Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.
Virtual Technologies and Engineering, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.
J Phys Chem B. 2022 Jun 23;126(24):4555-4564. doi: 10.1021/acs.jpcb.2c01723. Epub 2022 Jun 8.
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.
人工神经网络 (ANNs) 的开发目的是准确预测二元流体混合物中各组分的自扩散常数。该 ANNs 在包含 75 种独特化合物的 131 种混合物的 4328 个自扩散常数实验数据库上进行了测试。强氢键分子的存在可能导致聚类或二聚化，从而导致非线性扩散行为。为了解决这个问题，为每个分子和混合物计算了自缔合能和二元缔合能，以提供关于分子间相互作用强度的信息，并将其用作 ANN 的输入特征。开发了一个准确的、通用的 ANN 模型，整体平均绝对偏差为 4.1%。正向输入特征选择揭示了关键性质和自缔合能以及其他流体性质的重要性。使用完整输入特征集的子集开发了其他 ANNs，以进一步研究各种性质对模型性能的影响。对两个特定混合物的结果进行了更详细的讨论：一个提供了强氢键的示例，另一个提供了极端压力变化的示例，ANN 模型在这两种情况下都很好地预测了自扩散。
