Machine Learning-Assisted Materials Design and Discovery of Low-Melting-Point Inorganic Oxides for Low-Temperature Cofired Ceramic Applications
Machine Learning-Assisted Materials Design and Discovery of Low-Melting-Point Inorganic Oxides for Low-Temperature Cofired Ceramic Applications
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用于低温共烧陶瓷应用的机器学习辅助材料设计和低熔点无机氧化物的发现
DOI：
10.1021/acssuschemeng.1c06983
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Yongxiang Li
中科院分区：
文献类型：
--
作者：
Jincheng Qin;Zhifu Liu;Mingsheng Ma;Yongxiang Li
低温共烧陶瓷（LTCC）在低温（<900 °C）下烧结致密化，具有节能环保的特点。然而，寻找新的LTCC材料的试错法是耗时和昂贵的。LTCC材料通常具有低熔点，因此从低熔点陶瓷中发现高性能的LTCC材料是可行的。采用两阶段机器学习框架建立无机氧化物熔点预测模型。在第一阶段建模中，化学成分被用作特征;而在第二阶段，根据领域知识，更多的特征被集成以优化预测模型。人工神经网络算法建立的第二阶段模型表现出最好的性能，R2 = 0.7968，均方根误差= 247.4（K）。三个特征，包括每原子的形成能（fepa），理论密度（d）和原子数（na），被提取为无机氧化物的决定性特征。熔点与fepa和d的绝对值呈正相关。na作为一种“隐性基因”，因为它的作用是间接的，但却是必要的。讨论了特征与熔点之间的物理关系。此外，LTCC无机氧化物通常具有统计上低于1400 °C的熔点。已报道的LTCC/ultra-LTCC材料验证了这一准则。采用ML模型计算了由3600种无机氧化物组成的预测集中材料的熔点，从而有效地筛选出潜在的LTCC材料。
The fabrication of low-temperature cofired ceramics (LTCCs) densified at a low sintering temperature (<900 °C) is energy-saving and environmentally friendly. However, finding novel LTCC materials by the trial-and-error method is time-consuming and costly. The LTCC materials often have low melting points, so it is feasible to discover high-performance LTCC materials out of the low-melting-point ceramics. A two-stage machine learning framework was adopted to establish the melting-point prediction model for inorganic oxides. Chemical compositions were used as features in stage 1 modeling; while in stage 2, more features were integrated according to domain knowledge to optimize the prediction model. Stage 2 model built by an artificial neural network algorithm shows the best performances withR2= 0.7968 and root-mean-square error = 247.4 (K). Three features, including formation energy per atom (fepa), theoretical density (d), and number of atoms (na), were extracted as the decisive characteristics of inorganic oxides. The melting point demonstrates positive correlations with the absolute value of fepa andd. The na acts as a “recessive gene” because its contribution is indirect but necessary. The physical relationships between features and the melting point were also discussed. Furthermore, the LTCC inorganic oxides often have melting points lower than 1400 °C statistically. This criterion was verified by the reported LTCC/ultra-LTCC materials. The melting points of materials in the prediction set consisting of ∼3600 inorganic oxides were calculated by the ML model, and thus, the underlying LTCC materials could be screened out efficiently.
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