Polymeric material research is encountering a new paradigm driven by machine learning (ML) and big data. The ML-assisted design has proven to be a successful approach for designing novel high-performance polymeric materials. This goal is mainly achieved through the following procedure: structure representation and database construction, establishment of a ML-based property prediction model, virtual design and high-throughput screening. The key to this approach lies in training ML models that delineate structure–property relationships based on available polymer data (e.g., structure, component, and property data), enabling the screening of promising polymers that satisfy the targeted property requirements. However, the relative scarcity of high-quality polymer data and the complex polymeric multiscale structure–property relationships pose challenges for this ML-assisted design method, such as data and modeling challenges.

中文翻译：

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先进高分子材料的机器学习辅助设计


高分子材料研究正在遇到由机器学习（ML）和大数据驱动的新范式。机器学习辅助设计已被证明是设计新型高性能聚合物材料的成功方法。这一目标主要通过以下过程实现：结构表示和数据库构建、基于机器学习的性能预测模型的建立、虚拟设计和高通量筛选。这种方法的关键在于训练机器学习模型，该模型根据可用的聚合物数据（例如结构、成分和性能数据）描绘结构-性能关系，从而能够筛选出满足目标性能要求的有前途的聚合物。然而，高质量聚合物数据的相对稀缺和复杂的聚合物多尺度结构-性能关系给这种机器学习辅助设计方法带来了挑战，例如数据和建模挑战。