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Hybrid sampling approach to machine-learning potentials for gas adsorption: Hydrogen adsorption in MOF-303

Kartik Sau1,*, Ikutaro Hamada2,†, Tamio Ikeshoji3, Yiming Lu1, Susmita Roy4, Shohichi Furukawa1, Linda Zhang1, Hung Ba Tran1, Takahiro Kondo1,4,5,6 et al.

Hao Li1 and Shin-ichi Orimo1,7,‡

  • *Contact author: kartik.sau@gmail.com
  • †Contact author: ihamada@prec.eng.osaka-u.ac.jp
  • ‡Contact author: shin-ichi.orimo.a6@tohoku.ac.jp
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Phys. Rev. Materials 10, 105401 – Published 5 October, 2026

DOI: https://doi.org/10.1103/d5b5-66b6

This article was published on 5 October, 2026. Please update your links.

Abstract

Porous materials such as metal-organic frameworks (MOFs) are widely studied for applications in catalysis and gas adsorption, including carbon dioxide capture and hydrogen storage for energy and environmental challenges. Accurate simulation of gas adsorption in these materials remains challenging because most approaches rely on force fields (FFs), which lack first-principles accuracy. Here, we present a hybrid sampling approach, complemented by an active-learning-style refinement step, for generating machine-learning potentials (MLPs) tailored for grand canonical Monte Carlo (GCMC) simulations of gas adsorption. The training dataset combines snapshots from FF-based GCMC sampling, evaluated by single-point density functional theory (DFT) calculations, with trajectories from DFT-based molecular dynamics (MD). We demonstrate this approach for hydrogen adsorption in MOF-303, achieving accurate energy and force evaluation, as well as robust GCMC simulation including on-the-fly quantum corrections for nuclei. The MLP-based calculation also revealed four primary and four secondary adsorption sites formed near N, O, C, and –OH motifs, highlighting the material's structural heterogeneity and adsorption behavior. The resulting MLPs enable calculation of adsorption isotherms, isosteric heat values, and diffusion coefficients across temperatures. This hybrid-sampling framework bridges DFT-level accuracy and larger-scale simulation capability than abinitio MD, and can in principle be applied to other porous materials and gas types to support the design of materials for gas storage and separation.

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Phys. Rev. Materials 10, 105401– Published 5 October, 2026
  • Received 15 January 2026
  • Accepted 19 August 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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