Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning

Abstract

Summary Adsorption-driven osmotic heat engines offer an alternative way for harvesting low-grade waste heat below 80°C. In this study, we performed a high-throughput computational screening based on grand canonical Monte Carlo simulations to identify the high-performance metal-organic frameworks (MOFs) from 1322 computationally ready experimental MOF structures for adsorption-driven osmotic heat engines with LiCl-methanol as the working fluid. Structure-property relationship analysis reveals that MOFs exhibiting high energy efficiency possess large working capacity, pore size and surface area, and moderate adsorption enthalpy comparable to the evaporation enthalpy. Furthermore, machine learning is employed to accelerate the computational screening for satisfied MOFs via the structure properties. The optimal structure properties of the MOFs are further identified via the ensemble-based regression model by optimizing the energy efficiency via the genetic algorithm, which shed light on rationally designing and fabricating MOFs for desired heat-to-electricity conversion.

Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

“…Five structural descriptors including the largest cavity diameter (LCD), pore-limiting diameter (PLD), volumetric surface area (VSA), void fraction ( ϕ ), density ( ρ ), and an energy descriptor of heat of adsorption ( Q st ), were used to quantitatively describe the structure of the MOF. The reasons for the selection of these six descriptors are as follows: (1) they possessed the strong structure-performance relationships between gas and MOFs, confirmed by many previous works [ 28 , 32 , 34 , 35 ] of high-throughput calculation of MOFs, which means that these six MOF descriptors have a greater possibility of achieving the accuracy prediction in ML models than the thousands of other descriptors that could have been used; (2) these six descriptors could be applied in accuracy prediction of ML, which coincide with many ML works [ 36 , 37 , 38 , 39 ]; (3) these descriptors are relatively easy to measure in the experiment, and they can be used directly to guide the synthesis and application of MOF. The LCD and PLD in each CoRE-MOF were estimated using Zeo++ [ 40 ].…”

Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics

“…It includes the hydrophobic, hydrophilic, and total solvent accessible surface area of ​​the protein molecule. The calculated surface area is the canonical surface area 94 . The extent to which amino acids interact with the solvent and the protein core is proportional to the surface area exposed to the solvent.…”

Quantitative Structure–Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels

“…The PSD provides information about the fraction of void space that is occupied by pores of a certain size, and the Q st value reflects the energy information related to the adsorption process. These diverse descriptors with strong structure–performance relationships between guest molecules and zeolites reveal the features of zeolites from various aspects, which could be applied in accuracy prediction of machine learning. ,,, These descriptors are relatively easy to measure, and they can be used directly to guide the synthesis and application of zeolites. In this work, ρ was obtained directly through zeolite crystalline structure, and LCD, PLD, ASA, AV, and PSD were determined by the Zeo++ program, in which the radius of a probe (1.2 Å) was used for ASA and AV, and a bin size of 0.1 Å was used to obtain PSD histograms.…”