Accelerated Discovery of Metal–Organic Frameworks for CO₂ Capture by Artificial Intelligence

Abstract

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO 2 ) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal−organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO 2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO 2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO 2 capture.

Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF4 from CH4 and N2

“…It should be noted that having failed synthesis experiments for a particular MOF under various different synthesis conditions does not necessarily mean that the synthesis would be infeasible. In fact, some of the problems encountered in MOF synthesis could be solved through the use of artificial intelligence-driven techniques . For instance, it was recently shown that using a genetic algorithm optimization procedure, synthesis conditions that can lead to efficient and/or high-quality synthesis of HKUST-1 and Al-PMOF can be obtained.…”

Recent Progress and Challenges in the Field of Metal–Organic Framework-Based Membranes for Gas Separation

“…Many computational studies on MOF-based membranes serve as a starting point for listing promising membranes, though they rely on several assumptions [206][207][208][209][210][211][212][213]. Many studies use rigid frameworks to predict gas separation performance.…”

Balancing computational chemistry's potential with its environmental impact

“…Machine learning was successfully employed for exploring sustainability-focused reactions and design processes, such as the design of electrocatalysts for the CO 2 reduction, 54–56 the design of metal–organic frameworks to capture CO 2 , 2,57–59 helping to find greener solvent alternatives 60,61 and the synthesis methanol from CO 2 . 62…”

“…The success of such methods has already extensively showcased their potential, 1–3 also directly tackling CO 2 conversion 4–8 and storage. However, even when directly addressing climate change-related issues, scientists often overlook the environmental consequences of their direct actions; their research efforts have an associated environmental cost.…”