The role of computational results databases in accelerating the discovery of catalysts

Encapsulated Co-Ni alloy boosts high-temperature CO2 electroreduction

Electrochemical CO2 reduction into chemicals and fuels holds great promise for renewable energy storage and carbon recycling1-3. Although high-temperature CO2 electroreduction in solid oxide electrolysis cells is industrially relevant, current catalysts have modest energy efficiency and a limited lifetime at high current densities, generally below 70% and 200 h, respectively, at 1 A cm-2 and temperatures of 800 °C or higher4-8. Here we develop an encapsulated Co-Ni alloy catalyst using Sm2O3-doped CeO2 that exhibits an energy efficiency of 90% and a lifetime of more than 2,000 h at 1 A cm-2 for high-temperature CO2-to-CO conversion at 800 °C. Its selectivity towards CO is about 100%, and its single-pass yield reaches 90%. We show that the efficacy of our catalyst arises from its unique encapsulated structure and optimized alloy composition, which simultaneously enable enhanced CO2 adsorption, moderate CO adsorption and suppressed metal agglomeration. This work provides an efficient strategy for the design of catalysts for high-temperature reactions that overcomes the typical trade-off between activity and stability and has potential industrial applications.

Automated prediction of ground state spin for transition metal complexes

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes.

On the Arrival Time Distribution of Reacting Systems in Ion Mobility Spectrometry

Ion mobility spectrometry (IMS) is a widely used gas-phase separation technique, particularly when coupled with mass spectrometry (MS). Modern IMS instruments often apply elevated reduced field strengths for improved ion separation and ion focusing. These alter the collision dynamics and further drive ion reaction processes that can change the analyte's structure. As a result, the measured arrival time distribution (ATD) can change with the applied reduced field strengths. In this work, we systematically study how the ion collision dynamics and the ion reaction dynamics, as a function of the reduced field strength, can alter the ATD. To this end, we investigate 2,6-di-tert-butylpyridine, methanol, and ethyl acetate using a home-built drift tube IMS coupled to a home-built MS and extensive first-principles Monte Carlo modeling. We show how elevated reduced field strengths can actually lower resolving power through increased ion diffusion and how the field dependency of the ion mobility can introduce uncertainties to collision cross sections (CCS) calculated from the measured mobilities. On top of the collision dynamics, we show how chemical transformation processes that alter the analyte's CCS, e.g., dynamic clustering or fragmentation, can lead to broadened, shifted, or non-Gaussian ATDs and how sensitive these processes are to the applied field strengths. We highlight how first-principles ion dynamics simulations can help to understand and even harness the mentioned effects.

Recent Advances Hydrogenation of Carbon Dioxide to Light Olefins over Iron-Based Catalysts via the Fischer-Tropsch Synthesis

The massive burning of fossil fuels has been important for economic and social development, but the increase in the CO2 concentration has seriously affected environmental sustainability. In industrial and agricultural production, light olefins are one of the most important feedstocks. Therefore, the preparation of light olefins by CO2 hydrogenation has been intensively studied, especially for the development of efficient catalysts and for the application in industrial production. Fe-based catalysts are widely used in Fischer-Tropsch synthesis due to their high stability and activity, and they also exhibit excellent catalytic CO2 hydrogenation to light olefins. This paper systematically summarizes and analyzes the reaction mechanism of Fe-based catalysts, alkali and transition metal modifications, interactions between active sites and carriers, the synthesis process, and the effect of the byproduct H2O on catalyst performance. Meanwhile, the challenges to the development of CO2 hydrogenation for light olefin synthesis are presented, and future development opportunities are envisioned.

Low-nuclearity CuZn ensembles on ZnZrOx catalyze methanol synthesis from CO2

Metal promotion could unlock high performance in zinc-zirconium catalysts, ZnZrOx, for CO2 hydrogenation to methanol. Still, with most efforts devoted to costly palladium, the optimal metal choice and necessary atomic-level architecture remain unclear. Herein, we investigate the promotion of ZnZrOx catalysts with small amounts (0.5 mol%) of diverse hydrogenation metals (Re, Co, Au, Ni, Rh, Ag, Ir, Ru, Pt, Pd, and Cu) prepared via a standardized flame spray pyrolysis approach. Cu emerges as the most effective promoter, doubling methanol productivity. Operando X-ray absorption, infrared, and electron paramagnetic resonance spectroscopic analyses and density functional theory simulations reveal that Cu0 species form Zn-rich low-nuclearity CuZn clusters on the ZrO2 surface during reaction, which correlates with the generation of oxygen vacancies in their vicinity. Mechanistic studies demonstrate that this catalytic ensemble promotes the rapid hydrogenation of intermediate formate into methanol while effectively suppressing CO production, showcasing the potential of low-nuclearity metal ensembles in CO2-based methanol synthesis.

Combined DFT and Kinetic Monte Carlo Study of UiO-66 Catalysts for γ-Valerolactone Production

Zr-based metal-organic frameworks (MOFs) are excellent heterogeneous porous catalysts due to their thermal stability. Their tunability via node and linker modifications makes them amenable for theoretical studies on catalyst design. However, detailed benchmarks on MOF-based reaction mechanisms combined with kinetics analysis are still scarce. Thus, we here evaluate different computational models and density functional theory (DFT) methods followed by kinetic Monte Carlo studies for a case reaction relevant in biomass upgrading, i.e., the conversion of methyl levulinate to γ-valerolactone catalyzed by UiO-66. We show the impact of cluster versus periodic models, the importance of the DF of choice, and the direct comparison to experimental data via simulated kinetics data. Overall, we found that Perdew-Burke-Ernzerhof (PBE), a widely employed method in plane-wave periodic calculations, greatly overestimates reaction rates, while M06 with cluster models better fits the available experimental data and is recommended whenever possible.

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Reaction-Induced Metal-Metal Oxide Interactions in Pd-In2 O3 /ZrO2 Catalysts Drive Selective and Stable CO2 Hydrogenation to Methanol

Ternary Pd-In2 O3 /ZrO2 catalysts exhibit technological potential for CO2 -based methanol synthesis, but developing scalable systems and comprehending complex dynamic behaviors of the active phase, promoter, and carrier are key for achieving high productivity. Here, we show that the structure of Pd-In2 O3 /ZrO2 systems prepared by wet impregnation evolves under CO2 hydrogenation conditions into a selective and stable architecture, independent of the order of addition of Pd and In phases on the zirconia carrier. Detailed operando characterization and simulations reveal a rapid restructuring driven by the metal-metal oxide interaction energetics. The proximity of InPdx alloy particles decorated by InOx layers in the resulting architecture prevents performance losses associated with Pd sintering. The findings highlight the crucial role of reaction-induced restructuring in complex CO2 hydrogenation catalysts and offer insights into the optimal integration of acid-base and redox functions for practical implementation.

MobCal-MPI 2.0: an accurate and parallelized package for calculating field-dependent collision cross sections and ion mobilities

Ion mobility spectrometry (IMS), which can be employed as either a stand-alone instrument or coupled to mass spectrometry, has become an important tool for analytical chemistry. Because of the direct relation between an ion's mobility and its structure, which is intrinsically related to its collision cross section (CCS), IMS techniques can be used in tandem with computational tools to elucidate ion geometric structure. Here, we present MobCal-MPI 2.0, a software package that demonstrates excellent accuracy (RMSE 2.16%) and efficiency in calculating low-field CCSs via the trajectory method (≤30 minutes on 8 cores for ions with ≤70 atoms). MobCal-MPI 2.0 expands on its predecessor by enabling the calculation of high-field mobilities through the implementation of the 2^nd order approximation to two-temperature theory (2TT). By further introducing an empirical correction to account for deviations between 2TT and experiment, MobCal-MPI 2.0 can compute accurate high-field mobilities that exhibit a mean deviation of <4% from experimentally measured values. Moreover, the velocities used to sample ion-neutral collisions were updated from a weighted to a linear grid, enabling the near-instantaneous evaluation of mobility/CCS at any effective temperature from a single set of N₂ scattering trajectories. Several enhancements made to the code are also discussed, including updates to the statistical analysis of collision event sampling and benchmarking of overall performance.

Validation of Field-Dependent Ion-Solvent Cluster Modeling via Direct Measurement of Cluster Size Distributions

Ion mobility spectrometry is widely used in analytical chemistry, either as a stand-alone technique or coupled to mass spectrometry. Ions in the gas phase tend to form loosely bound clusters with surrounding solvent vapors, artificially increasing the collisional cross section and the mass of the ion. This, in turn, affects ion mobility and influences separation. Further, ion-solvent clusters play an important role in most ionization mechanisms occurring in the gas phase. Consequently, a deeper understanding of ion-solvent cluster association and dissociation processes is desirable to guide experimental design and interpretation. A few computational models exist, which aim to describe the amount of clustering as a function of the reduced electric field strength, bath gas pressure and temperature, and the chemical species probed. It is especially challenging to model ion mobility under high reduced electrical field strengths due to the nonthermal conditions created by the field. In this work, we aim to validate a recently proposed first-principles model by comparing its predictions with direct measurements of cluster size distributions over a range of 20-120 Td as observed using a High Kinetic Energy Ion Mobility Spectrometer coupled to a mass spectrometer (HiKE-IMS-MS). By studying H⁺(H₂O)_n, [MeOH + H + n(H₂O)]⁺, [ACE + H + n(H₂O)]⁺, and [PhNH₂ + H + n(H₂O)]⁺ as test systems, we find very good agreement between model and experiment, supporting the validity of the computational workflow. Further, the detailed information gained from the modeling yields important insights into the cluster dynamics within the HiKE-IMS, allowing for better interpretation of the measured ion mobility spectra.

First-Principles Modeling of Preferential Solvation in Mixed-Modifier Differential Mobility Spectrometry

Differential mobility spectrometry (DMS) separates ions based on mobility differences between high and low electric field conditions. To enhance resolution, solvents such as water and acetonitrile are often used to modify the collision environment and take advantage of differing dynamic clustering behavior between analytes that coelute in hard-sphere environments (e.g., N₂). When binary solvent mixtures are used to modify the DMS environment, one solvent can have a dominant influence over the other with respect to ion trajectories. For example, for quinoline derivatives, a 9:1 water:acetonitrile solvent mixture exhibited identical behavior to an environment containing only acetonitrile as a modifier. It was hypothesized that this effect arises due to the significantly different binding strengths of the two solvents. Here, we utilize a first-principles model of DMS to study analytes in single and binary solvent mixtures and explore the effects governing the dominance of one solvent over the other. Computed DMS dispersion curves of quinoline derivatives are in excellent agreement with those measured experimentally. For mixed-modifier environments, the predicted cluster populations show a clear preferential solvation of ions with the stronger binding solvent. The influence of ion-solvent binding energies, solvent concentration, and solvent molecule size is discussed in the context of the observed DMS behavior. This work can guide the usage of binary solvent mixtures for improving ion separations in cases where compounds coelute in pure N₂ and in single-solvent modifier environments. Moreover, our results indicate that binary solvent mixtures can be used to create a relative scale for solvent binding energies.

Chiral Seeded Growth of Gold Nanorods Into Fourfold Twisted Nanoparticles with Plasmonic Optical Activity

A robust and reproducible methodology to prepare stable inorganic nanoparticles with chiral morphology may hold the key to the practical utilization of these materials. An optimized chiral growth method to prepare fourfold twisted gold nanorods is described herein, where the amino acid cysteine is used as a dissymmetry inducer. Four tilted ridges are found to develop on the surface of single-crystal nanorods upon repeated reduction of HAuCl4 , in the presence of cysteine as the chiral inducer and ascorbic acid as a reducing agent. From detailed electron microscopy analysis of the crystallographic structures, it is proposed that the dissymmetry results from the development of chiral facets in the form of protrusions (tilted ridges) on the initial nanorods, eventually leading to a twisted shape. The role of cysteine is attributed to assisting enantioselective facet evolution, which is supported by density functional theory simulations of the surface energies, modified upon adsorption of the chiral molecule. The development of R-type and S-type chiral structures (small facets, terraces, or kinks) would thus be non-equal, removing the mirror symmetry of the Au NR and in turn resulting in a markedly chiral morphology with high plasmonic optical activity.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

Kinetics in DMS: Modeling Clustering and Declustering Reactions

Differential mobility spectrometry (DMS) uses high-frequency oscillating electrical fields to harness the differential mobility of ions for separating complex sample mixtures prior to detection. To increase the resolving power, a dynamic microsolvation environment is often created by introducing solvent vapors. Here, relatively large clusters are formed at low-field conditions which then evaporate to form smaller clusters at high-field conditions. The kinetics of these processes as the electrical field strength oscillates are not well studied. Here, we develop a computational framework to investigate how the different reactions (cluster association, cluster dissociation, and fast conformational changes) behave at different field strengths. We aim to better understand these processes, their effect on experimental outcomes, and whether DMS model accuracy is improved via incorporating their description. We find that cluster association and dissociation reactions for typical ion-solvent pairs are fast compared to the time scale of the varying separation fields usually used. However, low solvent concentration, small dipole moments, and strong ion-solvent binding can result in reaction rates small enough that a lag is observed in the ion's DMS response. This can yield differences of several volts in the compensation voltages required to correct ion trajectories for optimal transmission. We also find that the proposed kinetic approach yields generally better agreement with experiment than using a modified Boltzmann weighting scheme. Thus, this work provides insights into the chemical dynamics occurring within the DMS cell while also increasing the accuracy of dispersion plot predictions.

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.

Chemical reaction network knowledge graphs: the OntoRXN ontology

The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows.

Modeling Operando Electrochemical CO2 Reduction

Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO₂ reduction (eCO₂R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO₂R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.

Computational Mechanism of Methyl Levulinate Conversion to γ-Valerolactone on UiO-66 Metal Organic Frameworks

Metal-organic frameworks (MOFs) are gaining importance in the field of biomass conversion and valorization due to their porosity, well-defined active sites, and broad tunability. But for a proper catalyst design, we first need detailed insight of the system at the atomic level. Herein, we present the reaction mechanism of methyl levulinate to γ-valerolactone on Zr-based UiO-66 by means of periodic density functional theory (DFT). We demonstrate the role of Zr-based nodes in the catalytic transfer hydrogenation (CTH) and cyclization steps. From there, we perform a computational screening to reveal key catalyst modifications to improve the process, such as node doping and linker exchange.

Improved First-Principles Model of Differential Mobility Using Higher Order Two-Temperature Theory

Differential mobility spectrometry is a separation technique that may be applied to a variety of analytes ranging from small molecule drugs to peptides and proteins. Although rudimentary theoretical models of differential mobility exist, these models are often only applied to small molecules and atomic ions without considering the effects of dynamic microsolvation. Here, we advance our theoretical description of differential ion mobility in pure N₂ and microsolvating environments by incorporating higher order corrections to two-temperature theory (2TT) and a pseudoequilibrium approach to describe ion-neutral interactions. When comparing theoretical predictions to experimentally measured dispersion plots of over 300 different compounds, we find that higher order corrections to 2TT reduce errors by roughly a factor of 2 when compared to first order. Model predictions are accurate especially for pure N₂ environments (mean absolute error of 4 V at SV = 4000 V). For strongly clustering environments, accurate thermochemical corrections for ion-solvent clustering are likely required to reliably predict differential ion mobility behavior. Within our model, general trends associated with clustering strength, solvent vapor concentration, and background gas temperature are well reproduced, and fine structure visible in some dispersion plots is captured. These results provide insight into the dynamic ion-solvent clustering process that underpins the phenomenon of differential ion mobility.

Mechanism and Stereochemistry of Rhodium-Catalyzed [5 + 2 + 1] Cycloaddition of Ene-Vinylcyclopropanes and Carbon Monoxide Revealed by Visual Kinetic Analysis and Quantum Chemical Calculations

Previously, we developed a rhodium-catalyzed [5 + 2 + 1] cycloaddition of ene-vinylcyclopropanes (ene-VCPs) and carbon monoxide to synthesize eight-membered carbocycles. The efficiency of this reaction can be appreciated from its application in the synthesis of several natural products. Herein we report the results of a 15-year investigation into the mechanism of the [5 + 2 + 1] cycloaddition by applying visual kinetic analysis and high-level quantum chemical calculations at the DLPNO-CCSD(T)//BMK level. According to the kinetic measurements, the resting state of the catalyst possesses a dimeric structure (with two rhodium centers) whereas the active catalytic species is monomeric (with one rhodium center). The catalytic cycle consists of cyclopropane cleavage (the turnover-limiting step), alkene insertion, CO insertion, reductive elimination, and catalyst transfer steps. Other reaction pathways have also been considered but then have been ruled out. The steric origin of the diastereoselectivity (cis versus trans) was revealed by comparing the alkene insertion transition states. In addition, how the double-bond configuration of the VCPs (Z versus E) affects the substrate reactivity and the origins of chemoselectivity ([5 + 2 + 1] versus [5 + 2]) were also investigated. The present study will provide assistance in understanding other carbonylative annulations catalyzed by transition metals.

New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited

The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH₃ (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.

UVPD spectroscopy of differential mobility-selected prototropic isomers of protonated adenine

Two prototropic isomers of adenine are formed in an electrospray ion source and are resolved spatially in a differential mobility spectrometer before detection in a triple quadrupole mass spectrometer. Each isomer is gated in CV space before being trapped in the linear ion trap of the modified mass spectrometer, where they are irradiated by the tuneable output of an optical parametric oscillator and undergo photodissociation to form charged fragments with m/z 119, 109, and 94. The photon-normalised intensity of each fragmentation channel is measured and the action spectra for each DMS-gated tautomer are obtained. Our analysis of the action spectra, aided by calculated vibronic spectra and thermochemical data, allow us to assign the two signals in our measured ionograms to specific tautomers of protonated adenine.

John PC, Kim S, Paton RS. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data.The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used.Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors.Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity.

Defects as catalytic sites for the oxygen evolution reaction in Earth-abundant MOF-74 revealed by DFT

The oxygen evolution reaction (OER) is the bottleneck of hydrogen production via water splitting and understanding electrocatalysts at atomic level becomes paramount to enhance the efficiency of this process.

tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes

We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm.

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict-this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

Quasi-degenerate states and their dynamics in oxygen deficient reducible metal oxides

The physical and chemical properties of oxides are defined by the presence of oxygen vacancies. Experimentally, non-defective structures are almost impossible to achieve due to synthetic constraints. Therefore, it is crucial to account for vacancies when evaluating the characteristics of these materials. The electronic structure of oxygen-depleted oxides deeply differs from that of the native forms, in particular, of reducible metal oxides, where excess electrons can localize in various distinct positions. In this perspective, we present recent developments from our group describing the complexity of these defective materials that highlight the need for an accurate description of (i) intrinsic vacancies in polar terminations, (ii) multiple geometries and complex electronic structures with several states attainable at typical working conditions, and (iii) the associated dynamics for both vacancy diffusion and the coexistence of more than one electronic structure. All these aspects widen our current understanding of defects in oxides and need to be adequately introduced in emerging high-throughput screening methodologies.

Catalysis-Hub.org, an open electronic structure database for surface reactions

We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles

The frontiers between homogeneous and heterogeneous catalysis are progressively disappearing.