Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features
Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features
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DOI：
10.1021/bk-2019-1326.ch002
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发表时间：
2019-01
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影响因子：
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通讯作者：
Joy Garnett
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作者：
Joy Garnett
硬度，或抗永久或塑性变形的定量值，在许多应用的材料设计中起着至关重要的作用，如陶瓷涂层和磨料。硬度测试是一种特别有用的方法，因为它是非破坏性的，易于实施和测量材料的塑性性能。在这项研究中，我提出了一种机器或统计学习方法来预测天然陶瓷材料的硬度，该方法将原子和电子特征直接整合到各种矿物成分和晶体系统中。首先，原子和电子的特征，如货车德瓦尔斯，共价半径，和价电子数，从组成中提取。结果表明，该方法对F1值> 0的材料的莫氏硬度具有很好的预测效果。85 .本研究中的数据集包括在更大的材料和硬度值集合中建模，这在以前的研究中从未预测过。接下来，使用特征重要性来确定这些组成特征在多个硬度制度中的最强贡献。最后，在天然存在的陶瓷矿物上训练的模型被应用于合成的人工生长的单晶陶瓷。
Hardness , or the quantitative value of resistance to permanent or plastic deformation , plays a crucial role in materials design for many applications , such as ceramic coatings and abrasives . Hardness testing is an especially useful method because it is nondestructive and simple to implement and gauge the plastic properties of a material . In this study , I proposed a machine , or statistical , learning approach to predict hardness in naturally occurring ceramic materials , which integrates atomic and electronic features from composition directly across a wide variety of mineral compositions and crystal systems . First , atomic and electronic features , such as van der Waals , covalent radii , and the number of valence electrons , were extracted from composition . The results showed that this proposed method is very promising for predicting Mohs hardness with F1 - scores > 0 . 85 . The dataset in this study included modeling across a larger set of materials and hardness values , which have never been predicted in previous studies . Next , feature importances were used to identify the strongest contributions of these compositional features across multiple regimes of hardness . Finally , the models that were trained on naturally occurring ceramic minerals were applied to synthetic , artificially grown single crystal ceramics .