Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design
Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design
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DOI：
10.1016/j.matdes.2020.109168
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发表时间：
2020-11-01
影响因子：
8.4
通讯作者：
Kaneko, Hiromasa
中科院分区：
文献类型：
--
作者：
Shimizu, Naoto;Kaneko, Hiromasa
在高功能材料的研究和开发中，需要新的材料和化合物来实现多种性能和活性Y的目标值。因为为每个Y变量构建单个模型的回归方法不能考虑Y变量之间的关系，所以不能有效地执行对满足所有目标Y值的材料的候选的搜索。此外，模型的伪逆分析（其中基于通过将大量的解释变量X（例如实验条件和分子描述符）代入模型中而预测的Y值来选择有希望的候选者）不能从X个候选者中搜索具有期望Y值的候选者。因此，在这项研究中，我们专注于高斯混合回归（GMR），它可以同时处理多个Y变量。GMR可以同时预测多个Y变量，同时考虑Y变量之间的关系，它也可以直接预测X变量，通过替代多个Y变量的值。我们使用Y变量非线性相关且X与Y之间存在非线性关系的数值模拟数据，验证了GMR模型的预测能力以及GMR模型直接逆分析的优势。此外，我们分析了热电转换材料的数据集，并寻找新的高性能热电转换材料。(c)2020作者（S）由Elsevier Ltd.发布。这是CC BY许可下的开放获取文章（http：//creativecommons.org/licenses/by/4.0/）。
In research and development of highly functional materials, new materials and compounds are required to achieve target values of multiple properties and activities Y. Because regression methods that construct a single model for each Y variable cannot consider the relationships between the Y variables, the search for candidates for materials that satisfy all of the target Y values cannot be efficiently performed. Furthermore, pseudo-inverse analysis of models, where promising candidates are selected based on the Y values predicted by substituting a large number of candidates for explanatory variables X, such as experimental conditions and molecular descriptors, into models, cannot search for candidates with desired Y values from X candidates. Therefore, in this study, we focused on Gaussian mixture regression (GMR), which can simultaneously handle multiple Y variables. GMR can simultaneously predict multiple Y variables while considering the relationships between the Y variables, and it can also directly predict X variables by substituting the values of multiple Y variables. We used numerical simulation data in which the Y variables were nonlinearly correlated and nonlinear relationships existed between X and Y, and verified the predictive ability of the GMR model and the advantages of direct inverse analysis of the GMR model. In addition, we analyzed a dataset of thermoelectric conversion materials and searched for new high-performance thermoelectric conversion materials.(c) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).