bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and characterization of nanoscale composite particles formed by 2D layers of Cu-Fe sulfide and Mg-based hydroxide
Authors: Mikhlin Yuri , Borisov Roman , Vorobyev Sergey , Tomashevich Yevgeny , Romanchenko Alexander , Likhatski Maxim , Karacharov Anton , Bayukov Oleg , Knyazev Yurii , Velikanov Dmitriy , Zharkov Sergey , Krylov Alexander , Krylova Svetlana , Nemtsev Ivan
Published: February 2022

Two-dimensional phenomena are attracting enormous interest at present and the search for novel 2D materials is very challenging. We propose here the layered material valleriite composed of altering atomic sheets of Cu-Fe sulfide and Mg-based hydroxide synthesized via a simple hydrothermal pathway as particles of 50-200 nm in the lateral size and 10-20 nm thick. The solid products and aqueous colloids prepared with various precursor ratios were examined using XRD, TEM, EDS, X-ray photoelectron spectroscopy (XPS), reflection electron energy loss spectroscopy (REELS), Raman, Mössbauer, UV-vis-NIR spectroscopies, magnetic, dynamic light scattering, zeta potential measurements. The material properties are largely determined by the narrow-gap (less than 0.5 eV) sulfide layers containing Cu+ and Fe3+ cations, monosulfide and minor polysulfide anions but are strongly affected by the hydroxide counterparts. Particularly, Fe distribution between sulfide (55-90%) and magnesium hydroxide layers is controlled through insertion of Al into the hydroxide part and by Cr and Co dopants entering both layers. Room-temperature Mössbauer signals of paramagnetic Fe3+ transformed to several Zeeman sextets with hyperfine magnetic fields up to 500 kOe in the sulfide layers at 4 K. Paramagnetic or more complicated characters were observed for valleriites with higher and lower Fe concentrations in hydroxide sheets, respectively. Valleriite colloids showed negative zeta potentials, suggesting negative electric charging of the hydroxide sheets, and optical absorption maxima between 500 nm and 700 nm, also depended on the Fe distribution. The last features observed also in the REELS spectra may be due to localized surface plasmon or, more likely, quasi-static dielectric resonance. The tunable composition, electronic, magnetic, optic and surface properties highlight valleriites as a rich platform for novel 2D composites promising for numerous applications.
Photophysical Properties of Benzophenone Based TADF Emitters in Relation to Their Molecular Structure
Authors: Bas Ekin Esme , Ulukan Pelin , Monari Antonio , Aviyente Viktorya , Catak Saron
Published: February 2022

Thermally activated delayed fluorescence (TADF) materials are commonly used in various apparatus, including organic light emitting device (OLED)-based displays, as they remarkably improve the internal quantum efficiencies (IQE). Alt-hough there is a wide range of donor-acceptor based compounds possessing TADF properties, in this computational study we investigated TADF and some non-TADF chromophores, containing benzophenone or its structural derivatives as the acceptor core, together with various donor moieties. Following the computational modeling of the emitters, several excited state properties, such as the absorption spectra, singlet-triplet energy gaps (∆EST), natural transition orbitals (NTO) and the topological ΦS indices, have been computed. Along with the donor-acceptor torsion angles and spin-orbit coupling (SOC) values, these descriptors have been utilized to investigate potential TADF efficiency. Our study has shown that on the one hand, our photophysical/structural descriptors and computational methodologies predict the experi-mental results quite well, on the other hand, our extensive benchmark can be useful to pinpoint the most promising func-tionals and descriptors for the study of benzophenone based TADF emitters.
Non-innocent hybrid cycloolefin ligands for palladium/olefin cooperative catalysis
Authors: Zheng Ya-Xin , Jiao Lei
Published: February 2022

Catalyst design is a key research area in modern synthetic chemistry. Engineering of molecular catalysts in homogeneous catalysis brings about new catalytic modes that enable efficient synthetic transformations. In this regard, design of novel ligands for transition-metal catalysis have played a major role. Olefins have been emerging as a significant class of steering ligands in transition-metal catalysis, which are known to serve as innocent ligands that provide electronic and steric tuning of the metal center. However, it is unknown whether a distinct type of olefin ligand that contrasts the common innocent feature is possible. Here we show that a novel type of heteroatom-cycloolefin hybrid ligand functions as a non-innocent ligand in palladium catalysis, which exhibits covalent catalytic function that enables efficient ipso,ortho-difunctionalization of iodoarenes. Detailed mechanistic study revealed that this ligand undergoes reversible covalent bonding between the substrate and the cycloolefin unit, which forms key organopalladium intermediates to enable new reactivity. Our results demonstrate a novel design concept that utilizes unstrained cycloolefin as a covalent catalytic module, opening an avenue to a more general transition metal/olefin cooperative catalysis.
Soot and charcoal are reservoirs of extracellular DNA
Authors: Jelavic Stanislav , Thygesen Lisbeth Garbrecht , Magnin Valerie , Findling Nathaniel , Müller Sascha , Meklesh Viktoriia , Sand Karina Krarup
Published: February 2022

Soot and charcoal are carbonaceous materials widespread in the environment where they readily can come in contact with extracellular DNA shed from organisms. The adsorption at a surface protects DNA from chemical and biological degradation. However, a comprehensive insight into DNA adsorption at soot and charcoal is lacking. We measured DNA adsorption capacity at soot and charcoal as a function of solution composition, time and DNA length. We observed that the capacity for DNA is the highest at low pH, it increases with solution concentration and cation valency and that the activation energy for DNA adsorption at both soot and charcoal is ~50 kJmol-1. We demonstrate how the interaction between DNA and soot and charcoal partly occurs via terminal basepairs, suggesting that, besides electrostatic forces, hydrophobic interactions play an important role in binding. The importance of hydrophobic interactions increases as the hydrophobicity of a surface increases. Such strong binding and hydrophobic interactions need to be taken into account to improve DNA extraction protocols and for mitigation of the spread of antibiotic resistance genes in environmental matrices that contain soot and charcoal such as aerosol, wastewater and topsoil.
Origin of Increased Reactivity in Rhenium-Mediated Cycloadditions of Tetrazines
Authors: Turlik Aneta , Houk Kendall N. , Svatunek Dennis
Published: February 2022

Pyridyl tetrazines coordinated to metals like rhenium have been shown to be more reactive in [4+2] cycloadditions than their un-complexed counterparts. Using density functional theory (DFT) calculations, and in particular distortion/interaction and energy de-composition analysis, we analyze the factors that contribute to this increase in selectivity. The reaction of the complexed tetrazine has a more favorable interaction energy compared to the uncomplexed tetrazine, and the main contributor to this favorable interaction energy an increase in orbital interactions through a lowering of the LUMO in the complexed tetrazine. Additionally, the high regioselectivity of the reaction is shown to be due to a greater degree of charge stabilization in the transition state leading to the major prod-uct. This study suggests that the energy of the FMOs is a good indicator of reactivity in these reactions, and the enhanced [4+2] reactivity upon complexation adds another tool to the toolbox of click reactions.
Employing artificial neural networks to find reaction coordinates and pathways for self-assembly
Authors: Appeldorn Jörn , Lemcke Simon , Speck Thomas , Nikoubashman Arash
Published: February 2022

Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process through two distinct half-bound states, which are correctly identified by the neural net. We exploit this latent space representation to construct a Markov state model for predicting the four molecular conformations and transition rates. Our work opens up new avenues for the computational modeling of multi-step and hierarchical self-assembly, which has proven challenging so far.
Electron Density Geometry and the Quantum Theory of Atoms in Molecules
Authors: Wilson Timothy , Alexandrova Anastassia , Eberhart Mark
Published: February 2022

A novel form of charge density analysis, that of isosurface curvature redistribution, is formulated and applied to the toy problem of carbonyl oxygen activation in formaldehyde. The isosurface representation of the electron charge density allows us to incorporate the rigorous geometric constraints of closed surfaces towards the analysis and chemical interpretation of the charge density response to perturbations. Visual inspection of 2D isosurface motion resulting from applied external electric fields reveals how isosurface curvature flows within and between atoms, and that a molecule can be uniquely and completely partitioned into chemically significant regions of positive and negative curvature. These concepts reveal that carbonyl oxygen activation proceeds primarily through curvature and charge redistribution within rather than between Bader atoms. Using gradient bundle analysis—the partitioning of formaldehyde into infinitesimal volume elements bounded by QTAIM zero flux surfaces—the observations from visual isosurface inspection are verified. The results of the formaldehyde carbonyl analysis are then shown to be transferable to the substrate carbonyl in the ketosteroid isomerase enzyme, laying the groundwork for extending this approach to the problems of enzymatic catalysis.
De novo drug design using reinforcement learning with graph-based deep generative models
Authors: Romeo Atance Sara , Viguera Diez Juan , Engkvist Ola , Olsson Simon , Mercado Rocío
Published: February 2022

Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models. Here, we propose a graph-based deep generative model for de novo molecular design using reinforcement learning. We demonstrate how the reinforcement learning framework can successfully fine-tune the generative model towards molecules with various desired sets of properties, even when few molecules have the goal attributes initially. We explored the following tasks: decreasing/increasing the size of generated molecules, increasing their drug-likeness, and increasing protein-binding activity. Using our model, we are able to generate 95% predicted active compounds for a common benchmarking task, outperforming previously reported methods on this metric.
Sustainable polyesters via direct functionalization of lignocellulosic sugars
Authors: Manker Lorenz , Dick Graham , Demongeot Adrien , Hédou Maxime , Rayroud Christèle , Rambert Thibault , Jones Marie , Sulaeva Irina , Leterrier Yves , Potthast Antje , Maréchal Francois , Michaud Véronique , Klok Harm-Anton , Luterbacher Jeremy
Published: February 2022

The development of sustainable plastics from abundant renewable feedstocks has been limited by the complexity and efficiency of their production as well as their lack of competitive material properties. Here, we demonstrate the direct transformation of the hemicellulosic fraction of non-edible biomass into a diester plastic precursor at 83% yield (95% from commercial xylose) during integrated plant fractionation with glyoxylic acid. Melt polycondensation of the resulting xylose-based diester with a range of aliphatic diols led to high-molecular weight amorphous polyesters with combined high glass transition temperatures, tough mechanical properties, and strong gas barriers, which could be processed by injection-molding, thermoforming, and 3D-printing. These polyesters could then be chemically recycled from mixed plastic waste streams or digested under biologically relevant conditions. The transformation’s simplicity led to projected costs that were competitive with fossil alternatives and significantly reduced associated greenhouse gas emissions, especially if glyoxylic acid was sourced from CO2.
Origin of The Failure of Density Functional Theories in Predicting Inverted Singlet-Triplet Gaps
Authors: Ghosh Soumen , Bhattacharyya Kalishankar
Published: February 2022

Recent experimental and theoretical studies have shown several new organic molecules that violate Hund’s rule and have the first singlet excited state lower in energy than the first triplet excited state. While many correlated single reference wave function methods have successfully predicted excited state energetics of these low-lying states, conventional linear-response time-dependent density functional theory (LR-TDDFT) fails to predict the correct excited state energy ordering. Herein, we have shown that it is possible to get inverted singlet-triplet gaps within the density functional theory framework by taking into account correlation contributed by double excitations and choosing correct exchange-correctional functional. Going beyond Kohn-Sham density functional theory (KS-DFT), we have demonstrated that a combined wave function and density functional method resulting in multiconfiguration pair-density functional theory (MC-PDFT), in some cases, can predict inverted singlet-triplet gaps. Consequently, we have identified that both the missing doubly excited configurations and the form of the exchange-correlation functionals are the foremost grounds for the failure of the LR-TDDFT method. We have also compared the accuracy of single reference correlated wave function methods for these low-lying singlet and triplet excited states to multireference second-order perturbation theory.
Modeling Spectral Tuning in Red Fluorescent Proteins Using the Dipole Moment Variation upon Excitation
Authors: Khrenova Maria , Mulashkin Fedor , Nemukhin Alexander
Published: February 2022

We describe a model for spectral tuning in red fluorescent proteins (RFPs) based on the relation between an electronic structure descriptor, the dipole moment variation upon excitation (DMV), and the excitation energy of a protein. This approach aims to overcome a problem of accurate prediction of excitation energies in RFPs, which span a very narrow window of band maxima. The latter roughly corresponds to an energy gap of 0.1 eV, which is comparable with typical errors in calculations of the excitation energy by conventional quantum chemistry methods. In this work, we demonstrate a strong quantitative correlation between DMV values, obtained computationally with modest efforts, and excitation energies ΔEex at the experimental excitation band maxima for a series of RFPs with the bands between 570 and 605 nm. Protein models are constructed by motifs of the relevant crystal structures, and atomic coordinates are optimized in quantum mechanics/molecular mechanics (QM/MM) calculations with QM-subsystems composed of large chromophore-containing regions. DMV values are evaluated with the electron density computed at the TDDFT level using several functionals and basis sets. We demonstrate that the results obtained with the CAM-B3LYP, BHHLYP and M06-2X functionals demonstrate favorable correlations between DMV and ΔEex with the mean absolute error less than 0.01 eV. Taking into account the solid theoretical grounds of the relation between the DMV and the excitation energy in fluorescent proteins, the described modeling strategy presents a rational tool for spectral tuning in these efficient markers for in vivo imaging.
A Photoswitchable Ru cis-Dihydride Catalyst Accessed through Atypical Metal-Ligand Cooperative H2 Activation: Photo-Enhanced Acceptorless Alcohol Dehydrogenation
Authors: Fanara Paul , Vigneswaran Vipulan , Gunasekera Parami , MacMillan Samantha , Lacy David
Published: February 2022

Dehydrohalogenation of pyridine-derived pincer ruthenium complexes often lead to dearomatized moieties, such as in Mil-stein’s PNN-Ru(CO)(Cl)(H) (1Py) catalyst. Thus, we were surprised to find an aromatized 3-N,C,P binding mode in the lutidine-derived bidentate analog [{LutP`}Ru(CO)(H)(PPh3)] (2), instead of a dearomatized compound, upon dehydrohalo-genation of [{LutP}Ru(CO)(Cl)(H)(PPh3)] (1). The reaction of 2 with H2 results in formation of a cis-dihydride [{LutP}Ru(CO)(H)2(PPh3)] (3) and labeling studies confirm cooperative metal-ligand activation. 3 exhibits reversible photo-chemistry, forming another cis-dihydride isomer (4). The lability of 4 toward ligand substitution was leveraged to demon-strate a unique example of photoswitchable H2 production via acceptorless alcohol dehydrogenation. Labeling studies impli-cate metal-ligand cooperative (MLC) processes during the photocatalytic reaction, but they appear to be off-path processes based on our mechanistic study of the system. The latter emphasizes that aromatization/dearomatization may not be neces-sary for acceptorless transformations, which is generally consistent with several contemporary studies on analogous Ru cata-lysts.
Functional Group Resolved Nuclear Spin Relaxation in Porous Media
Authors: Robinson Neil , May Eric , Johns Michael
Published: February 2022

Understanding solid-fluid interactions within porous materials is critical for their efficient utilisation across chemical reaction and separation processes. However, detailed characterisation of interfacial phenomena within such systems is hampered by their optically opaque nature. Motivated by the need to bridge this capability gap, we detail here the application of low magnetic field 2D 1H nuclear spin relaxation measurements as a non-invasive probe of sorbate/sorbent interactions, exploring the relaxation characteristics exhibited by liquid adsorbates confined to a model mesoporous silica. For the first time, we demonstrate the capacity of such measurements to distinguish functional group-specific relaxation phenomena across a diverse range of protic adsorbates of wide importance as solvents, reagents, and hydrogen carriers, with distinct relaxation environments assigned to the alkyl and hydroxyl moieties of the confined liquids. Uniquely, this relaxation behaviour is shown to correlate with adsorbate acidity, with the observed relationship rationalised on the basis of surface-adsorbate proton exchange dynamics.
JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields
Authors: Kaymak Mehmet Cagri , Rahnamoun Ali , O'Hearn Kurt A. , van Duin Adri C. T. , Merz Jr. Kenneth M. , Aktulga Hasan Metin
Published: February 2022

Molecular dynamics (MD) simulations ease the study of the chemistry of interest. While classical models that governs the interaction of the atoms lack reactivity, the quantum mechanics based methods increase the computational cost drastically. ReaxFF fills the gap between these two ends of the spectrum by allowing bond breaking and dynamic charges. To achieve realistic simulations using ReaxFF, the model parameters need to be carefully tuned based on the training data created using more accurate but expensive methods. The current optimization methods focus on black-box optimization methods such as genetic algorithms (GAs), Monte-Carlo methods and covariance matrix adaptation evolutionary strategy (CMA-ES). Due to the stochastic behavior of these methods, the training requires hundreds of thousands of error evaluations for complex training tasks and each error evaluation usually involves energy minimization of the many molecules in the training data. In this work, we propose a novel approach which takes advantage of the modern tools developed for machine learning to improve the training efficiency of the force field development for ReaxFF. By calculating the gradients of the loss function using JAX library developed by Google, we are able to use well studied local optimization methods such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) method. To further decrease the training time, we skip the energy minimization of the molecules during the local optimization since the parameters of the model are not likely to change drastically during a local search. JAX allows us to easily parallelize the error evolution by compiling the code for CPUs, GPUs or TPUs. With the help of the modern accelerators and the gradient information, we are able to decrease the training time from days to minutes.
JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields
Authors: Kaymak Mehmet Cagri , Rahnamoun Ali , O'Hearn Kurt A. , van Duin Adri C. T. , Merz Jr. Kenneth M. , Aktulga Hasan Metin
Published: February 2022

Molecular dynamics (MD) simulations facilitate the study of physical and chemical processes of interest. Traditional classical MD models lack reactivity to explore several important phenomena; while quantum mechanical (QM) models can be used for this purpose, they come with steep computational costs. The reactive force field (ReaxFF) model bridges the gap between these approaches by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data, typically with QM accuracy. Existing parameter optimization methods for ReaxFF consist of black-box techniques using genetic algorithms or Monte-Carlo methods. Due to the stochastic behavior of these methods, the optimization process can require millions of error evaluations for complex parameter fitting tasks, significantly hampering the rapid development of high quality parameter sets. In this work, we present JAX ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable extremely fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, we are able to utilize highly effective local optimization methods, such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) methods. As a result of the performance portability of JAX, JAX-ReaxFF can execute efficiently on multi-core CPUs, GPUs (or even TPUs). By leveraging the gradient information and modern hardware accelerators, we are able to decrease parameter optimization time for ReaxFF from days to mere minutes. JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling.
Cobalt-Electrocatalytic Hydrogen Atom Transfer for Functionalization of Unsaturated C–C Bonds
Authors: baran phil , Gnaim Samer , Bauer Adriano , Zhang Hai-Jun , Chen Longrui , Gannet Cara , Malapit Christian , Hill David , Vogt David , Tang Tianhua , Daley Ryan , Hao Wei , Quertenmont Mathilde , Beck Wesley , Kandahari Elya , Vantourout Julien , Echeverria Pierre-Georges , Abruna Hector , Blackmond Donna , Minteer Shelley , Reisman Sarah , Sigman Matthew
Published: February 2022

The study and application of transition metal hydrides (TMH) has been an active area of chemical research since the early 1960’s. The use of TMHs has been broadly bifurcated into fields focused on energy storage through the reduction of protons to generate hydrogen and in organic synthesis for the functionalization of unsaturated C–C, C–O, and C–N bonds. In the former instance, electrochemical means for driving such reactivity has been commonplace since the 1950’s. In contrast, the use of stoichiometric exogenous organic and metal-based reductants to harness the power of TMHs in synthetic chemistry remains the norm. In particular, Co-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often via a net hydrogen atom transfer (HAT). Here, we show how an electrocatalytic approach inspired by decades of energy storage precedent can be leveraged in the context of modern organic synthesis. Such an approach not only offers benefits in terms of sustainability and efficiency but also enables enhanced chemoselectivity and unique and tunable reactivity. Ten different reaction manifolds across dozens of substrates are thus exemplified, along with a detailed mechanistic and computational analysis of this scalable electrochemical entry into Co-H chemistry.
Identification of Atovaquone and Mebendazole as Repurposed Drugs with Antiviral Activity against SARS-CoV-2 (Version 6)
Authors: Ahmed Mahmoud , Farag Ayman , Boys Ian N. , Wang Ping , L. Eitson Jennifer , Ohlson Maikke B. , Fan Wenchun , McDougal Matthew B. , Menendez-Montez Ivan , Uyen Nhi Nguyen Ngoc Uyen Nhi Nguyen, , Mar Katrina , Ortiz Francisco , Young Kim Soo , Williams Noelle , lemoff Andrew Lemoff , DeBerardinis Ralph , W. Schoggins John , Sadek Hesham
Published: February 2022

Given the continuing heavy toll of the COVID-19 pandemic, therapeutic options for treatment are urgently needed. Here, we adopted a repositioning approach using in silico molecular modeling to screen FDA-approved drugs with established safety profiles for potential inhibitory effects against SARS-CoV-2. We used structure-based drug design to screen more than 2000 FDA approved drugs against SARS-CoV-2 main protease enzyme (Mpro) substrate-binding pocket. We additionally screened the top hits from both sites for potential covalent binding via nucleophilic thiol attack of Cys 145. High-scoring candidates were then screened for antiviral activity against infectious SARS-CoV-2 in a cell-based viral replication assay, and counter screened for toxicity. Promising candidates included atovaquone, mebendazole, ouabain, dronedarone, and entacapone, although atovaquone and mebendazole were the only two candidates with IC50s that fall within their therapeutic plasma concentration. In addition, we performed Mpro assays on the top hits, which demonstrated inhibition of Mpro by dronedarone (IC50 18 M), mebendazole (IC50 19 M) and entacapone (IC50 9 M). Atovaquone showed only modest Mpro inhibition, and thus we explored other potential antiviral mechanisms. Although atovaquone is a known DHODH inhibitor, we did not observe inhibition of DHODH by atovaquone at concentrations relevant to the SARS-CoV-2 IC50. Interestingly, metabolomic profiling of atovaquone treated cells demonstrated marked dysregulation of metabolites in the purine metabolism pathway. In summary, a number of our top hits from the in-silico screen demonstrated Mpro inhibitory activity associated with antiviral effects. Atovaquone and mebendazole are the most promising candidates targeting SARS-CoV-2 from our screen, however atovaquone did not significantly inhibit Mpro at therapeutically meaningful concentrations but may inhibit SARS-CoV-2 viral replication by targeting host purine metabolism.
The role of excited-state character, structural relaxation, and symmetry breaking in enabling delayed fluorescence activity in push-pull chromophores
Authors: Kimber Patrick , Goddard Pooja , Wright Iain , Plasser Felix
Published: February 2022

Thermally activated delayed fluorescence (TADF) is a current promising route for generating highly efficient light-emitting devices. However, the design process of new chromophores is hampered by the complicated underlying photophysics that requires a number of different pathways to be optimised simultaneously. In this work, four closely related donor-pi-acceptor-pi-donor systems have been investigated, two of which were synthesised previously, with the aim of elucidating their varying effectiveness for TADF. We, first, outline that neither the frontier orbitals nor the singlet-triplet gaps are sufficient in discriminating between the molecules. Subsequently, a detailed analysis of the excited states, performed at a correlated ab initio level, is shown highlighting the presence of a number of closely spaced singlet and triplet states of varying character. Five density functionals are benchmarked against this reference revealing dramatic changes in, both, excited state energies and wavefunctions following variations in the amount of Hartree-Fock exchange included. Excited-state minima are optimised in solution showing the crucial role of structural variations for stabilising locally excited and CT states and of symmetry breaking for producing a strongly emissive S1 state. More generally, this work shows how a detailed analysis of excited-state wavefunctions can provide critical new insight into excited-state electronic structure, helping to reveal the photophysics of existing push-pull chromophores and ultimately guiding the design of new ones.
Catalytic Resonance of Ammonia Synthesis by Dynamic Ruthenium Crystal Strain
Authors: Wittreich Gerhard , Liu Shizhong , Dauenhauer Paul , Vlachos Dionisios
Published: February 2022

Ammonia affords dense storage for renewable energy as a fungible liquid fuel provided it can be efficiently synthesized from hydrogen and nitrogen. In this work, the catalysis of ammonia synthesis was computationally explored beyond the Sabatier limit by dynamically straining a ruthenium crystal (± 4%) at the resonant frequencies (10^2 to 10^5+ Hz) of N2 surface dissociation and hydrogenation. Density functional theory calculations at different strain conditions indicated that the energies of NHx surface intermediates and transition states scale linearly with that of the surface nitrogen on terraces, allowing the description of ammonia synthesis at a continuum of strain conditions. A microkinetic model including multiple sites and surface diffusion between step and Ru(0001) terrace sites of varying ratios for nanoparticles of differing size revealed that dynamic strain yields catalytic ammonia synthesis conversion and turnover frequency comparable to industrial reactors (400 ⁰C, 200 atm) but at lower temperature (320 ⁰C) and an order of magnitude lower pressure (20 atm).
CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry
Authors: Wang Anthony , Mahmoud Mahamad Salah , Czasny Mathias , Gurlo Aleksander
Published: February 2022

Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike.
A study of boosting molecular descriptors with quantum-derived features in prediction of maximum emission wavelengths of chromophores.
Authors: Fliszkiewicz Bartłomiej
Published: February 2022

The following research assesses the capability of machine learning in predicting maximum emission wavelengths of organic compounds. The predictions are based on molecular descriptors and fingerprints widely applied in cheminformatics. In an effort to further improve accuracy, developed machine learning models were enriched with quantum mechanics derived features. Multi linear, gradient boosting and random forest regressions were applied. Computers were trained and tested with database of experimental data of optical properties.
Chiral Motifs in Interpenetrated Metal-Organic Frameworks Formed from Achiral Tetrahedral Ligands
Authors: Wen Qiang , di Gregorio Maria Chiara , Shimon Linda J. W. , Alexandrov Eugeny V. , Proserpio Davide M. , van der Boom Milko E. , Lahav Michal
Published: February 2022

We demonstrate the formation of highly interpenetrated frameworks. An interesting observation is the presence of very large adamantane-shaped cages in a single network, making these crystals new entries in the collection of diamondoid-type metal-organic frameworks (MOFs). The frameworks were constructed by assembling tetrahedral pyridine ligands and copper dichloride. Currently, the networks’ degree of interpenetration is among the highest reported and increases when the size of the ligand is increased. Usually, highly interpenetrated frameworks have low surface contact areas. In contrast, in our systems, the voids take up to 63% of the unit cell volume. The frameworks are chiral and formed from achiral components. The chirality is manifested by the coordination chemistry frameworks around the metal center, the structure of the helicoidal channels and the motifs of the individual networks. Channels with both chiralities are present within the unit cells. This phenomenon shapes the walls of the channels, which are composed of 10, 16, or 32 chains correlated to the degree of interpenetration 10-, 16- and 32-fold. By changing the distance between the center of the ligand and the coordination moieties, we succeeded to tune the diameter of the channels. Relatively large channels were formed, having diameters up to 31.0 Å × 14.8 Å.
Nonadiabatic quantum dynamics of the coherent excited state intramolecular proton transfer of 10-hydroxybenzo[h]quinoline
Authors: Picconi David
Published: February 2022

The photoinduced nonadiabatic dynamics of the enol-keto isomerization of 10-hydroxybenzo[h]quinoline (HBQ) are studied computationally using high dimensional quantum dynamics. The simulations are based on a diabatic vibronic coupling Hamiltonian, which includes the two lowest ππ* excited states and a nπ* state, which has high energy in the Franck-Condon zone, but significantly stabilizes upon excited state intramolecular proton transfer. A procedure, applicable to large classes of excited state proton transfer reactions, is presented to parametrize this model using potential energies, forces and force constants, which, in this case, are obtained by time-dependent density functional theory. The wave packet calculations predict a time scale of 10-15 fs for the photoreaction, and reproduce the time constants and the coherent oscillations observed in time-resolved spectroscopic studies performed on HBQ. In contrast to the interpretation given to the most recent experiments, it is found that the reaction initiated by 1ππ* ← S0 photoexcitation proceeds essentially on a single potential energy surface, and the observed coherences bear signatures of Duschinsky mode-mixing along the reaction path. The dynamics after the 2ππ* ← S0 excitation are instead nonadiabatic, and the nπ* state plays a major role in the relaxation process. The simulations suggest a mainly active role of the proton in the isomerization, rather than a passive migration assisted by the vibrations of the benzoquinoline backbone.
Direct synthesis of barium titanium oxyhydride promising for electrode material with hydrogen permeability
Authors: Uchimura Tasuku , Takeiri Fumitaka , Okamoto Kei , Saito Takashi , Kamiyama Takashi , Kobayashi Genki
Published: February 2022

Barium titanium oxyhydride BaTiO3–xHx is a promising functional material that exhibits H–/e– mixed conduction and catalytic activity. Here we firstly report a direct synthesis of BaTiO3–xHx by mechanochemical method. The prepared polycrystalline sample was able to work as electrodes exhibiting hydrogen pearmeability.
All Are Aromatic: A 3D Globally Aromatic Cage Containing Five Types of 2D Aromatic Macrocycles
Authors: Ren Longbin , Han Yi , Hou Xudong , Ni Yong , Wu Jishan
Published: February 2022

The studies on three-dimensional (3D) aromaticity have been mainly focused on fullerenes, boron-based deltahedrons/clusters, metal clusters and polyhedral hydrocarbons, but there are very limited researches on the fundamental aromaticity rule for 3D fully π-conjugated molecules. Herein, we report a π-conjugated molecular cage in which two aromatic porphyrin units are bridged by four thiophene-based arms. Two-electron chemical oxidation leads to a 3D globally aromatic cage with a C2 symmetry according to X-ray diffraction, NMR, electronic absorption spectra, and theoretical calculations. Detailed magnetic shielding response analysis along different axes reveal that all the possible five types of two-dimensional (2D) macrocycles in the cage skeleton are aromatic and follow Hückel rule. The switch from localized aromaticity to global aromaticity upon chemical oxidation is also observed in a tricyclic model compound. The study indicates that to attain 3D global aromaticity in a molecular cage, all the formally available π-conjugated macrocycles should be 2D aromatic.
Improving the Accuracy of AutoDock Vina by Changing the Empirical Parameters
Authors: Pham T. Ngoc Han , Nguyen Trung Hai , Tam Nguyen Minh , Vu Thien Y , Pham Nhat Truong , Huy Nguyen Truong , Mai Binh Khanh , Tung Nguyen Thanh , Pham Minh Quan , Vu Van V. , Ngo Son Tung
Published: February 2022

According to the previous benchmark, Autodock Vina (Vina) achieved a very high successful-docking rate, p ̂, but give a rather a low correlation coefficient, R, for binding affinity with respect to experiment. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is a main objective of docking simulations. The accuracy of Vina likely depends on the empirical parameters, which include the Gaussian steric interaction, repulsion, hydrophobic, hydrogen bond, and rotation metrics. In this context, we evaluated the dependence of Vina accuracy upon empirical parameters. Although changing of six parameters alters the obtained R values, the gauss2 and rotation terms form more effects. The p ̂ terms are sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Therefore, three sets of empirical parameters were proposed as well as more to modestly focus on R (set1), modestly focus on p ̂ (set3), and keep a tradeoff between R and p ̂. The testing study over 800 complexes indicated that the Vina with proposed sets of parameters can provide more accurate results since forming the larger R value (R_set1=0.556±0.025) compared with the original one (R_Default=0.493±0.028) and Vina version 1.2 (R_(Vina 1.2)=0.503±0.029). Besides, the testing study over 48 biological targets indicated that the modified Vina can be applied more widely compared with the default package. These newly proposed parameters achieved a higher correlation coefficient and reasonable correlation coefficients (R>0.500) for at least 32 targets, whereas the default parameters provided by the original Vina gave only 31 targets with at least 0.500 correlation. In addition, validation calculations for 1315 complexes obtained from the version 2019 of PDBbind refined structures suggested that set1 of parameters are more appropriate than the other parameters (R_set1=0.621±0.016) compared with the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes probably enhance the ranking of ligand-binding affinity using Autodock Vina.
Copper Mediated C(sp2)–H Sulfonylation of Aldehydes using a Catalytic Transient Imine Directing Group
Authors: Higham Joe , Bull James A
Published: February 2022

The copper mediated β–C(sp2)–H sulfonylation of benzaldehydes with sulfinate salts is accomplished using β-alanine as a catalytic transient directing group. A broad range of sulfonylated benzaldehydes are prepared us-ing copper fluoride as both copper source and oxidant. Both b-(ortho) and g-(peri)-sulfonylation are demon-strated. Mechanistic studies indicate the turnover limiting step to be a concerted asynchronous C–H cleavage via a Wheland-type transition state.
In silico all-atom approach to thermophoresis in aqueous solutions
Authors: Diaz-Marquez Alejandro , STIRNEMANN Guillaume
Published: February 2022

When liquid mixtures containing 2 or more constituants are subject to thermal gradients, their respective concentrations are not homogeneous anymore and usually reach a steady-state where certain molecules accumulate in cold regions while others are rather more concentrated in hot regions, a phenomenon known as thermophoresis. Also called the Soret effect, it has been evidenced in a variety of systems and has many practical applications, as well as implications in the context of the origins of life where it could have served as way to accumulate otherwise dilute precursors in the absence of biological compartments. While it was discovered in the 19th century, a complete theoretical picture able to explain this effect is still missing and phenomenological approaches are often employed to account for the experimental observations. In particular, the amplitude of the resulting concentration-gradients (quantified by the Soret coefficient) depends on many factors and is not straightforwardly rationalized. All-atom molecular dynamics simulations appear as an exquisite tool to shed light on the molecular origins for this phenomenon in molecular systems. Although they have already been applied in such contexts in the past, they are not routinely employed, in particular in the case of dilute aqueous solutions, which are of particular relevance in the context of reactant accumulation in aqueous environments, but which pose significant practical challenges. Here, we propose a robust all-atom MD approach to tackle thermophoresis in dilute realistic solutions at the molecular level. We propose to rely on a recent enhanced heat-exchange algorithm to generate temperature-gradients, which, to the best of our knowledge, has not yet been applied in this context. We carefully assess the convergence of thermophoretic simulations in dilute aqueous solutions. We show that simulations typically need to be propagated on very long timescales (hundreds of nanoseconds) to generate reliable concentration-gradients. We find that the magnitude of the temperature gradient and the box sizes have little effect on the measured Soret coefficients. Practical guidelines are derived from such observations. Provided with this reliable setup, we discuss the results of thermophoretic simulations on several examples of molecular, neutral solutes, which we find in very good agreement with experimental measurements regarding the concentration-, mass-, and temperature-dependence of the Soret coefficient.
Online tools to easily build augmented reality models of biomolecules that run right on the web
Authors: Cortés Rodríguez Fabio , Dal Peraro Matteo , Abriata Luciano
Published: February 2022

Several groups developed in the last years augmented and virtual reality (AR/VR) programs and apps to visualize 3D molecules, most rather static, limited in content, and requiring software installs, some even requiring specialized hardware. During the Covid-19 pandemic, our team launched moleculARweb (https://molecularweb.epfl.ch), a website that offers interactive content for chemistry and structural biology education through web-based AR that works on consumer devices like smartphones, tablets and laptops. The website quickly got thousands of student and teacher users, a substantial fraction of them accessing from their homes given the pandemic. Teachers have been increasingly requesting more biological macromolecules to be available in AR, a demand that we cannot satisfy individually. Therefore, to allow them to build their own material, and also to help us expedite development of activities, we built a web interface where any user can build any online AR experience in few steps starting from a PDB structure or from virtual objects/scenes exported from VMD. We here briefly describe the tool, that is accessible at https://molecularweb.epfl.ch/pages/pdb2ar.html.
Diamond Membrane Production: The Critical Role of Radicals in the Non-Contact Electrochemical Etching of sp2 Carbon
Authors: Tully Josh , Braxton Emily , Cobb Sam , Breeze Ben , Markham Matthew , Newton Mark , Rodriguez Paramaconi , Macpherson Julie
Published: February 2022

Sub-micrometre single crystal diamond membranes are of huge importance for next generation optical, quantum and electronic device applications. Electrochemical etching has proven a critical step in the production of such membranes. Etching is used to selectively remove a very thin layer of sub-surface sp2 bonded carbon, prepared by ion implantation in bulk diamond, releasing the diamond membrane. Due to the nanosized dimensions, etching is carried out using non-contact electrochemistry in low conductivity solutions (bipolar arrangement) which whilst effective, results in extremely slow etch rates. In this work, a new method of non-contact electrochemical etching is presented which uses high conductivity, high concentration, fully dissociated aqueous electrolytes. Careful choice of the electrolyte anion results in significant improvements in the sp2 carbon etch rate. In particular, we show both chloride and sulfate electrolytes increase etch rates significantly (up to ×40 for sulfate) compared to the current state-of-the-art. Electron paramagnetic resonance experiments, recorded after the electrode potential has been switched off, reveal sizeable hydroxyl radical concentrations at timescales > 10^7 longer than their lifetime (< microsecond). These measurements highlight the importance of electrochemically initiated, solution chemistry radical generation and regeneration pathways in high concentration sulfate and chloride solutions for nano-etching applications.