Active learning of alchemical adsorption simulations; towards a universal adsorption model.

ABSTRACT: Adsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource intensive, limiting the identification of promising materials. Active learning (AL) methods offer an effective strategy to reduce the computational burden by selectively acquiring critical data for model training. Metal-organic frameworks (MOFs) exhibit immense potential across various adsorption applications due to their porous structure and their modular nature, leading to diverse pore sizes and chemistry that serve as an ideal platform to develop adsorption models. Here, we d

SUBMITTER: Osaro E 

PROVIDER: S-EPMC11459438 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature