Campos-Villalobos Gerardo, Giunta Giuliana, Marín-Aguilar Susana, Dijkstra Marjolein
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands.
J Chem Phys. 2022 Jul 14;157(2):024902. doi: 10.1063/5.0091319.
Spherically symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally efficient simulations of model systems consisting of colloidal particles with an anisotropic shape: mixtures of colloidal rods and non-adsorbing polymer coils, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer coils. We find good agreement with the results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well described by the orientation-dependent ML potentials.
原子环境的球对称原子中心描述符已被广泛用于构建原子和胶体系统的势能或自由能表面，并使用机器学习技术来表征局部结构。然而，当粒子形状为非球形时，如棒状和椭球状，仅标准的球对称结构函数会对局部环境产生不精确的描述。为了考虑取向的影响，我们为棒状粒子组成的系统引入了与二体和三体取向相关的粒子中心描述符。为了证明所提出函数的适用性，我们使用一种有效的特征选择方案和简单线性回归来构建粗粒度多体相互作用势，用于对由具有各向异性形状的胶体粒子组成的模型系统进行计算高效的模拟：胶体棒和非吸附性聚合物线圈的混合物、被弹性微凝胶壳包围的硬棒以及配体稳定的纳米棒。我们通过在直接共存模拟中使用基于取向相关对称函数的机器学习（ML）有效多体势来绘制胶体棒和非吸附性聚合物线圈的相行为，从而验证了这些势。我们发现与真实二元混合物模拟结果吻合良好，表明取向相关的ML势能够很好地描述有效相互作用。
