Instituto de Ciencias e Ingeniería de la Computación (ICIC), Universidad Nacional del Sur (UNS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Buenos Aires 8000, Argentina.
Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Buenos Aires 8000, Argentina.
J Chem Phys. 2022 May 28;156(20):204903. doi: 10.1063/5.0087392.
The artificial intelligence-based prediction of the mechanical properties derived from the tensile test plays a key role in assessing the application profile of new polymeric materials, especially in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.
基于人工智能的拉伸试验机械性能预测在评估新型聚合物材料的应用范围方面起着关键作用，特别是在设计阶段，即在合成之前。当使用市场日益要求的具有改进性能的新型聚合物时，这种策略可以节省时间和资源。本文提出了一种断裂拉伸强度的定量结构-性能关系(QSPR)模型。这里应用的 QSPR 方法基于机器学习工具、可视化分析方法和专家循环策略。从整个研究中，提出了一个由五个分子描述符组成的 QSPR 模型，该模型达到了 0.9226 的相关系数。我们在两个分析层次上应用了可视化分析工具：一个更一般的层次，其中模型因冗余信息指标而被丢弃，另一个更深入的层次，化学专家可以从物理化学的角度出发，根据分子描述符的子集，对模型的组成做出决策。通过这种方式，通过本工作，我们完成了对聚合物信息学的贡献循环，提供了面向预测与拉伸试验相关的机械性能的 QSPR 模型。
