Building a Machine Learning Potential for MOF-303 Hydrogen Adsorption with Hybrid Sampling
An accepted paper in APS reports that a machine learning potential for hydrogen adsorption in MOF-303 was created using hybrid sampling and active-learning refinement, and applied to GCMC adsorption simulations. It states that energies and forces were matched accurately, and that temperature-dependent isotherms, heats of adsorption, diffusion coefficients, and identification of 8 adsorption sites were possible.