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Abstract Global reliance on fossil fuel combustion for energy production has contributed to the rising concentration of atmospheric CO2, creating significant global climate challenges. In this regard, direct air capture (DAC) of CO2from the atmosphere has emerged as one of the most promising strategies to counteract the harmful effects on the environment, and the further development and commercialization of this technology will play a pivotal role in achieving the goal of net‐zero emissions by 2050. Among various DAC adsorbents, metal–organic frameworks (MOFs) show great potential due to their high porosity and ability to reversibly adsorb CO2at low concentrations. However, the adsorption efficiency and cost‐effectiveness of these materials must be improved to be widely deployed as DAC sorbents. To that end, this perspective provides a critical discussion on several types of benchmark MOFs that have demonstrated high CO2capture capacities, including an assessment of their stability, CO2capture mechanism, capture‐release cycling behavior, and scale‐up synthesis. It then concludes by highlighting limitations that must be addressed for these MOFs to go from the research laboratory to implementation in DAC devices on a global scale so they can effectively mitigate climate change.
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A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
Abstract Metal-organic frameworks (MOFs) exhibit great promise for CO2capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2adsorption capacities. We present the top six AI-generated MOFs with CO2capacities greater than 2m mol g−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.
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- Award ID(s):
- 2209892
- PAR ID:
- 10490845
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Chemistry
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2399-3669
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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