Department of Physics , King's College London , Strand , London WC2R 2LS , United Kingdom.
Department of Chemical Engineering , University College London , Torrington Place , London WC1E 7JE , United Kingdom.
J Chem Inf Model. 2019 May 28;59(5):2141-2149. doi: 10.1021/acs.jcim.9b00005. Epub 2019 Mar 27.
Computer simulation studies of multiphase systems rely on the accurate identification of local molecular structures and arrangements in order to extract useful insights. Local order parameters, such as Steinhardt parameters, are widely used for this identification task; however, the parameters are often tailored to specific local structural geometries and generalize poorly to new structures and distorted or undercoordinated bonding environments. Motivated by the desire to simplify the process and improve the accuracy, we introduce DeepIce, a novel deep neural network designed to identify ice and water molecules, which can be generalized to new structures where multiple bonding environments are present. DeepIce demonstrates that the characteristics of a crystalline or liquid molecule can be classified using as input simply the Cartesian coordinates of the nearest neighbors without compromising the accuracy. The network is flexible and capable of inferring rotational invariance and produces a high predictive accuracy compared to the Steinhardt approach, the tetrahedral order parameter and polyhedral template matching in the detection of the phase of molecules in premelted ice surfaces.
多相系统的计算机模拟研究依赖于准确识别局部分子结构和排列，以提取有用的见解。局部顺序参数，如Steinhardt 参数，广泛用于此识别任务；然而，这些参数通常针对特定的局部结构几何形状进行定制，并且对新结构和扭曲或配位不足的键合环境概括得很差。受简化该过程和提高准确性的愿望的推动，我们引入了 DeepIce，这是一种新的深度神经网络，旨在识别冰和水分子，可以推广到存在多种键合环境的新结构。DeepIce 表明，使用简单的近邻笛卡尔坐标作为输入，可以对晶体或液体分子的特征进行分类，而不会降低准确性。该网络具有灵活性，能够推断旋转不变性，并在检测预熔化冰表面分子的相时，与 Steinhardt 方法、四面体顺序参数和多面体模板匹配相比，产生了较高的预测准确性。
