论文标题
具有对称性的振动和电子光谱对振动和电子光谱的有效仿真，具有对称性的神经网络模型
Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
论文作者
论文摘要
机器学习彻底改变了分子特性（例如势能）的高维表示。但是，有一些稀缺的机器学习模型针对的是旋转协变的张力特性。在这里，我们提出了张力神经网络（NN）模型，以学习张力响应和过渡性能，其中原子坐标向量将乘积与标量NN输出或其衍生物乘以保持旋转协方差对称性。该策略使结构描述符对称不变，以使所得的张力NN模型与标量对应物一样有效。我们通过学习水低聚物和液体水的响应特性以及模型的蛋白质结构单位的过渡偶极矩来验证这种方法的性能和普遍性。机器学习的紧张模型已实现了对逼真的蛋白质的液体水和紫外光谱的有效模拟，有望对生物分子和材料进行可行和准确的光谱模拟。
Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties, in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water, and transition dipole moment of a model structural unit of proteins. Machine learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.
