Predicting hydrogen storage in MOFs via machine learning

Bayesian Optimization for Efficient Prediction of Gas Uptake in Nanoporous Materials

The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs) are crucial for addressing global challenges like climate change, energy security, and environmental degradation. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the complex design spaces of NPMs for gas storage applications. For a MOF dataset drawn from 19 different sources, we present a quantitative evaluation of BO using a curated set of surrogate model and acquisition function couples. In our study, we employed machine learning (ML) techniques to conduct regression analysis on many models. Following this, we identified the three ML models that exhibited the highest accuracy, which were subsequently chosen as surrogates in our investigation, including the conventional Gaussian Process (GP) model. We found that GP with expected improvement (EI) as the acquisition function but without a gamma prior which is standard in Bayesian Optimisation python library (BO Torch) outperforms other surrogate models. Additionally, it should be noted that while the machine learning model that exhibits superior performance in predicting the target variable may be considered the best choice, it may not necessarily serve as the most suitable surrogate model for BO. This observation has significant importance and warrants further investigation. This comprehensive framework accelerates the pace of materials discovery and addresses urgent needs in energy storage and environmental sustainability. It is to be noted that rather than identifying new MOFs, BO primarily enhances computational efficiency by reducing the reliance on more demanding calculations, such as those involved in Grand Canonical Monte Carlo (GCMC) or Density Functional Theory (DFT).

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

Long Duration Energy Storage Using Hydrogen in Metal-Organic Frameworks: Opportunities and Challenges

Materials-based H2 storage plays a critical role in facilitating H2 as a low-carbon energy carrier, but there remains limited guidance on the technical performance necessary for specific applications. Metal-organic framework (MOF) adsorbents have shown potential in power applications, but need to demonstrate economic promises against incumbent compressed H2 storage. Herein, we evaluate the potential impact of material properties, charge/discharge patterns, and propose targets for MOFs' deployment in long-duration energy storage applications including backup, load optimization, and hybrid power. We find that state-of-the-art MOF could outperform cryogenic storage and 350 bar compressed storage in applications requiring ≤8 cycles per year, but need ≥5 g/L increase in uptake to be cost-competitive for applications that require ≥30 cycles per year. Existing challenges include manufacturing at scale and quantifying the economic value of lower-pressure storage. Lastly, future research needs are identified including integrating thermodynamic effects and degradation mechanisms.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design

Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.

Hydrogen production and pollution mitigation: Enhanced gasification of plastic waste and biomass with machine learning & storage for a sustainable future

The pursuit of carbon neutrality confronts the twofold challenge of meeting energy demands and reducing pollution. This review article examines the potential of gasifying plastic waste and biomass as innovative, sustainable sources for hydrogen production, a critical element in achieving environmental reform. Addressing the problem of greenhouse gas emissions, the work highlights how the co-gasification of these feedstocks could contribute to environmental preservation by reducing waste and generating clean energy. Through an analysis of current technologies, the potential for machine learning to refine gasification for optimal hydrogen production is revealed. Additionally, hydrogen storage solutions are evaluated for their importance in creating a viable, sustainable energy infrastructure. The economic viability of these production methods is critically assessed, providing insights into both their cost-effectiveness and ecological benefits. Findings indicate that machine learning can significantly improve process efficiencies, thereby influencing the economic and environmental aspects of hydrogen production. Furthermore, the study presents the advancements in these technologies and their role in promoting a transition to a green economy and circular energy practices. Ultimately, the review delineates how integrating hydrogen production from unconventional feedstocks, bolstered by machine learning and advanced storage, can contribute to a sustainable and pollution-free future.

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

The AB2 metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB2 alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB2 metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R2, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB2 metal hydrides.

Nanomaterials: paving the way for the hydrogen energy frontier

This comprehensive review explores the transformative role of nanomaterials in advancing the frontier of hydrogen energy, specifically in the realms of storage, production, and transport. Focusing on key nanomaterials like metallic nanoparticles, metal-organic frameworks, carbon nanotubes, and graphene, the article delves into their unique properties. It scrutinizes the application of nanomaterials in hydrogen storage, elucidating both challenges and advantages. The review meticulously evaluates diverse strategies employed to overcome limitations in traditional storage methods and highlights recent breakthroughs in nanomaterial-centric hydrogen storage. Additionally, the article investigates the utilization of nanomaterials to enhance hydrogen production, emphasizing their role as efficient nanocatalysts in boosting hydrogen fuel cell efficiency. It provides a comprehensive overview of various nanocatalysts and their potential applications in fuel cells. The exploration extends to the realm of hydrogen transport and delivery, specifically in storage tanks and pipelines, offering insights into the nanomaterials investigated for this purpose and recent advancements in the field. In conclusion, the review underscores the immense potential of nanomaterials in propelling the hydrogen energy frontier. It emphasizes the imperative for continued research aimed at optimizing the properties and performance of existing nanomaterials while advocating for the development of novel nanomaterials with superior attributes for hydrogen storage, production, and transport. This article serves as a roadmap, shedding light on the pivotal role nanomaterials can play in advancing the development of clean and sustainable hydrogen energy technologies.

DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials

Nanoporous materials such as metal-organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO2 in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol-1 per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption.

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Biosensor Characterization from Cratylia mollis Seed Lectin (Cramoll)-MOF and Specific Carbohydrate Interactions in an Electrochemical Model

Biosensors are small devices known for their selectivity, high specificity and sensitivity to the respective analyte, at low concentrations. We developed an electrochemical biosensor using the crystalline polymer MOF-[Cu3 (BTC)2 (H2 O)2 ]n to characterize Cratylia mollis seed lectin (Cramoll) and its interaction with free carbohydrate (glucose) and carbohydrates on the surface of rabbit erythrocytes. The electrochemical potentials presented by the exponential curves that vary from 96 to 142 mV in relation to concentrations of 10 to 20 mM of glucose are decisive for the use of the system containing gold electrode/MOF/Cramoll for the characterization of biological models due to its high sensitivity. As well as the kinetic behavior presented in the cyclic voltammograms, with a cathodic current response of 0.000 3 A for a glucose concentration of 15 mM. These results were due to the high specificity of Cramoll under these conditions, promoting stability of surface charges at the Cramoll/electrode interface. This phenomenon facilitates the monitoring of the interaction with free glucose present in the electrolyte medium by potentiometric and amperometric methods and with carbohydrates present on the surface of rabbit erythrocytes through the potentiometric method. Through scanning electron microscopy (SEM) it was possible to observe Cramoll immobilized on the MOF surface, proving the specificity of the ligand (glucose-lectin) through the morphological lectin changes in this process. This electrochemical model, Cramoll/MOF biosensor, is effective for evaluating free lectin/carbohydrate or in the erythrocyte membrane.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R²) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects.

High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO2/H2 separation

It is challenging to identify the optimal metal-organic framework (MOF) adsorbents for gas adsorption and membrane-based separation from the large-scale material databases. The high-throughput computational screening (HTCS) method was adopted to discover the optimal materials for CO₂/H₂ separation from thousands of MOFs. First, a hierarchical strategy was used to select 1092 MOFs from 13 512 MOFs, and their adsorption capacity towards the equimolar CO₂/H₂ mixture at 298 K and 10 bar was further calculated using the grand canonical Monte Carlo (GCMC) simulations. The results show that those MOFs with lvtb topology and organic linker 1,2,4,5-tetrazine are conducive to exhibiting high performance CO₂/H₂ adsorption separation among top-100 MOFs with high performance. The MOFs with pore limited diameter (PLD), largest cavity diameter (LCD), gravimetrical surface area (GSA), and void fraction in the range of 4-12 Å, 5-12 Å, 5500-6500 m² g^-1 and 0.80-0.85, respectively, have high adsorption capacity towards CO₂. Second, the dynamic adsorption properties of the top-4 MOFs were simulated by the breakthrough curves of the binary (CO₂/H₂) and quinary (CO₂/H₂/CH₄/CO/N₂) mixtures in the fixed adsorption bed. MOF-4641 exhibits a high breakthrough time of 130 for the quinary mixture. Finally, the adsorption mechanism of CO₂ in the top-4 MOFs was investigated by the radial distribution function (RDF), the mass center probability density distribution, etc. The atomic insights from HTCS and breakthrough curve predictions in this work will be helpful in developing novel porous materials and obtaining superior CO₂ separation performance.

Enhancing Hydrogen Adsorption Capacity of Metal Organic Frameworks M(BDC)TED0.5 through Constructing a Bimetallic Structure

Metal organic frameworks (MOFs) have promising application prospects in the field of hydrogen storage. However, the successful application of MOFs in the field is still limited by their hydrogen storage capacity. Herein, a series of M _x M_1-x (BDC)TED_0.5 (M = Zn, Cu, Co, or Ni) with a bimetallic structure was constructed by introducing two metal ions in the synthesis process. The results of X-ray diffraction, scanning electron microscopy, energy-dispersive spectroscopy, X-ray photoelectron spectroscopy, and inductively coupled plasma showed that the bimetallic structure with different content ratios can be stably constructed by a hydrothermal method. Among them, the Cu-based bimetal MOFs Cu_0.625Ni_0.375(BDC)TED_0.5 exhibited the best hydrogen storage capacity of 2.04 wt% at 77 K and 1 bar, which was 22% higher than that of monometallic Ni(BDC)TED_0.5. The enhanced hydrogen storage capacity can be attributed to the improved specific surface area and micropore volume of bimetal MOFs by introducing an appropriate amount of bimetallic atoms.

Thermal Management for Hydrogen Charging and Discharging in a Screened Metal-Organic Framework Particle Tank

Thermal management of H₂ gas storage in a tank is crucial for determining the H₂ gas deliverable capacity. In this study, a strategy for the design of an excellent comprehensive performance fuel storage tank from the screening of microscopic materials to the design of macroscopic particle adsorption tank performance is proposed. The best metal-organic framework (MOF) for H₂ deliverable capacity in a computation-ready experimental MOF database is first screened using a grand canonical Monte Carlo (GCMC) method. An upscale model that combines the finite volume method with GCMC is then established to investigate the H₂ charging and discharging processes in a screened best MOF-filled adsorption particle tank that is integrated with a phase-change material (PCM) jacket. The process of the heat and mass transfer in the screened best MOF particle adsorption tank with and without the PCM jacket-inserted metal foam is studied. The results show that the prescreened XAWVUN has the highest gravimetric and considerable volumetric deliverable capacity among 503 MOFs, which can reach up to 23.1 mol·kg^-1 and 20.8 kg·m^-3 at 298 K and pressures between 35 000 kPa (adsorption pressure) and 160 kPa (desorption pressure), respectively. The H₂ deliverable capacity can be maximized by 3.2 and 12.1% for PCM jackets inserted with metal foam in the H₂ charging and discharging processes when it is compared with the case without the PCM jacket, respectively. The above study will facilitate the development of new equipment for hydrogen storage.