural network assisted by first-principles calculations for predicting transformation temperatures in shape memory alloys
Artificial neural network assisted by first-principles calculations for predicting transformation temperatures in shape memory alloys
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DOI：
10.1142/s0217979219500553
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发表时间：
2019-04
影响因子：
1.7
通讯作者：
Daichi Minami;T. Uesugi;Y. Takigawa;K. Higashi
中科院分区：
文献类型：
--
作者：
Daichi Minami;T. Uesugi;Y. Takigawa;K. Higashi
新型形状记忆合金设计的一个关键特性是其工作温度范围，该范围取决于其转变温度 T0。在之前的工作中，T0 是使用关于母体相和马氏体相之间的能量差的简单线性回归来预测的，[公式：参见文本]E[公式：参见文本]。在本文中，我们开发了一种基于机器学习并辅以第一性原理计算的准确预测 T0 的方法。对 15 种形状记忆合金进行了第一性原理计算；然后，我们提出了一种人工神经网络方法，不仅使用计算的[公式：参见文本]E[公式：参见文本]，还使用体积模量作为输入变量来预测T0。与简单线性回归的 188 K 相比，所提出的人工神经网络的 T0 预测误差提高到 49 K。
A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T0. In previous works, T0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, [Formula: see text]E[Formula: see text]. In this paper, we developed an accurate method to predict T0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed [Formula: see text]E[Formula: see text] but also bulk moduli as input variables to predict T0. The prediction error of T0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression.