npj Computational Materials

Editorial Summary

Developing polymer grafting self-assembly multi-body potential by machine learning

The incorporation of nanoparticles (NPs) into polymers is a powerful strategy for improving their thermomechanical properties or for introducing new attributes (optical, electronic, magnetic, or catalytic properties) into otherwise inert polymers. The surfaces of the NPs are traditionally grafted with polymer ligands, as the grafts introduce steric (entropic) repulsion between NPs that, if sufficiently large, can overcome the attractive interparticle forces that promote aggregation. However, emerging studies show that polymer grafting can also be used to direct NP assembly and access distinctive mesoscopic morphologies such as 1D strings and 2D sheets. These low-dimensional morphologies often exhibit functional properties very distinct from their 3D counterparts (superlattices, globular aggregates). The formation of such anisotropic NP phases arises from higher-body interactions between NPs, that is, perturbative corrections to the free energy from the higher-order arrangement of particles beyond pairwise distances. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. In recent years, machine learning (ML) techniques have been a viable tool to efficiently approximate the many-body interactions in atomistic systems and have been used to speed up ab initio molecular dynamics (MD) simulations. In this work, Yilong Zhou et al. from the Department of Mechanical Engineering and Materials Science, Duke University, employed ML to develop an analytical potential that can accurately capture many-body interactions between polymer-grafted NPs in a polymer matrix and used the potential to explore NP assembly over large length and time scales. The approach involves the calculation of potentials of mean force for the NPs in the polymer matrix through MD simulations and fitting them using permutationally invariant polynomials cast as functions of Coulomb-transformations of interparticle distances. To validate the developed ML potential, the authors used it to carry out MD simulations of NPs undergoing assembly and showed that all known structural phases, namely the 1D strings, 2D sheets, and 3D globular aggregates, were successfully reproduced. The ML potential reduced the computational cost of MD simulations by at least three orders of magnitude, allowing us to explore NP assembly at large lengths and time scales and thereby discover additional phases. This work provides a more accurate method for the behavior of materials in material composite processes, which is of great significance for material processing technology.

编辑概述

机器学习开发聚合物接枝自组装多体势

将纳米颗粒（NPs）掺入聚合物中是一种强有力的策略，可以提高惰性聚合物的热力学性能或引入新的属性（光学、电子、磁性或催化性能）。传统上NPs的表面与聚合物配体接枝，由于聚合物分子在NP中引入排斥力从而克服粒子间吸引力。新的研究发现聚合物接枝也可以用于指导NP组装和获得独特的介观形态，如一维弦和二维薄片。这些低维形态通常表现出与三维对应物（超晶格、球状聚集物）非常不同的功能特性。这种各向异性NP相的形成，来自于NPs之间的高体相互作用，即超过成对距离的粒子高阶排列对自由能的微扰修正。然而，在NP组装的模拟中捕获这种相互作用是非常具有挑战性的，这是因为聚合物移植物和熔体链模拟的计算成本非常高。近年来，机器学习（ML）技术已经成为一种有效的工具，以有效地近似多体相互作用的原子系统，并已被用于加速从头计算分子动力学（MD）模拟。在本工作中，来自杜克大学机械工程与材料科学系的Yilong Zhou等人，开发了一种独特的ML方法来发展分析多体势，它可以准确地描述聚合物基体中球形聚合物接枝NPs之间的二体和三体相互作用。该方法包括通过MD模拟计算聚合物基体中NPs的平均力势，并使用置换不变多项式作为粒子间距离库仑变换的函数进行拟合。为了验证所开发的ML势，作者使用它对进行组装的NPs进行MD模拟，并表明所有已知的结构阶段，即一维弦、二维片和三维球状聚集，都被成功复现。ML势将MD模拟的计算成本降低了至少三个数量级，这使大的长度和时间尺度上的NP组装成为可能，从而发现额外的相。本工作为材料复合工艺中材料的行为提供了更加准确的研究方案，对材料加工技术有重要意义。