Two-dimensional interaction parameter histograms as a simple and versatile nanoporous material representation for machine learning prediction of adsorption properties

Tatiane Gercina de Vilas; Jair Fernando Fajardo-Rojas; Omar Mansurov; Ruby Devaisher; Eric Toberer; Diego Gomez-Gualdron

doi:10.1039/d6me00034g

January 1, 2026Molecular Systems Design & Engineering

Two-dimensional interaction parameter histograms as a simple and versatile nanoporous material representation for machine learning prediction of adsorption properties

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Key Points

This research aims to create a simple representation of nanoporous materials for improving machine learning predictions related to adsorption properties.
Developed two-dimensional interaction parameter histograms as a representation method.
Applied physics-based representations for nanoporous material screening.
Utilized machine learning algorithms to predict adsorption performance of materials.
Enhanced the accuracy of predictions for adsorption properties using the new material representation.
Demonstrated applicability across various types of nanoporous materials including metal-organic frameworks.

Implication

Demonstrates effective nanoporous material representation for ML adsorption predictions, suggesting broader applications.

Abstract

Machine-learning (ML) adsorption models are essential to computationally screen nanoporous materials, which are spearheaded by metal-organic frameworks (MOFs). Physics-based MOF representations offer advantages for the training of ML adsorption models...

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Cite This Study

Vilas et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc5b7dee9eb8c0dce70dd https://doi.org/10.1039/d6me00034g

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Two-dimensional interaction parameter histograms as a simple and versatile nanoporous material representation for machine learning prediction of adsorption properties

Key Points

Implication

Abstract

Discussion

Cite This Study

Two-dimensional interaction parameter histograms as a simple and versatile nanoporous material representation for machine learning prediction of adsorption properties

Key Points

Implication

Abstract

Discussion

Cite This Study

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