Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition–Property Relationship: A Case Study of NanoMine Database
Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition–Property Relationship: A Case Study of NanoMine Database
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机器学习辅助理解聚合物纳米复合材料成分与性质关系：NanoMine 数据库案例研究
DOI：
10.1021/acs.macromol.2c02249
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发表时间：
2023
期刊：
影响因子：
5.5
通讯作者：
Brinson, L. Catherine
中科院分区：
文献类型：
--
作者：
Ma, Boran;Finan, Nicholas J.;Jany, David;Deagen, Michael E.;Schadler, Linda S.;Brinson, L. Catherine
NanoMine数据库是MaterialsMine数据库的两个节点之一，是一个新的材料数据资源，收集聚合物纳米复合材料（PNC）的注释数据。这项工作展示了NanoMine和其他材料数据资源的潜力，以帮助基本材料的理解，从而合理的材料设计。这个具体的案例研究是围绕研究玻璃化转变温度Tg（ΔTg）的变化与纳米填料和PNC中聚合物基体的关键描述符之间的关系而建立的。我们筛选了NanoMine中精选的2000多个实验样本的数据，训练了决策树分类器来预测PNCΔTg的符号，并建立了多元幂回归元模型来预测ΔTg。成功的模型使用的关键描述符，包括组成，纳米粒子的体积分数，和界面表面能。结果证明了使用聚合材料数据来获得洞察力和预测能力的力量。进一步的分析指出，必须对处理方法的参数进行额外分析，并不断增加精选数据集，以增加样本库规模。
The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperatureTg(ΔTg) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNCΔTg, and built a multiple power regression metamodel to predictΔTg. The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size.
影响因子：
6.1
作者：
Jain, Anubhav;Shyue Ping Ong;Persson, Kristin A.
通讯作者：
Persson, Kristin A.
影响因子：
13.6
作者：
Barnett, J. Wesley;Bilchak, Connor R.;Kumar, Sanat K.
通讯作者：
Kumar, Sanat K.
影响因子：
--
作者：
Rittigstein, Perla;Torkelson, John M.
通讯作者：
Torkelson, John M.