Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores

被引:0
|
作者
Desgranges, Caroline [1 ]
Delhommelle, Jerome [1 ,2 ,3 ,4 ]
机构
[1] Univ North Dakota, MetaSimulat Nonequilibrium Proc MSNEP Grp, Tech Accelerator, Grand Forks, ND 58202 USA
[2] Univ North Dakota, Dept Chem, Grand Forks, ND 58202 USA
[3] Univ North Dakota, Dept Biomed Engn, Grand Forks, ND 58202 USA
[4] Univ North Dakota, Sch Elect Engn & Comp Sci, Grand Forks, ND 58202 USA
基金
美国国家科学基金会;
关键词
capillary; phase transition; free energy; activated process; liquid bridges; bubbles; machine learning; METAL-ORGANIC FRAMEWORKS; MONTE-CARLO-SIMULATION; GAUGE-CELL METHOD; CYLINDRICAL PORES; PHASE-SEPARATION; ADSORPTION; NUCLEATION; TRANSITION; FLUID; EXPLORATION;
D O I
10.3390/e24010097
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores.
引用
收藏
页数:17
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