Acceleration of phase diagram construction by machine learning incorporating Gibbs' phase rule
Acceleration of phase diagram construction by machine learning incorporating Gibbs' phase rule
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DOI：
10.1016/j.scriptamat.2021.114335
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发表时间：
2021-10-20
影响因子：
6
通讯作者：
Tamara, Ryo
中科院分区：
文献类型：
--
作者：
Terayama, Kei;Han, Kwangsik;Tamara, Ryo
为了有效地构建合金系统的相图，提出了一种基于机器学习的相平衡热力学方法。通过在主动学习中使用不确定性采样，可以推荐下一个要综合或测量的点，以有效地绘制相图。为了获得适当的推荐，机器学习方法中引入了两个巧妙之处：检测到多相共存区域时的训练数据准备和基于吉布斯相位规则的搜索空间缩减。我们通过结合这些独创性，使用我们的机器学习方法演示了三元相图的构建。即使在初始阶段仅了解单组分系统的信息，也可以有效地绘制合金系统的复杂相图。与随机抽样相比，我们的机器学习方法提出的建议可以帮助将构建相图所需的实验数量减少到大约 1/8。 (c) 2021 年作者。由 Elsevier Ltd 代表 Acta Materialia Inc 发布。这是一篇基于 CC BY 许可证的开放获取文章 (http://creativecommons.org/licenses/by/4.0/)
To efficiently construct phase diagrams of alloy systems, a machine learning-based method advanced by thermodynamics on phase equilibria is proposed. With the use of uncertainty sampling in active learning, the next point to be synthesized or measured can be recommended to efficiently draw the phase diagram. For appropriate recommendations, two ingenuities are introduced in the machine learning method: training data preparation when the multiphase coexisting region is detected and search space reduction based on the Gibbs' phase rule. We demonstrate the construction of ternary phase diagrams using our machine learning method by incorporating these ingenuities. The complicated phase diagram of alloy systems could be effectively plotted even when knowing only the information of single-component systems in the initial step. The recommendation made by our machine learning method can help reduce the number of experiments required to construct a phase diagram to approximately 1/8 compared with random sampling. (c) 2021 The Authors. Published by Elsevier Ltd on behalf of Acta Materialia Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )