Sun Yangzesheng, DeJaco Robert F, Siepmann J Ilja
Department of Chemistry and Chemical Theory Center , University of Minnesota , 207 Pleasant Street SE , Minneapolis , Minnesota 55455-0431 , USA . Email:
Department of Chemical Engineering and Materials Science , University of Minnesota , 412 Washington Avenue SE , Minneapolis , Minnesota 55455-0132 , USA.
Chem Sci. 2019 Mar 18;10(16):4377-4388. doi: 10.1039/c8sc05340e. eCollection 2019 Apr 28.
We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical ( ) Gibbs ensemble Monte Carlo simulations were performed to model a single-stage equilibrium desorptive drying process for (1,4-butanediol or 1,5-pentanediol)/water and 1,5-pentanediol/ethanol from all-silica MFI zeolite and 1,5-pentanediol/water from all-silica LTA zeolite. A multi-task deep NN was trained on the simulation data to predict equilibrium loadings as a function of thermodynamic state variables. The NN accurately reproduces simulation results and is able to obtain a continuous isotherm function. Its predictions can be therefore utilized to facilitate optimization of desorption conditions, which requires a laborious iterative search if undertaken by simulation alone. Furthermore, it learns information about the binary sorption equilibria as hidden layer representations. This allows for application of transfer learning with limited data by fine-tuning a pretrained NN for a different alkanediol/solvent/zeolite system.
我们采用深度神经网络（NNs）作为一种高效且智能的替代方法，通过概率建模对复杂吸附平衡进行分子模拟。进行了正则（ ）吉布斯系综蒙特卡罗模拟，以模拟全硅MFI沸石中（1,4 - 丁二醇或1,5 - 戊二醇）/水和1,5 - 戊二醇/乙醇的单级平衡解吸干燥过程，以及全硅LTA沸石中1,5 - 戊二醇/水的单级平衡解吸干燥过程。在模拟数据上训练了一个多任务深度神经网络，以预测作为热力学状态变量函数的平衡负载量。该神经网络能准确再现模拟结果，并能够获得连续的等温线函数。因此，其预测结果可用于促进解吸条件的优化，若仅通过模拟进行优化，则需要进行费力的迭代搜索。此外，它将有关二元吸附平衡的信息作为隐藏层表示进行学习。这使得通过对预训练的神经网络针对不同的链烷二醇/溶剂/沸石系统进行微调，能够在有限数据的情况下应用迁移学习。
