Machine Learning-Assisted Design of Advanced Polymeric Materials

Abstract

Conspectus 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. In this Account, we summarize the state-of-the-art advancements concerning the ML-assisted design of polymeric materials. Regarding structure representation and database construction, the digital representations of polymers are the predominant methods in cheminformatics along with some newly developed methods that integrate the polymeric multiscale structure characteristics. When establishing a ML-based property prediction model, the key is choosing and optimizing ML models to attain high-precision predictions across a vast chemical structure space. Advanced ML algorithms, such as transfer learning and multitask learning, have been utilized to address the data and modeling challenges. During the ML-assisted screening process, by defining and combining polymer genes, virtual polymer candidates are generated, and subsequently, their properties are predicted and high-throughput screened using ML property prediction models. Finally, the promising polymers identified through this approach are verified by computer simulations and experiments. We provide an overview of our recent efforts toward developing ML-assisted design approaches for discovering advanced polymeric materials and emphasize the intricate nature of polymer structural design. To well describe the multiscale structures of polymers, new structure representation methods, such as polymer fingerprint and cross-linking descriptors, were developed. Moreover, a multifidelity learning method was proposed to leverage the multisource isomerous polymer data from experiments and simulations. Additionally, graph neural networks and Bayesian optimization methods have been developed and applied for predicting polymer properties as well as designing polymer structures and compositions. Finally, we identify the current challenges and point out the development directions in this emerging field. It is highly desirable to establish new structure representation and advanced ML modeling methods for polymeric materials, particularly when constructing polymer large models based on chemical language. Through this Account, we seek to stimulate further ...