ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, USA.
Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA.
J Chem Phys. 2022 Jul 7;157(1):014503. doi: 10.1063/5.0093658.
Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.
基于多基因遗传程序的符号回归（SR）已被用于阐明新的经验公式，以描述莱纳德-琼斯（LJ）流体中的扩散。例如，这些公式可以预测纯 LJ 流体中的自扩散，以及描述二元 LJ 流体中自扩散的有限尺寸修正。SR 获得的方程的性能与文献中现有的经验方程以及最近报告的人工神经网络（ANN）模型的结果进行了比较。结果发现，与现有的经验方程相比，SR 方程具有更好的预测性能，即使使用的可调参数数量较少，但与更广泛的神经网络相比，整体性能有所下降。
