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高通量选方法的框架,以评估聚合物/塑化剂混合性:聚合烯中的碳化合物的案例

Lois Smith¹, Hossein Ali Karimi-Varzaneh², Sebastian Finger²

¹Department of Chemical Engineering, School of Engineering, The University of Manchester, Oxford Road, M13 9PL Manchester, U.K.

|June 3, 2024

概括

研究人员开发了一种机器学习模型,以预测聚合物中的可塑剂混合性. 该方法使用分子模拟来识别关键的分子特征,大大提高了选择有效的塑化剂的效率,用于聚合物加工.

科学领域:

聚合物科学聚合物科学
材料科学材料科学材料科学
计算化学的计算化学

背景情况:

聚合物复合材料需要塑化剂 (PLs) 通过降低玻璃过渡温度来提高可加工性.
PLs的有效性在很大程度上取决于它们与聚合物矩阵的可混合性,这取决于PL特征,如尺寸,拓和灵活性.
庞大的潜在PL数量使得传统的试错选效率低下.

研究的目的:

确定可靠的拓和热力学描述器,用于预测聚合物中的可塑化剂混合性.
开发一种使用分子模拟进行选的低成本计算方法,用于选塑化剂.
建立一个机器学习模型,用于自动化塑化剂选过程.

主要方法:

在48种可塑剂的数据集上利用粗粒度分子模拟.
建立了增塑剂拓,内部灵活性,聚合热力学和可混合性之间的相关性.
开发并验证了一种使用这些描述符的决策树模型,用于将可塑剂分类为可混合或不可混合的.

主要成果:

确定了关键的拓和热力学描述符,可以准确预测塑化剂的可混合性.
决策树模型通过交叉验证实现了0.86 ± 0.01的高F1得分,证明了强大的预测性能.
开发的程序使选试验空间的可塑化剂减少了10倍.

结论:

基于分子模拟描述器的机器学习为自动化可塑剂选提供了一个有前途的途径.
鉴定的描述符和方法可以加速开发高效的工作流程,用于选择各种聚合物,包括多聚烯的可塑化剂.
可能需要进一步的研究,以适应极性相互作用主导混合性的系统的描述符.

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