A Mechanism-Guided Inverse Engineering Framework to Unlock Design Principles of H-Bonded Organic Frameworks for Gas Separation

TL;DR

This paper introduces a data-driven inverse engineering framework using a large HOF database and machine learning to understand and optimize hydrogen-bonded frameworks for efficient gas separation, especially Xe/Kr.

Contribution

It presents the first large-scale HOF database and a novel inverse design approach that enhances interpretability and guides the rational design of nanoporous materials for gas separation.

Findings

Identified key hydrogen bonding features influencing separation performance

Achieved high Xe/Kr selectivity (>10^3) through optimized HOF structures

Demonstrated the effectiveness of machine learning in inverse material design

Abstract

The diverse combinations of novel building blocks offer a vast design space for hydrogen-boned frameworks (HOFs), rendering it a great promise for gas separation and purification. However, the underlying separation mechanism facilitated by their unique hydrogen-bond networks has not yet been fully understood. In this work, a comprehensive understanding of the separation mechanisms was achieved through an iterative data-driven inverse engineering approach established upon a hypothetical HOF database possessing nearly 110,000 structures created by a material genomics method. Leveraging a simple yet universal feature extracted from hydrogen bonding information with unambiguous physical meanings, the entire design space was exploited to rapidly identify the optimization route towards novel HOF structures with superior Xe/Kr separation performance (selectivity >103). This work not only provides the first large-scale HOF database, but also demonstrates the enhanced machine learning interpretability of our model-driven iterative inverse design framework, offering new insights into the rational design of nanoporous materials for gas separation.