Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning

25 April 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.


reinforcement learning
inverse design
metal-organic frameworks
direct air capture
carbon capture

Supplementary materials

Supplementary Information
Supplementary Figure S1-8, Supplementary Note S1

Supplementary weblinks


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