Polyhydroxyalkanoate-based polymers-being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties-are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. The burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature Tg as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure-property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured Tg values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified Tg. While the present contribution is focused specifically on Tg, the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

译文

:基于聚羟基链烷酸酯的聚合物环保,可生物合成,经济上可行,并具有广泛的可调性能,目前正积极地用作石油基塑料的有前途的替代品。在这类聚合物中可利用的巨大化学复杂性在合理发现用于特定应用的新型聚合物化学中提出了挑战。高分子信息学的新兴领域通过提供基于可靠的过去数据的替代机器学习模型来提供加速性能预测和材料设计的工具和策略,从而解决了这一挑战。在这一贡献中,我们以玻璃化转变温度Tg作为示例目标特性,以证明基于数据的途径有望加速学习基于PHA的聚合物中精确的结构特性图。我们的分析使用了一组根据实验测得的Tg值,聚合物分子量以及基于文献的PHA基均聚物和共聚物的多分散指数的数据集。设计了一种指纹图谱,该图谱可基于拓扑,形状以及形成聚合物主链的特定化学单元或基序的电荷/极性捕获关键特性,以数字方式表示聚合物。然后开发经过验证的统计学习模型,以允许以物理上有意义和可靠的方式将聚合物指纹映射到属性空间上。一旦建立,该模型不仅可以快速预测新型PHA聚合物的性质,而且还可以提供预测所依据的不确定性。该模型还与基于进化算法的搜索策略结合在一起,可以有效地识别具有预定Tg的多组分聚合物成分。尽管当前的贡献专门针对Tg,但是这里提出的替代模型开发方法是通用的,并且原则上可以扩展到其他属性的范围。

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