标题： 用于模拟纳米硅酸盐团簇红外光谱的机器学习势能 显示英文标题

标题： A machine learning potential for simulating infrared spectra of nanosilicate clusters

摘要： 机器学习（ML）在化学物理中的应用使得构建具有从头算方法精度且计算成本与经典力场相当的原子间势成为可能。训练一个ML模型需要一种高效的训练数据生成方法。在这里，我们应用了一种准确且高效的方法来收集用于构建基于神经网络的ML原子间势的训练数据，针对纳米硅酸盐团簇进行了研究。初始训练数据来自正常模式和最远点采样。随后，通过主动学习策略扩展了训练数据集，在该策略中，新的数据由一组ML模型之间的分歧来识别。整个过程通过结构上的并行采样进一步加速。我们使用该ML模型运行不同尺寸的纳米硅酸盐团簇的分子动力学（MD）模拟，从中可以提取包含非谐性的红外光谱。此类光谱数据对于理解星际介质（ISM）和恒星周围环境中的硅酸盐尘埃颗粒的性质至关重要。 显示英文摘要

摘要： The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here we apply an accurate and efficient protocol to collect training data for constructing a neural network based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics (MD) simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium (ISM) and in circumstellar environments.