Solvents are central to metal-organic framework (MOF) solvothermal synthesis. However, how solvent-MOF interplay impacts MOF stabilization individually and across polymorphs is not well understood. To address this knowledge gap, here we perform data-driven analysis of 20,532 heats of adsorption at dilute conditions (Delta H ads) and 447 free energies of solvation (Delta F sol) for four solvents, namely, dimethylformamide (DMF), water (H2O), methanol (MeOH), and n-hexane (C6) (which was used as a control). To accelerate data collection, we developed a protocol to extrapolate Delta F sol from calculations with the MOFs only partially solvated. Free energies were obtained via thermodynamic integration. We found Delta F sol and Delta H ads to be only moderately correlated due to solvent-solvent interactions coming into play when the MOF is solvated. In any case, trends in Delta F sol were ultimately explained on the basis of solvent kinetic diameter and polarity, as well as MOF void fraction (V f ) and functionalization polarity. For instance, the correlation between Delta F sol and V f was one of the strongest correlations presented in this study (more so as the solvent size increases), indicating that small-pore MOFs are more easily stabilized by solvation than large-pore MOFs. We also found that solvation-induced MOF stabilization became more pronounced as solvent kinetic diameter (polarity) decreased (increased). We found differences in this solvation-induced stabilization between polymorphs capable of overcoming inherent (i.e., in vacuum) differences in polymorph stability, causing the most stable polymorph to "switch." We found the probability to cause "switches" to increase as solvent kinetic diameter (polarity) decreased (increased). Inspection of multivariate linear regression coefficients suggested that differences in solvation-induced stabilization in polymorphs can be primarily explained by their differences in density, V f , and, to a lesser extent, volumetric surface area.

Functionalization is poised to play a prominent role in MOF development as it could become the to-go strategy to bestow extant MOF with new properties, and to control MOF pore shape and size by modulating polymorph selection. Thus, to speed up MOF development through computational work, a better (predictive) understanding on how functionalization impacts MOF synthesizability is needed. Here we use a data-driven approach where molecular dynamics simulations on 5,000+ MOFs are used to shed light on how functionalization affects MOF free energy, as the latter has been largely tied to MOF synthesizability and polymorph selection. More consistently in MOFs with higher void fractions, we find that functionalization generally reduces free energy, with entropy contributing significantly to this thermodynamic stabilization. Although with some functionalizations (-CF3, -F, -Br, -SH, -OH) the role of entropy is more apparent than with others (-CN, -CH3, -NO2, -NH3). Through uneven stabilization of polymorphs, we also find functionalization (more often with -Br, -CN and -CF3) as capable of altering polymorph (topology) selection relative to original non-functionalized polymorphic families. However, no switch in polymorph stability ever occurred when the original (unfunctionalized) polymorphs were separated by more than 1.42 kJ/mol per MOF atom. We show that machine learning can predict functionalization-induced free energy change of a parent MOF with a mean absolute error of 0.16 kJ/mol per atom, using only physical properties of the parent MOF and the functional group as input. The ML-based SHAP analysis agrees with human analysis on the functionalization molecular mass and the hydrogen fraction of the parent MOF being among the factors that influence change in free energy the most. Finally, we present a publicly accessible dynamic interface to visualize and navigate the free energy data , thereby encouraging the research community to engage with and utilize the data to help uncover new insights.

The formation mechanisms of metal-organic frameworks (MOFs) are not fully understood. Therefore, experimental realization of potential "breakthrough" MOFs is hindered by uncertainty in the synthesis conditions that would allow the constituent nodes and linkers to self-assemble into the targeted MOF structure. Here, a multiscale endeavor using density functional theory (DFT) calculations, followed by metadynamics with DFT-informed classical atomistic potentials, followed by standard molecular dynamics (MD) and Hamiltonian replica exchange (HREX) simulations with a metadynamics-informed, coarse-grained (CG) model, was used to study the self-assembly mechanism of cubic-symmetry porous crystals inspired by the IRMOF-n family of MOFs. Mechanistic differences were examined for different values of node-linker coordination strength-understood as the free energy penalty for breaking a coordination bond in a solvated environment. Our integrated analyses of HREX-derived free energy surfaces and standard MD trajectories indicate that at coordination strengths typical of the IRMOF-n family in dimethylformamide (DMF) (i.e., 52 kJ/mol), disassembled nodes and linkers are favored to overcome a small 1.3 kJ/mol free energy barrier to first form solid amorphous clusters, which then overcome a series of barriers (the largest of which is 3.7 kJ/mol) to heal and form ordered, more stable, cubic-symmetry crystals. This healing seems to occur through the splintering/reattaching of small clusters from/to large clusters. Our analyses also suggest that if coordination strength is moderately weakened (e.g., to 40 kJ/mol), crystals form without the preliminary formation of amorphous clusters. However, further coordination strength weakening (e.g., to 36 kJ/mol) makes the formation of sizable crystals unfavorable thermodynamically. On the other hand, strengthening the coordination would increase the free energy barrier to heal the amorphous clusters into crystals. Accordingly, if coordination becomes too strong (e.g., 65+ kJ/mol), then healing may become unlikely. In practical terms, our study suggests that MOF formation is favored only when the free energy of coordination, accounting for solvent effects, falls within a relatively narrow range (approximately 40 to 65 kJ/mol), at least at 300 K, for MOFs with cubic symmetries.

The role of Ru promotion of Fe catalysts in the growth of small-diameter single-wall carbon nanotubes (SWCNTs) has been investigated using an Autonomous Research System (ARES)a high throughput, laser-induced chemical vapor deposition (CVD) system capable of in situ Raman spectroscopy. Growth experiments in the ARES were conducted using a standard feedstock (ethylene) at 800 °C for Fe-Ru and Fe catalysts. The results show that Fe-Ru (with a composition ∼10%) supports the growth of small-diameter SWCNTs (less than 1.1 nm) compared to pure Fe. In addition, density functional theory (DFT) calculations on Fe nanoparticle clusters of 55 and 59 atoms with and without adding Ru were performed to probe the effect of Ru on the catalyst stability. The DFT results show that, irrespective of the exact distribution of Ru atoms within these clusters, the addition of Ru to Fe clusters increases the cohesive energy of the catalyst particle with respect to the pure Fe cluster. The combined Raman and DFT data indicate that high-melting-point transition metals can stabilize the catalyst nanoparticles and suppress sintering, thus increasing small-diameter selectivity of SWCNTs. This finding has been verified in a conventional hot-wall CVD system that utilizes industrial gaseous waste as a feedstock using multiexcitation Raman spectroscopy.

Designing multi-functional alloys requires exploring high-dimensional composition-structure-property spaces, yet current tools are limited to low-dimensional projections and offer limited support for sensitivity or multi-objective tradeoff reasoning. We introduce AlloyLens, an interactive visual analytics system combining a coordinated scatterplot matrix (SPLOM), dynamic parameter sliders, gradient-based sensitivity curves, and nearest neighbor recommendations. This integrated approach reveals latent structure in simulation data, exposes the local impact of compositional changes, and highlights tradeoffs when exact matches are absent. We validate the system through case studies co-developed with domain experts spanning structural, thermal, and electrical alloy design.

Metal-organic frameworks (MOFs) offer vast potential for numerous applications but their deceptively simple synthesis remains poorly understood. This fact hinders translating proposed MOF designs from computational high-throughput screening methods into successful synthesis, and slows down MOF development in general. In addition, much of the practical chemical intuition on MOF synthesis remains embedded in natural language across thousands of MOF reports. These limitations call for research exploring data-driven understanding of MOF synthesis and MOF synthesis automation. This paper reports on novel research developing an MOF expert-guided framework that leverages large language models (LLMs) to extract and codify synthesis procedures of metal-organic frameworks (MOFs) in a sequence-aware manner from experimental literature. Specifically, we developed an end-to-end pipeline that combines literature matching, synthesis paragraph classification, and prompt-based entity and relation extraction using GPT-4. Guided by MOF experts, we designed a comprehensive and FAIR-compliant synthesis codification schema that captures synthesis actions, precursors, conditions, and their interrelations as a sequence-aware directed graph. Our model achieves high accuracy in synthesis paragraph classification (F1 score: 0.93) and in entity and relation extraction (F1 score: 0.96 and 0.94, respectively). This work enables large-scale, structured synthesis data collection and paves the way for AI-assisted synthesis prediction and knowledge discovery in materials science.

Metal-organic frameworks (MOFs) promise to engender technology-enabling properties for numerous applications. However, one significant challenge in MOF development is their overwhelmingly large design space, which is intractable to fully explore even computationally. To find diverse optimal MOF designs without exploring the full design space, we develop Vendi Bayesian optimization (VBO), a new algorithm that combines traditional Bayesian optimization with the Vendi Score, a recently introduced interpretable diversity measure. Both Bayesian optimization and the Vendi Score require a kernel similarity function, we therefore also introduce a novel similarity function in the space of MOFs that accounts for both chemical and structural features. This new similarity metric enables VBO to find optimal MOFs with properties that may depend on both chemistry and structure. We statistically assessed VBO by its ability to optimize three NH3-adsorption dependent performance metrics that depend, to different degrees, on MOF chemistry and structure. With ten simulated campaigns done for each metric, VBO consistently outperformed random search to find high-performing designs within a 1,000-MOF subset for i) NH3 storage, ii) NH3 removal from membrane plasma reactors, and iii) NH3 capture from air. Then, with one campaign dedicated to finding optimal MOFs for NH3 storage in a “hybrid” ~10,000-MOF database, we identify twelve extant and eight hypothesized MOF designs with potentially record-breaking working capacity ∆NNH3 between 300 K and 400 K at 1 bar. Specifically, the best MOF designs are predicted to i) achieve ∆NNH3 values between 23.6 and 29.3 mmol/gm, potentially surpassing those that MOFs previously experimentally tested for NH3 adsorption would have at the proposed operation conditions, ii) be thermally stable at the operation conditions and iii) require only ca. 10% of the energy content in NH3 to release the stored molecule from the MOF. Finally, the analysis of the generated simulation data during the search indicates that a pore size of around 10 Å, a heat of adsorption around 33 kJ/mol, and the presence of Ca could be part of MOF design rules that could help optimize NH3 working capacity at the proposed operation conditions

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion.

Purpose This paper reports on a scientometric analysis bolstered by human-in-the-loop, domain experts, to examine the field of metal-organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human-in-the-loop processes, can help develop a comprehensive view of a field's research trends, influential works, and specialized topics.Design/methodology/approach A scientometric analysis was conducted, integrating natural language processing (NLP), topic modeling, and network analysis methods. The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals. MOF researcher feedback was incorporated into our method. The data sample included 65,209 MOF research articles. Python3 and software tool VOSviewer were used to perform the analysis.Findings The findings demonstrate the value of including domain experts in research workflows, refinement, and interpretation of results. At each stage of the analysis, the MOF researchers contributed to interpreting the results and method refinements targeting our focus on MOF research. This study identified influential works and their themes. Our findings also underscore four main MOF research directions and applications.Research limitations This study is limited by the sample (articles identified and referenced by the Cambridge Structural Database) that informed our analysis.Practical implications Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research. Additionally, the results will help domain scientists target future research directions.Originality/value To the best of our knowledge, the number of publications collected for analysis exceeds those of previous studies. This enabled us to explore a more extensive body of MOF research compared to previous studies. Another contribution of our work is the iterative engagement of domain scientists, who brought in-depth, expert interpretation to the data analysis, helping hone the study.

We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing the LLM, ChatGPT, to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials.

Adsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource intensive, limiting the identification of promising materials. Active learning (AL) methods offer an effective strategy to reduce the computational burden by selectively acquiring critical data for model training. Metal-organic frameworks (MOFs) exhibit immense potential across various adsorption applications due to their porous structure and their modular nature, leading to diverse pore sizes and chemistry that serve as an ideal platform to develop adsorption models. Here, we demonstrate the efficacy of AL in predicting gas adsorption within MOFs using “alchemical” molecules and their interactions as surrogates for real molecules. We first applied AL separately to each MOF, reducing the training dataset size by 57.5% while retaining predictive accuracy. Subsequently, we amalgamated the refined datasets across 1800 MOFs to train a multilayer perceptron (MLP) model, successfully predicting adsorption of real molecules. Furthermore, by integrating MOF features into the AL framework using principal component analysis (PCA), we navigated MOF space effectively, achieving high predictive accuracy with only a subset of MOFs. Our results highlight AL's efficiency in reducing dataset size, enhancing model performance, and offering insights into adsorption phenomenon in large datasets of MOFs. This study underscores AL's crucial role in advancing computational material science and developing more accurate and less data intensive models for gas adsorption in porous materials.

Plasma reactors are promising to decarbonize the production of NH3, but their NH3 energy yields need to improve to facilitate their broad adoption. Two emerging strategies to reduce energy inefficiencies aim to protect the freshly formed NH3 from destruction by the plasma by leveraging NH3 adsorption properties of porous materials as either catalyst supports or as membranes. As metal-organic frameworks (MOFs) are promising porous materials for adsorption-based applications, we performed large-scale computational screening of 13,460 MOFs to study their potential for the above-mentioned uses. To reduce computational cost by similar to 10-fold, we developed a generalizable hierarchical MOF screening strategy that starts with the selection of a 200-MOF set based on NH3 adsorption Henry's constants, for which the relevant performance metrics are calculated via molecular simulation. This set is used to "initialize" a machine learning (ML) model that predicts the relevant metrics in the whole MOF database, in turn guiding the selection of additional promising MOFs to be evaluated via molecular simulation. The ML model is then iteratively refined leveraging the emerging molecular simulation data from the MOFs selected at each iteration from the ML predictions themselves. From evaluation of only similar to 10% of the database, for each use (catalyst support or membrane), 20 extant MOFs were holistically assessed and proposed for experimental testing based on desirable adsorption properties as well as complementary properties (e.g., high thermal decomposition temperature, constituted by earth abundant metals, etc.). Data-driven material design guidelines also emerged from the screening. For instance, a pore diameter of similar to 10 & Aring; and a heat of adsorption of similar to 90 kJ/mol were found beneficial for the catalyst support use. On the other hand, for the membrane-based strategy, a pore diameter of similar to 2.75 & Aring; and a heat of adsorption of similar to 80 kJ/mol were found beneficial. The presence of V was found beneficial for both uses.

Singlet fission (SF) has been explored as a viable routeto improvephotovoltaic performance by producing more excitons. Efficient SFis achieved through a high degree of interchromophoric coupling thatfacilitates electron superexchange to generate triplet pairs. However,strongly coupled chromophores often form excimers that can serve asan SF intermediate or a low-energy trap site. The succeeding decoherenceprocess, however, requires an optimum electronic coupling to facilitatethe isolation of triplet production from the initially prepared correlatedtriplet pair. Conformational flexibility and dielectric modulationcan provide a means to tune the SF mechanism and efficiency by modulatingthe interchromophoric electronic interaction. Such a strategy cannotbe easily adopted in densely stacked traditional organic solids. Here,we show that the assembly of the SF-active chromophores around well-definedpores of solution-stable metal-organic frameworks (MOFs) canbe a great platform for a modular SF process. A series of three newMOFs, built out from 9,10-bis(ethynylenephenyl)anthracene-derivedstruts, show a topology-defined packing density and conformationalflexibility of the anthracene core to dictate the SF mechanism. Varioussteady-state and transient spectroscopic data suggest that the initiallyprepared singlet population can prefer either an excimer-mediatedSF or a direct SF (both through a virtual charge-transfer (CT) state).These solution-stable frameworks offer the tunability of the dielectricenvironment to facilitate the SF process by stabilizing the CT state.Given that MOFs are a great platform for various photophysical andphotochemical developments, generating a large population of long-livedtriplets can expand their utilities in various photon energy conversionschemes.

High quantum yield triplets, populated by initially prepared excited singlets, are desired for various energy conversion schemes in solid working compositions like porous MOFs. However, a large disparity in the distribution of the excitonic center of mass, singlet-triplet intersystem crossing (ISC) in such assemblies is inhibited, so much so that a carboxy-coordinated zirconium heavy metal ion cannot effectively facilitate the ISC through spin-orbit coupling. Circumventing this sluggish ISC, singlet fission (SF) is explored as a viable route to generating triplets in solution-stable MOFs. Efficient SF is achieved through a high degree of interchromophoric coupling that facilitates electron super-exchange to generate triplet pairs. Here we show that a predesigned chromophoric linker with extremely poor ISC efficiency ( k ISC ) but form triplets in MOF in contrast to the frameworks that are built from linkers with sizable k ISC but . This work opens a new photophysical and photochemical avenue in MOF chemistry and utility in energy conversion schemes.

About 1% of the world’s CO2 emissions are tied to the standard method to produce NH3 (i.e., Haber-Bosch process), hence there is a need to decarbonize the production this chemical. To this end, plasma-assisted catalysis is emerging as a “green” alternative to synthesize NH3. However, insufficient mechanistic understanding of this process has hindered significant improvements in its cost-effectiveness. Here we leverage “minimal plasma” microkinetic models and select experiments in a dielectric-barrier discharge (DBD) plasma reactor to look for missing mechanistic insights. Relatively robust to model assumptions, we find that our modeling supports the thesis that plasma N and H radicals are the kinetics controlling plasma species for reactions involving the catalyst. This support stems from the realization that only the inclusion of N and H radicals in our models can readily explain key experimental observations for plasma assisted NH3 synthesis such as: i) similar catalytic activity for Fe and Ag (two metals at the opposite ends of N2 dissociation capabilities), ii) activity increase in Fe (a metal that readily dissociates N2) relative to thermal catalysis, and iii) detection of catalyst bound N2HY species. We also find the N radicals (a source of surface bound N*) to be more important in nitrophobic metals and H radicals (a hydrogenating agent via Eley-Rideal reactions) to be more important in nitrophilic metals. On the other hand, other mechanistic aspects such as the kinetic relevance of N2HY-forming pathways and dissolution reactions are discussed as a function of model assumptions. Our modeling suggests that some of these assumptions could be potentially clarified through in situ compositional analysis of catalyst adlayers (e.g., the fraction of radicals from the plasma bulk that reach the catalyst surface), as the adlayer composition seems to be rather sensitive to the plasma environment assumed to be “seen” by the catalyst.

Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivity k in MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm−3), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m−1 K−1), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m−1 K−1), the structures of which we describe in additional detail.

Solar energy conversion requires the working compositions to generate photoinduced charges with high potential and the ability to deliver charges to the catalytic sites and/or external electrode. These two properties are typically at odds with each other and call for new molecular materials with sufficient conjugation to improve charge conductivity but not as much conjugation as to overly compromise the optical band gap. In this work, we developed a semiconducting metal-organic framework (MOF) prepared explicitly through metal-carbodithioate "(-CS2)nM" linkage chemistry, entailing augmented metal-linker electronic communication. The stronger ligand field and higher covalent character of metal-carbodithioate linkages─when combined with spirofluorene-derived organic struts and nickel(II) ion-based nodes─provided a stable, semiconducting 3D-porous MOF, Spiro-CS2Ni. This MOF lacks long-range ordering and is defined by a flexible structure with non-aggregated building units, as suggested by reverse Monte Carlo simulations of the pair distribution function obtained from total scattering experiments. The solvent-removed "closed pore" material recorded a Brunauer-Emmett-Teller area of ∼400 m2/g, where the "open pore" form possesses 90 wt % solvent-accessible porosity. Electrochemical measurements suggest that Spiro-CS2Ni possesses a band gap of 1.57 eV (σ = 10-7 S/cm at -1.3 V bias potential), which can be further improved by manipulating the d-electron configuration through an axial coordination (ligand/substrate), the latter of which indicates usefulness as an electrocatalyst and/or a photoelectrocatalyst (upon substrate binding). Transient-absorption spectroscopy reveals a long-lived photo-generated charge-transfer state (τCR = 6.5 μs) capable of chemical transformation under a biased voltage. Spiro-CS2Ni can endure a compelling range of pH (1-12 for weeks) and hours of electrochemical and photoelectrochemical conditions in the presence of water and organic acids. We believe this work provides crucial design principles for low-density, porous, light-energy-conversion materials.

Recent developments in Large Language Models (LLMs) have advanced the natural language processing (NLP) studies to a new era [1], [2], [4]–[6]. In generic domains, LLMs have become a key component in wide variety of state-of-the-art NLP tasks. In addition, prompt learning enables LLMs-based models to reach robust performance with much smaller training data.

This work highlights how Pd–O arrangements and particle sizes impact primary H 2 O 2 selectivities and yields in its direct synthesis.