Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

Abstract

The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.

Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights

“…A procedure to reduce the cost of large-scale screening studies of adsorbent materials such as MOFs is to prescreen structures using properties that are fairly inexpensive to calculate. 20,[54][55][56][57] These include geometric features, such as LCDs, pore volume, fractional free volume (FFV), and accessible surface areas, as well as energetic features such as heats of adsorption and Henry's constants at the infinite dilution condition. While these can be useful in informing behavior of adsorbent materials in general, the complex mechanisms through which water adsorption occurs raise a question as to whether these features can be predictive for adsorption properties and help identify interesting materials.…”

“…Indeed, these have been commonly employed in the prescreening of materials as reported in numerous studies, mainly concerning molecules which are simpler to simulate such as CH 4 and CO 2 . 20,[54][55][56][57] However, insights into their predictive abilities for water adsorption behavior remain rather limited to date.…”

“…Thus, utilizing simple geometric properties such as largest cavity diameter (LCD), pore volume, void fraction, accessible surface area as well as energetic parameters such as heats of adsorption and Henry's constants to aid the selection of MOFs can lower computational costs. Indeed, these have been commonly employed in the prescreening of materials as reported in numerous studies, mainly concerning molecules which are simpler to simulate such as CH 4 and CO 2 20,54–57 . However, insights into their predictive abilities for water adsorption behavior remain rather limited to date.…”

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

“…For that purpose, the CO 2 capture potential of 6013 CoRE MOFs from air was predicted using four different ML algorithms (back-propagation neural network (BPNN), DT, RF, and SVM) with structural descriptors such as pore sizes, volumetric surface area, and heat of adsorption ( Q st ). 72 To train the models, a maximum of 1000 MOFs were used while the remaining MOFs were used for the validation of the ML models. The ML model built with RF algorithm was reported to have the best prediction performance of CO 2 adsorption selectivity ( R 2 = 0.98) for the validation set and the relative importance analysis revealed that Q st is the most important parameter to predict adsorption selectivity for CO 2 .…”

Off-Design Operation of Conventional and Phase-Change CO2 Capture Solvents and Mixtures: A Systematic Assessment Approach

“…The development of efficient CO 2 abatement systems is widely pursued as a means of mitigating the detrimental effects of global warming [1]. Currently, intense research efforts are focusing on CO 2 capture technologies, such as membrane processes [2], adsorption systems [3], and materials- [4,5] and solvent-based absorption/desorption processes [6]. The latter represents a technology with significant potential, which has been demonstrated even in large-scale plants [7,8].…”