Adaptive Strategies for Materials Design using Uncertainties.
Adaptive Strategies for Materials Design using Uncertainties.
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DOI：
10.1038/srep19660
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发表时间：
2016-01-21
影响因子：
4.6
通讯作者：
Lookman T
中科院分区：
文献类型：
--
作者：
Balachandran PV;Xue D;Theiler J;Hogden J;Lookman T
我们使用223 M2 AX系列化合物的数据集比较了几种自适应设计策略，这些化合物的弹性性能[体积（B）、剪切（G）和杨氏（E）模量]已使用密度泛函理论计算。设计策略被分解成一个迭代循环，其中有两个主要步骤：机器学习用于训练回归器，该回归器根据材料各个组分的基本轨道半径预测弹性特性;选择器使用这些预测及其不确定性来选择下一个要研究的材料。最终目标是在尽可能少的迭代中获得具有所需弹性特性的材料。我们研究了数据集大小，回归量和选择器的选择如何影响设计。我们发现，选择器，使用信息的预测不确定性优于那些不。我们的工作是说明自适应设计工具如何指导寻找具有所需特性的新材料的一个步骤。
We compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.