Patra Tarak K
Department of Chemical Engineering, Center for Atomistic Modeling and Materials Design and Center for Carbon Capture Utilization and Storage, Indian Institute of Technology Madras, Chennai, TN 600036, India.
ACS Polym Au. 2021 Dec 28;2(1):8-26. doi: 10.1021/acspolymersau.1c00035. eCollection 2022 Feb 9.
Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.
由于聚合物具有巨大的化学和构型空间，其优化设计是一项具有挑战性的任务。计算、机器学习方面的最新进展，以及数据和软件可用性的不断增加，有可能解决这一问题，并加速聚合物的分子尺度设计。在此，本文回顾了聚合物设计的核心问题，并讨论了数据驱动方法的总体思路及其在聚合物设计背景下的工作原理。本综述提供了一个历史视角，总结了当前的趋势，并概述了数据驱动方法在聚合物研究中的未来应用范围。文中还介绍了一些使用此类数据驱动方法发现具有优异性能的新型聚合物的代表性案例研究。此外，本文还试图强调数据驱动策略如何有助于建立新的相关性，并推动对聚合物的基本理解。本综述认为，机器学习、聚合物的快速计算表征以及大量开源同类数据的可用性相结合，将在未来几十年改变聚合物的研发。希望本综述能为希望开发和应用数据驱动方法进行聚合物研究和教育的研究人员提供有用的参考。
