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

Applied Sciences

Shuhua Li¹  Zhiwei Qiao¹  Xiaomei Deng¹  Zenan Shi¹  Hong Liang¹  Wenyuan Yang¹ 

[1] Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China;

关键词: co₂ capture;    monte carlo;    machine learning;    metal–organic framework;    adsorption;    diffusion;   

DOI  :  10.3390/app10020569

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown