WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models

Published: April 24, 2024

Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity air environment. Consequently, it is useful to predict whether MOF water-stable before investing time resources into synthesis. Existing heuristics for designing MOFs generality artificially limit diversity explored chemistry due narrowly defined criteria. Machine learning (ML) models offer promise improve predictions require diverse experimental data be trained. In an improvement on previous efforts, we enlarge available training prediction by over 400%, adding 911 labels assigned through semi-automated manuscript analysis curate new set WS24. The additional shown ML model performance (test ROC-AUC > 0.8) both harsher acidic conditions. We illustrate how expanded can used previously developed activation carry out genetic algorithms quickly screen ~10,000 from space hundreds thousands candidates multivariate (i.e., activation, water, acid). Model algorithm results uncover metal- geometry-specific design rules robust MOFs. this work, which disseminate easy-to-use web interface, expected contribute toward accelerated discovery novel, such as direct capture treatment.

Language: Английский

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Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes

Machine Learning Science and Technology, Journal Year: 2024, Volume and Issue: 5(4), P. 045080 - 045080

Published: Dec. 1, 2024

Abstract Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based do not include any 3D information and are unable distinguish stereoisomers such as those arising from different orderings ligands around a metal center coordination complexes. In this work we present modification revised autocorrelation descriptors, molecular graph featurization method, predicting spin state dependent octahedral transition complexes (TMCs). Inspired by analytical semi-empirical TMCs, the new modeling strategy is based on many-body expansion (MBE) allows one tune captured stereoisomer changing truncation order MBE. We necessary modifications approach two commonly used ML methods, kernel ridge regression feed-forward neural networks. On test set composed all possible isomers binary best MBE achieve mean absolute errors (MAEs) 2.75 kcal mol −1 spin-splitting energies 0.26 eV frontier orbital energy gaps, 30%–40% reduction error compared our previous approach. also observe improved generalization previously unseen where best-performing exhibit MAEs 4.00 (i.e. 0.73 reduction) 0.53 0.10 gaps. Because incorporates insights electronic structure theory, ligand additivity relationships, these systematic homoleptic heteroleptic complexes, allowing efficient TMC search

Language: Английский

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Covalent integration of polymers and porous organic frameworks

Frontiers in Chemistry, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 18, 2024

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic (MOFs), covalent (COFs) hydrogen-bonded (HOFs), represent a promising strategy for overcoming the existing limitations traditional materials. This allows combination advantages polymers, i.e., flexibility, processability chemical versatility etc., superiority POFs, like structural integrity, tunable porosity high surface area, creating type hybrid These resulting polymer-POF materials exhibit enhanced mechanical strength, stability functional diversity, thus opening up new opportunities applications across large variety fields, such as gas separation, catalysis, biomedical applications, environmental remediation energy storage. In this review, an overview synthetic routes strategies on how to covalently integrate different with various POFs is discussed, especially particular focus methods polymerization within, among POF structures. To investigate unique properties functions these resultant materials, characterization techniques, nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) scanning (SEM), adsorption (BET) computational modeling machine learning, are also presented. The ability polymer-POFs manipulate pore environments at molecular level affords wide range providing versatile platform future advancements in material science. Looking forward, fully realize potential authors highlight scalability, green synthesis methods, stimuli-responsive critical areas research.

Language: Английский

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Assessment of Long-Term Degradation of Adsorbents for Direct Air Capture by Ozonolysis

The Journal of Physical Chemistry C, Journal Year: 2024, Volume and Issue: 129(1), P. 899 - 909

Published: Dec. 20, 2024

Porous adsorbents are a promising class of materials for the direct air capture CO2 (DAC). Practical implementation adsorption-based DAC requires that can be used thousands adsorption–desorption cycles without significant degradation. We examined potential degradation by mechanism appears to have not been considered previously, namely, ozonolysis trace levels ozone from ambient air. focused on amine-appended metal–organic frameworks, specifically amine-functionalized Mg2(dobpdc), as representative adsorbent. Estimates based number amine sites in these and concentration suggest may relevant over if reactions with adsorbed fast. density functional theory calculations estimate reaction rates groups carbon–carbon double bonds Mg2(dobpdc).

Language: Английский

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Integrating crystallographic and computational approaches to carbon-capture materials for the mitigation of climate change

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(38), P. 25678 - 25695

Published: Jan. 1, 2024

This article presents a perspective on the state of art in structure determination microporous carbon-capture materials and paths toward future progress this field, as discussed NIST workshop same title.

Language: Английский

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