Inverse design of metal–organic frameworks for direct air capture of CO<sub>2</sub><i>via</i> deep reinforcement learning

Park, Hyunsoo; Majumdar, Sauradeep; Zhang, Xiaoqi; Kim, Jihan; Smit, Berend

doi:10.1039/d4dd00010b

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2024

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Cited by 12 publications

(1 citation statement)

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“…24–26 Since their discovery, interest in these materials has grown exponentially, with >100 000 structures currently reported in the literature. 27–29 Despite this, MOFs have multiple intrinsic challenges, including low mechanical stability, 30 non-sustainable synthesis conditions and problems surrounding the scalability of their synthetic procedures.…”

Section: Introductionmentioning

confidence: 99%

Synthetic and analytical considerations for the preparation of amorphous metal–organic frameworks

Shaw,

Chester,

Robertson

et al. 2024

Chem. Sci.

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Section: Introductionmentioning

confidence: 99%

Synthetic and analytical considerations for the preparation of amorphous metal–organic frameworks

Shaw,

Chester,

Robertson

et al. 2024

Chem. Sci.

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Optimizing the prediction of adsorption in metal–organic frameworks leveraging Q‐learning

Osaro,

Colón

2024

AIChE Journal

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The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal–organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML has helped survey the vast material space. This study explores the integration of reinforcement learning (RL), specifically Q‐learning, within an active learning (AL) context, combined with Gaussian processes (GPs) for predictive modeling of adsorption in MOFs. We demonstrate the effectiveness of the RL‐driven framework in guiding the selection of training data points and optimizing predictive model performance for methane and carbon dioxide adsorption, using two different reward metrics. Our results highlight the integration of RL as an AL method for adsorption predictions in MFs, and how it compares to a previously implemented AL scheme.

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Leveraging experimental and computational tools for advancing carbon capture adsorbents research

Ramasamy,

Raj,

Akula

et al. 2024

Environ Sci Pollut Res

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Inverse design of metal–organic frameworks for direct air capture of CO₂via deep reinforcement learning

Abstract: The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery,...

Cited by 12 publications

References 81 publications

Synthetic and analytical considerations for the preparation of amorphous metal–organic frameworks

Synthetic and analytical considerations for the preparation of amorphous metal–organic frameworks

Optimizing the prediction of adsorption in metal–organic frameworks leveraging Q‐learning

Leveraging experimental and computational tools for advancing carbon capture adsorbents research

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Inverse design of metal–organic frameworks for direct air capture of CO2via deep reinforcement learning

Abstract: The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery,...

Cited by 12 publications

References 81 publications

Synthetic and analytical considerations for the preparation of amorphous metal–organic frameworks

Synthetic and analytical considerations for the preparation of amorphous metal–organic frameworks

Optimizing the prediction of adsorption in metal–organic frameworks leveraging Q‐learning

Leveraging experimental and computational tools for advancing carbon capture adsorbents research

Contact Info

Product

Resources

About

Inverse design of metal–organic frameworks for direct air capture of CO₂via deep reinforcement learning