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Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials.

Aleksey Vishnyakov^1,2

¹Aramco Innovations LLC, 119234 Moscow, Russia.

Materials (Basel, Switzerland)

|February 13, 2025

Summary

Machine learning (ML) advances the characterization and design of disordered nanoporous materials. Despite data challenges, ML uncovers hidden correlations for optimizing material properties and production.

Keywords:

active carbons aerogels gas separation machine learning mesoporous oxides microporous polymers porous materials

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Area of Science:

Materials Science
Nanotechnology
Computational Chemistry

Background:

Disordered nanoporous materials are crucial for applications like gas separation.
Current data-driven approaches often focus on ordered materials, neglecting disordered ones.
Machine learning (ML) shows potential for analyzing complex, data-rich fields like disordered materials.

Purpose of the Study:

To review current data-driven methods for characterizing, designing, and optimizing disordered nanoporous materials.
To highlight the underutilization and potential of ML in this area.
To identify challenges and future directions for ML in disordered materials science.

Main Methods:

Review of existing literature on data-driven characterization and ML applications in porous materials.
Analysis of challenges in applying ML to disordered materials, focusing on data availability and feature interpretation.
Discussion of ML's role in uncovering non-linear correlations and optimizing material design.

Main Results:

ML is underutilized for disordered nanoporous materials compared to ordered ones.
Key challenges include navigating limited, non-transferable datasets and interpreting features.
ML demonstrates capability in discovering hidden correlations even with small datasets.

Conclusions:

ML offers significant potential for advancing disordered nanoporous materials research.
Future efforts should focus on building comprehensive databases and automated protocols.
Accessible language is crucial for bridging data science and chemistry domains.