Compared to conventional computational screening studies that are limited by the size of database, inverse design has a great potential to facilitate identifying new materials with optimal properties. In this work, we integrate machine learning with genetic algorithm to computationally design metal−organic frameworks (MOFs) for hydrogen storage applications at cryogenic conditions. As such, we identified 6277 MOFs that exceed the current record (37.2 g/L of NPF-200) at operating conditions between 5 and 100 bar at 77 K. MOFs, whose working capacities exceed 40.0 g/L (systembased 2025 DOE target) were also identified, where the highest working capacity obtained from this work was 41.6 g/L, which is higher than any other hypothetical MOFs reported thus far. Furthermore, synthesizability of the top performing structures was assessed by comparing relative stability with their polymorphic structures while taking into account the possibility of interpenetration. We demonstrate that our methodology can successfully design MOFs with both high hydrogen capacity and synthesizability and we anticipate our workflow can be widely applied to various other materials and applications.
Generating optimal nanomaterials using artificial neural networks can potentially lead to a significant revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. In this work, we have for the first time implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 14 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design in porous materials.
Generating optimal nanomaterials using artificial neural networks can potentially lead to a significant revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. In this work, we have for the first time implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 14 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design in porous materials.
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