Ma Boran, Finan Nicholas J, Jany David, Deagen Michael E, Schadler Linda S, Brinson L Catherine
Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States.
Department of Department of Mechanical Engineering, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, Vermont 05405, United States.
Macromolecules. 2023 May 23;56(11):3945-3953. doi: 10.1021/acs.macromol.2c02249. eCollection 2023 Jun 13.
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 temperature () 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 PNC , and built a multiple power regression metamodel to predict . 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.
纳米矿数据库是材料矿数据库的两个节点之一，是一个新的材料数据资源，收集有关聚合物纳米复合材料（PNC）的注释数据。这项工作展示了纳米矿和其他材料数据资源在辅助基础材料理解从而进行合理材料设计方面的潜力。这个具体的案例研究围绕着研究玻璃化转变温度（）的变化与PNC中纳米填料和聚合物基体的关键描述符之间的关系展开。我们筛选了纳入纳米矿的2000多个实验样本的数据，训练了一个决策树分类器来预测PNC 的符号，并构建了一个多元幂回归元模型来预测。成功的模型使用了包括组成、纳米颗粒体积分数和界面表面能在内的关键描述符。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。进一步的分析指出了对加工方法中的参数进行额外分析以及不断添加经过整理的数据集以增加样本池大小的重要性。
