Wolf Caitlyn M, Guio Lorenzo, Scheiwiller Sage, Pakhnyuk Viktoria, Luscombe Christine, Pozzo Lilo D
Department of Chemical Engineering, University of Washington, Box 351750, Seattle, Washington 98195-1750, United States.
Center for Neutron Research, Stop 6102, National Institute of Standards and Technology, Gaithersburg, Maryland 20889-6102, United States.
ACS Polym Au. 2021 Nov 1;1(3):134-152. doi: 10.1021/acspolymersau.1c00027. eCollection 2021 Dec 8.
Conjugated polymers (CPs) enable a wide range of lightweight, lower cost, and flexible organic electronic devices, but a thorough understanding of relationships between molecular structure and dynamics and electronic performance is critical for improved device efficiencies and for new technologies. Molecular dynamics (MD) simulations offer insight into this relationship, but their accuracy relies on the approach used to develop the model's parameters or force field (FF). In this Perspective, we first review current FFs for CPs and find that most of the models implement an arduous reparameterization of inter-ring torsion potentials and partial charges of classical FFs. However, there are few FFs outside of simple CP molecules, e.g., polythiophenes, that have been developed over the last two decades. There is also limited reparameterization of other parameters, such as nonbonded Lennard-Jones interactions, which we find to be directly influenced by conjugation in these materials. We further provide a discussion on experimental validation of MD FFs, with emphasis on neutron and X-ray scattering. We define multiple ways in which various scattering methods can be directly compared to results of MD simulations, providing a powerful experimental validation metric of local structure and dynamics at relevant length and time scales to charge transport mechanisms in CPs. Finally, we offer a perspective on the use of neutron scattering with machine learning to enable high-throughput parametrization of accurate and experimentally validated CP FFs enabled not only by the ongoing advancements in computational chemistry, data science, and high-performance computing but also using oligomers as proxies for longer polymer chains during FF development.
共轭聚合物（CPs）可用于制造各种轻量级、低成本且灵活的有机电子器件，但深入了解分子结构与动力学以及电子性能之间的关系对于提高器件效率和开发新技术至关重要。分子动力学（MD）模拟有助于深入了解这种关系，但其准确性取决于用于开发模型参数或力场（FF）的方法。在这篇综述中，我们首先回顾了当前用于CPs的力场，发现大多数模型都对经典力场的环间扭转势和部分电荷进行了艰巨的重新参数化。然而，在过去二十年中，除了简单的CP分子（如聚噻吩）之外，很少有力场得到开发。其他参数（如非键合的 Lennard-Jones 相互作用）的重新参数化也很有限，我们发现这些参数在这些材料中直接受到共轭的影响。我们进一步讨论了MD力场的实验验证，重点是中子和X射线散射。我们定义了多种方法，可以将各种散射方法与MD模拟结果直接进行比较，从而为CPs中电荷传输机制在相关长度和时间尺度上的局部结构和动力学提供一个强大的实验验证指标。最后，我们展望了将中子散射与机器学习相结合的应用前景，通过计算化学、数据科学和高性能计算的不断进步，以及在力场开发过程中使用低聚物作为较长聚合物链的替代物，实现对准确且经过实验验证的CP力场进行高通量参数化。
