Responses to the comments on “Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights”

Datar, Archit; Witman, Matthew; Lin, Li‐Chiang

doi:10.1002/aic.17684

Cited by 3 publications

(5 citation statements)

References 4 publications

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“…By further assuming that MOFs studied herein all feature a perfect step isotherm such that a C -ratio of >1 or <1 respectively predicts that the MOF possesses a zero uptake or saturation loading. To mimic the environment of arid climates, the adsorption condition is set to a relative humidity (RH) of 20% at 298 K, and the desorption condition is set to 15% RH at 338 K. , Following the recommendation by Datar et al., , the saturation pressure in this study has been set to be 986 Pa at 298 K, the saturation pressure computed per the adopted TIP4P-Ew model. At 338 K, a pressure of 9765 Pa is used, which is determined through the Antoine equation, with parameters provided by Vega et al The result of the equation at 298 K closely matches the value obtained by Datar et al It has been shown that it is critical to use the saturation pressure of the adopted model; with this, simulations of different water models can still yield comparable results.…”

Section: Methodsmentioning

confidence: 99%

“…Such methods biasedly sample all accessible loading macrostates, and their relative probability is then computed. As demonstrated by Datar et al, a particular approach of flat histogram methodsthe so-called NVT+W method developed by Witman et alcan overcome the drawback of the GCMC method to effectively compute water adsorption in MOFs. ,, This method is, however, computationally expensive to study a large number of material candidates, as it requires hundreds of canonical simulations for each structure. To this end, an approach denoted as the C-map method with a much-reduced calculation cost was further proposed by Datar et al The key idea behind this method is to sample adsorption systems only at a few selected loading macrostates to determine their range of adsorption loadings at a given condition.…”

Section: Introductionmentioning

confidence: 99%

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In Silico Screening of Metal–organic Frameworks for Water Harvesting

Wang,

Datar,

et al. 2024

J. Phys. Chem. C

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The increasingly limited availability of fresh water has become one of the most prominent challenges of our time, and therefore, producing clean water from unconventional sources is of urgent importance. Water harvesting, a process that utilizes changes in the pressure and temperature to capture atmospheric water, has recently drawn considerable attention. In this study, by employing state-of-the-art Monte Carlo simulations, a large-scale study of ∼12,000 metal–organic frameworks (MOFs) included in the Computational-Ready Experimental (CoRE) MOF database is conducted for their potential in water harvesting. The outcomes herein identify hundreds of promising adsorbents that can deliver a greater amount of fresh water per adsorption–desorption cycle than current state-of-the-art adsorbents. Analyses on such large amounts of computational data have also shed light on the structure–property relationships. Overall, the results obtained herein offer significant insights into the future development of MOFs as water adsorbents to harvest atmospheric water.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In Silico Screening of Metal–organic Frameworks for Water Harvesting

Wang,

Datar,

et al. 2024

J. Phys. Chem. C

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“…Subsequently, with the C-matrix and the detailed balance between macrostates, the MPD can be calculated, followed by its reweighting to determine adsorption uptakes at desired conditions (i.e., at varying pressures and temperatures, resulting in adsorption isotherms at different temperatures). This method has been demonstrated to compute pure component adsorption for porous materials, including those structures with internal flexibility and the adsorption of molecules with bimodal MPD distributions such as water as noted above. − …”

Section: Methodsmentioning

confidence: 99%

“…To obtain the MPD, the NVT+W (canonical plus Widom) method first reported by Smit and co-workers, , a special class of the flat histogram methods that resembles the transition-matrix Monte Carlo (TMMC) , simulations with a bin size of 1, can be employed. This approach has been demonstrated to compute pure component adsorption isotherms for nanoporous materials, including challenging molecules such as water in MOFs. − This approach uniformly samples each macrostate; that is, it involves calculations for each possible molecular number N . During the canonical simulation for each N , ghost Widom insertion and deletion moves, which will never be accepted are carried out to sample the transition probability from one macrostate to another macrostate (i.e., N to N + 1 or N – 1, respectively).…”

Section: Introductionmentioning

confidence: 99%

Computing Mixture Adsorption in Porous Materials through Flat Histogram Monte Carlo Methods

Chen,

Lin

2023

Langmuir

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Mixture adsorption properties of porous materials are critical to determine their potential as adsorbents in separation applications. Toward the discovery of optimal adsorbents, in silico screening studies typically employ the grand canonical Monte Carlo (GCMC) technique to compute adsorption properties of gas mixtures in materials of interest at a given condition (i.e., composition, total pressure, and temperature) or to compute their adsorption properties for each component, followed by utilizing methods to predict mixture adsorption isotherms. However, the former approach results in the need for repeated calculations when different conditions such as compositions are considered. For the latter, the predictions may involve uncertainties, sometimes originating from the fitting quality to the pure component isotherms, and repeated simulations may also be needed for different temperatures. To this end, this study demonstrates the potential of flat histogram Monte Carlo methods in addressing the abovementioned shortfalls. Specifically, the so-called NVT + W method, first reported by Smit and co-workers, is extended herein to determine the macrostate probability distribution (MPD) of binary mixtures in porous materials. The obtained MPD can be reweighted to any conditions, yielding accurate adsorption isotherms of any desired compositions and temperatures. This approach, denoted as 2D NVT + W, is also compared with the widely adopted ideal adsorbed solution theory (IAST) method, and the former is found to offer more reliable predictions. Overall, the 2D NVT + W approach represents an efficient and effective alternative to compute mixture adsorption isotherms for porous materials, and the obtained MPD can be conveniently reused by peer researchers. A user-friendly Python code is also provided along with this article to employ this method.

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“…Specifically, the so-called NVT+W approach is adopted, which has been recently applied to study water adsorption in MOFs. The readers are referred to recent studies by Datar et al ,, However, the application of NVT+W requires sampling each possible macrostate for a structure of interest to determine its adsorption properties (i.e., adsorption isotherms), leading to a huge computational demand. To this end, in this study, we attempt to classify only each studied MOF in a binary manner to be hydrophilic or hydrophobic under the adsorption condition.…”

Section: Methodsmentioning

confidence: 99%

Connectivity Analysis of Adsorption Sites in Metal–Organic Frameworks for Facilitated Water Adsorption

Xu,

Wang,

Lin

2023

ACS Appl. Mater. Interfaces

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Metal–organic frameworks (MOFs) have recently drawn considerable attention as promising adsorbents to harvest atmospheric water. To achieve an efficient harvesting process, seeking MOFs that demonstrate sharp condensation behavior is the key. Given that the clustering of water molecules in MOFs should be driven by not only MOF–water interactions but also water–water interactions, the spatial arrangement of water adsorption sites in a MOF is therefore crucial. Specifically, this study demonstrates the critical role of continuous adsorption channels (CACs) in MOFs. Such CACs will enable water molecules to stay in proximity and in a continuous manner, thus promoting the formation of hydrogen bonds and, consequently, the clustering of water molecules. We have developed an automatic algorithm to detect CACs based on the energy grid of host–guest interactions and applied the algorithm to more than 2000 diverse structures. The results show that more than 80% of the studied MOFs displaying water condensation at 298 K and 20% relative humidity predicted by Monte Carlo simulations indeed have CACs. The developments herein are anticipated to largely facilitate the future discovery of optimal adsorbents for water harvesting or water-adsorption-related applications in general. A Python-based code for detecting CACs in porous materials is also provided along with this article to employ this approach.

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Responses to the comments on “Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights”

Cited by 3 publications

References 4 publications

In Silico Screening of Metal–organic Frameworks for Water Harvesting

In Silico Screening of Metal–organic Frameworks for Water Harvesting

Computing Mixture Adsorption in Porous Materials through Flat Histogram Monte Carlo Methods

Connectivity Analysis of Adsorption Sites in Metal–Organic Frameworks for Facilitated Water Adsorption

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