Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective

Discovering Ultra-Stable Metal-Organic Frameworks for CO2 Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation

The rapid increase in atmospheric CO2, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal-organic frameworks (MOFs) are promising adsorbents to potentially reduce CO2 emissions from flue gases. However, many MOFs suffer from structural degradation and performance deterioration upon exposure to water in flue gases. Aiming to discover stable and efficient MOFs for CO2 capture from a wet flue gas, we propose a hierarchical high-throughput computational screening (HTCS) strategy. Machine learning (ML)-assisted stability analysis is incorporated within the HTCS, leveraging prior experimental experience to predict ultrastable (including water-, thermal-, and activation-stable) MOFs from ∼280,000 candidates in the ab initio REPEAT charge MOF (ARC-MOF) database. Among 9755 shortlisted MOFs, molecular simulations identify 1000 top-performing MOFs. Remarkably, several vanadium-based MOFs are revealed to be ultrastable, exhibiting high CO2 capture capability of 3-7 mmol/g and CO2/N2 selectivity of 95-401. Subsequently, ML regressors are developed to derive design principles for MOFs capable of overcoming the trade-off effect. Furthermore, an ML classifier is developed to analyze the impact of water on CO2 capture by comparing dry and wet conditions. The proposed hierarchical HTCS and developed ML models lay a solid foundation for the potential transition of MOFs into practical applications.

Green Synthesis of Nano-Sized Multiflower-like Fe3O4@SiO2/L-Tryptophan from Natural Resources and Agricultural Waste: A Photo-Switchable Oxidation Catalyst

This study presents a novel, eco-friendly, and cost-effective magnetic hybrid photocatalyst, Fe3O4@SiO2/L-tryptophan, synthesized through a scalable three-step green approach using natural and agricultural waste. The Fe3O4@SiO2/L-tryptophan nanoparticle features a core-shell structure with a high surface area (63.14 m2/g), strong visible-light absorption (λ > 448 nm), a narrow band gap (1.84 eV), and superparamagnetic properties (22 emu/g), enabling efficient separation and reusability. Characterization techniques (XRD, XPS, FT-IR, FE-SEM, HR-TEM, UV-vis DRS, TGA, BET, and EIS) confirmed its structural stability, charge separation, and interfacial charge transport. The photocatalyst achieved 82.1% oxidative desulfurization of dibenzothiophene (DBT) and high conversion rates for toluene (85%) and styrene (90%) under visible light using O2 as an oxidant. It retained over 85% activity after five cycles, demonstrating excellent durability. For the first time, all components are derived from natural sources: Fe3O4 from sorghum seed extract, SiO2 from rice husk, and L-tryptophan for enhanced light absorption and charge separation. This sustainable synthesis reduces chemical waste and energy consumption, setting a new benchmark for environmentally friendly photocatalysts.

Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test. First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among ≈280 000 candidates, ≈130 000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications.

Selective detection of food contaminants using engineered gallium-organic frameworks with MD and metadynamics simulations

The exclusion mechanism of food contaminants such as bisphenol A (BPA), Flavonoids (FLA), and Goitrin (GOI) onto the novel gallium-metal organic framework (MOF) and functionalized MOF with oxalamide group (MOF-OX) is evaluated by utilizing molecular dynamics (MD) and Metadynamics simulations. The atoms in molecules (AIM) analysis detected different types of atomic interactions between contaminant molecules and substrates. To assess this procedure, a range of descriptors including interaction energies, root mean square displacement, radial distribution function (RDF), density, hydrogen bond count (HB), and contact numbers are examined across the simulation trajectories. The most important elements in the stability of the systems under examination are found to be stacking π-π and HB interactions. It was confirmed by a significant value of total interaction energy for BPA/MOF-OX (- 338.21 kJ mol-1) and BPA/MOF (- 389.95 kJ mol-1) complexes. Evaluation of interaction energies reveals that L-J interaction plays an essential role in the adsorption of food contaminants on the substrates. The free energy values for the stability systems of BPA/MOF and BPA/MOF-OX complexes at their global minima reached about BPA/MOF = - 254.29 kJ mol-1 and BPA/MOF-OX = - 187.62 kJ mol-1, respectively. Nevertheless, this work provides a new strategy for the preparation of a new hierarchical tree-dimensional of the Ga-MOF hybrid material for the adsorption and exclusion of food contaminates and their effect on human health.

Classifying and Predicting the Thermal Expansion Properties of Metal-Organic Frameworks: A Data-Driven Approach

Metal-organic frameworks (MOFs) are versatile materials for a wide variety of potential applications. Tunable thermal expansion properties promote the application of MOFs in thermally sensitive composite materials; however, they are currently available only in a handful of structures. Herein, we report the first data set for thermal expansion properties of 33,131 diverse MOFs generated from molecular simulations and subsequently develop machine learning (ML) models to (1) classify different thermal expansion behaviors and (2) predict volumetric thermal expansion coefficients (αV). The random forest model trained on hybrid descriptors combining geometric, chemical, and topological features exhibits the best performance among different ML models. Based on feature importance analysis, linker chemistry and topological arrangement are revealed to have a dominant impact on thermal expansion. Furthermore, we identify common building blocks in MOFs with exceptional thermal expansion properties. This data-driven study is the first of its kind, not only constructing a useful data set to facilitate future studies on this important topic but also providing design guidelines for advancing new MOFs with desired thermal expansion properties.

Role of Interface of Metal-Organic Frameworks and Their Composites in Persulfate-Based Advanced Oxidation Process for Water Purification

The persulfate activation-based advanced oxidation process (PS-AOP) is an important technology in wastewater purification. Using metal-organic frameworks (MOFs) as heterogeneous catalysts in the PS-AOP showed good application potential. Considering the intrinsic advantages and disadvantages of MOF materials, combining MOFs with other functional materials has also shown excellent PS activation performance and even achieves certain functional expansion. This Review introduces the classification of MOFs and MOF-based composites and the latest progress of their application in PS-AOP systems. The relevant activation/degradation mechanisms are summarized and discussed. Moreover, the importance of catalyst-related interfacial interaction for developing and optimizing advanced oxidation systems is emphasized. Then, the interference behavior of environmental parameters on the interfacial reaction is analyzed. Specifically, the initial solution pH and coexisting inorganic anions may hinder the interfacial reaction process via the consumption of reactive oxygen species, affecting the activation/degradation process. This Review aims to explore and summarize the interfacial mechanism of MOF-based catalysts in the activation of PS. Hopefully, it will inspire researchers to develop new AOP strategies with more application prospects.