Dipartimento di Fisica "Enrico Fermi", Università di Pisa, Largo B. Pontecorvo 3, I-56127 Pisa, Italy.
Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Pisa, Largo B. Pontecorvo 3, I-56127 Pisa, Italy.
Int J Mol Sci. 2022 Aug 18;23(16):9322. doi: 10.3390/ijms23169322.
Two neural networks (NN) are designed to predict the particle mobility of a molecular glassformer in a wide time window ranging from vibrational dynamics to structural relaxation. Both NNs are trained by information concerning the local structure of the environment surrounding a given particle. The only difference in the learning procedure is the inclusion (NN ) or not (NN ) of the information provided by the fast, vibrational dynamics and quantified by the local Debye-Waller factor. It is found that, for a given temperature, the prediction provided by the NN is more accurate, a finding which is tentatively ascribed to better account of the bond reorientation. Both NNs are found to exhibit impressive and rather comparable performance to predict the four-point susceptibility χ4(t) at τα, a measure of the dynamic heterogeneity of the system.
设计了两个神经网络 (NN) 来预测分子玻璃形成体在从振动动力学到结构弛豫的宽时间窗口中的颗粒迁移率。两个神经网络都是通过有关给定颗粒周围环境的局部结构的信息进行训练的。学习过程的唯一区别在于是否包含 (NN ) 或不包含 (NN ) 由快速、振动动力学提供的信息，并用局部德拜-沃勒因子量化。结果发现，对于给定的温度，NN 提供的预测更准确，这一发现暂时归因于更好地考虑了键的重取向。两个神经网络都被发现具有令人印象深刻且相当可比的性能，可预测 ta 处的四点灵敏度 χ4(t)，这是衡量系统动态异质性的一个指标。
