bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid structural analysis of minute quantities of organic solids by exhausting 1H polarization in solid-state NMR spectroscopy at fast MAS
Authors: Yan Zhiwei , Zhang Rongchun
Published: February 2022

Solid-state nuclear magnetic resonance (NMR) is a powerful and indispensable tool for structural and dynamic studies of various challenging systems. Nevertheless, it often suffers from significant limitations due to the inherent low signal sensitivity when low- nuclei are involved. Herein, we report an efficient solid-state NMR approach for rapid and efficient structural analysis of minute amounts of organic solids. By encoding staggered chemical shift evolution in the indirect dimension and staggered acquisition in 1H dimension, a proton-detected homonuclear 1H/1H and heteronuclear 13C/1H chemical shift correlation (HETCOR) spectrum can be obtained simultaneously in a single experiment at fast magic-angle-spinning (MAS) conditions with barely increasing experimental time, compared to conventional proton-detected HETCOR experiment. We establish that abundant 1H polarization can be efficiently manipulated and fully utilized in proton-detected solid-state NMR spectroscopy for extraction of more critical structural information and thus reduction of total experimental time.
Multi-fidelity prediction of molecular optical peaks with deep learning
Authors: Greenman Kevin , Green William , Gómez-Bombarelli Rafael
Published: February 2022

Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties.
Flexible boundary layer using exchange for embedding theories. II. QM/MM dynamics of the hydrated electron
Authors: Shen Zhuofan , Peng Shaoting , Glover William
Published: February 2022

The FlexiBLE embedding method introduced in the preceding companion paper [Z. Shen and W. J. Glover, J. Chem. Phys. X, X (2021)] is applied to explore the structure and dynamics of the aqueous solvated electron at an all-electron density functional theory QM/MM level. Compared to a one-electron mixed quantum/classical description, we find the dynamics of the many-electron model of the hydrated electron exhibits enhanced coupling to water OH stretch modes. Natural Bond Orbital analysis reveals this coupling is due to significant population of water OH σ* orbitals, reaching 20%. Based on this, we develop a minimal frontier orbital picture of the hydrated electron involving a cavity orbital and important coupling to 4-5 coordinating OH σ* orbitals. Implications for the interpretation of the spectroscopy of this interesting species are discussed.
Using Data Science to Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources
Authors: Kariofillis Stavros , Jiang Shutian , Żurański Andrzej , Gandhi Shivaani , Martinez Alvarado Jesus , Doyle Abigail
Published: February 2022

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because non-standardized sets of aryl bromides are used in scope evaluation. Herein we report a Ni/photoredox-catalyzed alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. We describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing the scope examples from published Ni/photoredox methods on this chemical space, we identify areas of sparse coverage and high/low yields, enabling comparisons between prior art and this method. We demonstrate that the systematically-selected scope of aryl bromides can be used to quantify population-wide reactivity trends with supervised ML.
Dual-State Emission in Molecular Rotors with Reorientable Benzotriazole Acceptors
Authors: Rodríguez-Cortés Lizbeth , Hernández Federico , Rodríguez Mario , Toscano Rubén , Jiménez-Sánchez Arturo , Crespo-Otero Rachel , Rodríguez-Molina Braulio
Published: February 2022

Dual-State Emission (DSE) of organic molecules in both solution and solid-state is an elusive property that has recently drawn significant interest. In this work, we report two molecular rotors 1 and 2 with exceptional photoluminescence (PL) in solution (ФPL = 0.53 and ФPL = 0.43) and in the solid state (ФPL = 0.92 and ФPL = 0.84). To the best of our knowledge, this is the first report containing Donor-pi-Acceptor-pi-Donor (D-pi-A-pi-D) compounds with high quantum yields in both states that features a rotary acceptor. Furthermore, we describe the marked variations in PL in solution upon polarity and viscosity changes. TD-DFT computations indicate that these changes are facilitated by the fast molecular rotation in these molecules. The symmetry (or lack of it) in 1 and 2 plays a crucial role in producing noticeable differences in the X-ray crystal arrays with variations in the solid-state PL. We attribute this DSE behavior to the favorable combination of the D--A--D architecture and the twisted conformations adopted in the solid state.
Additive Color Mixing of Semi-Transparent Laminated Tandem Type Polymer Light-Emitting Diodes
Authors: Morimoto Masahiro , Ozawa Yuya , Naka Shigeki , Okada Hiroyuki
Published: February 2022

Polymer light-emitting diodes (PLEDs) are generally fabricated using the wet process. Although it is difficult to stack multiple layers by the wet process, it is compatible with the lamination process that can be used to fabricate multi-layer tandem type devices. We fabricate tandem type PLEDs composed of two transparent substrates using the lamination process. Stacked red and green PLEDs exhibit additive color mixed emission resulting in orange emission. By careful selection of materials, similar emission is observed from both substrates of the semi-transparent tandem PLEDs because the over-layer does not absorb the emission from the under-layer.
Selective Crystallization of Four Bis(phthalocyaninato)lanthanoid(III) Polymorphs
Authors: Mullenax Maegan , Besson Claire
Published: February 2022

Bis(phthalocyaninato)lanthanoid(III) (LnPc2) complexes have attracted significant attention for their exceptional optical, electronic and magnetic properties. Crystallization of those compounds usually requires cumbersome methods such as sublimation and electrocrystallisation, which is a significant limitation to both structural determinations and the preparation of high purity materials at scale. We report here the selective crystallization of four polymorphs of LnPc2 using exclusively slow evaporation of saturated solutions. The obtained phase depends on the initial oxidation state of the LnPc2 molecule and the choice of solvent. Single-crystal X-ray diffraction studies were used to determine 14 new structures including Ln = La, Pr, Nd, Sm, Gd, Tb, Dy, Er and Yb, as well as correct previous mis-identifications from the literature. We provide a detailed comparison of molecular structure and crystal packing in all LnPc2 polymorphs. The primary feature in all phases is columnar stacking based on parallel π π interactions, with a variety of slip angles within those stacks as well as secondary interactions between them. Chemical redox and acid-base titrations, performed on re-dissolved crystals demonstrate that LnPc2+ and LnPc2- are easily obtained through weak oxidizing and reducing agents, respectively. Additionally, we show that the protonated form of the NdPc2- complex has a nearly identical UV-Vis spectra to that of neutral NdPc2, explaining some of the confusion over chemical composition in previously published literature.
Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces
Authors: Raghunathan Shampa
Published: February 2022

Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties.
ACCURATE PREDICTION OF VOLTAGE OF BATTERY ELECTRODE MATERIALS USING ATTENTION BASED GRAPH NEURAL NETWORKS
Authors: Siriwardane Edirisuriya , Louis Steph-Yves , Joshi Rajendra , Kumar Neeraj , Hu Jianjun
Published: February 2022

Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions.
Determining the Ethanol Concentrations in Ethyl Alcohol-Based Hand Sanitizers with Different Expiration Dates Using Fourier-Transform Infrared Spectroscopy (FT-IR)
Authors: Shlafstein Max , Hannah Emily , hao ling
Published: February 2022

Hand sanitizer has proved to be an essential public health tool in slowing the spread of the coronavirus (COVID-19). However, amid the COVID-19 pandemic, the availability of recently manufactured hand sanitizer can be limited. The product expiration date set by manufacturers is also minimally regulated. The efficacy of hand sanitizer is entirely determined by its most volatile ingredient, alcohol, which must remain at a concentration greater than 60% (v/v) to kill bacterial and viral pathogens. In our undergraduate Chem 2123W course (Introductory Quantitative Analysis Laboratory) in spring 2021, we designed and conducted an experiment to investigate the alcohol concentration of ethyl alcohol-based commercial hand sanitizers with varying expiration dates. The presence of a primary alcohol functional group in ethyl alcohol can be observed by Fourier-transform infrared spectroscopy (FT- IR). Through a series of standard additions of ethyl alcohol to the diluted hand sanitizer sample, we measured the changes in the integration of the FT-IR primary alcohol C-O stretch. The signal changes in integration were used to generate a standard addition curve using ethanol concentration and calculate the initial ethanol concentration of each sample. Our approach accounts for this matrix effect and does not rely on a compositional approximation. All the samples studied were found to exceed the minimum concentration required to effectively exhibit antimicrobial properties despite three of the four samples being expired. However, the expired samples remained close to the 60% threshold and with consideration of error analysis, the effectiveness of these hand sanitizers remains uncertain. The samples analyzed in this study varied in reported initial ethyl alcohol concentration, manufacturer, color, perfume, and other ingredients. The heterogeneity of our samples coupled with our consistent results provided insight into consumer use of expired ethyl alcohol-based hand sanitizers.
Determining the Ethanol Concentrations in Ethyl Alcohol-Based Hand Sanitizers with Different Expiration Dates Using Fourier-Transform Infrared Spectroscopy (FT-IR)
Authors: Shlafstein Max , Hannah Emily , Hao Ling
Published: February 2022

Hand sanitizer has proved to be an essential public health tool in slowing the spread of the coronavirus (COVID-19). However, amid the COVID-19 pandemic, the availability of recently manufactured hand sanitizer can be limited. The product expiration date set by manufacturers is also minimally regulated. The efficacy of hand sanitizer is entirely determined by its most volatile ingredient, alcohol, which must remain at a concentration greater than 60% (v/v) to kill bacterial and viral pathogens. In our undergraduate Chem 2123W course (Introductory Quantitative Analysis Laboratory) in spring 2021, we designed and conducted an experiment to investigate the alcohol concentration of ethyl alcohol-based commercial hand sanitizers with varying expiration dates. The presence of a primary alcohol functional group in ethyl alcohol can be observed by Fourier-transform infrared spectroscopy (FT- IR). Through a series of standard additions of ethyl alcohol to the diluted hand sanitizer sample, we measured the changes in the integration of the FT-IR primary alcohol C-O stretch. The signal changes in integration were used to generate a standard addition curve using ethanol concentration and calculate the initial ethanol concentration of each sample. Our approach accounts for this matrix effect and does not rely on a compositional approximation. All the samples studied were found to exceed the minimum concentration required to effectively exhibit antimicrobial properties despite three of the four samples being expired. However, the expired samples remained close to the 60% threshold and with consideration of error analysis, the effectiveness of these hand sanitizers remains uncertain. The samples analyzed in this study varied in reported initial ethyl alcohol concentration, manufacturer, color, perfume, and other ingredients. The heterogeneity of our samples coupled with our consistent results provided insight into consumer use of expired ethyl alcohol-based hand sanitizers.
Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo molecules
Authors: Litsa Eleni , Chenthamarakshan Vijil , Das Payel , Kavraki Lydia
Published: February 2022

Elucidating the structure of a chemical compound is a fundamental task in chemistry with application in multiple domains including the emerging field of metabolomics, with promising applications in drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a chemical compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, database retrieval methods fail to identify novel molecules that are not present in the reference database. In this work, we propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key substructures in the molecule from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree based methods, in recommending molecules for a given mass spectrum.
Carbamoyl phosphate and its substitutes for the uracil synthesis in origins of life scenarios
Authors: Ter-Ovanessian Louis , Rigaud Baptiste , Mezzetti Alberto , Lambert Jean-François , Maurel Marie-Christine
Published: February 2022

The first step of pyrimidine synthesis along the orotate pathway is studied to test the hypothesis of geochemical continuity of protometabolic pathways at the origins of life. Carbamoyl phosphate (CP) is the first high-energy building block that intervenes in the in vivo synthesis of the uracil ring of UMP. Thus, the likelihood of its occurrence in prebiotic conditions is investigated herein. The evolution of carbamoyl phosphate in water and in ammonia aqueous solutions without enzymes was characterised using ATR-IR, 31P and 13C spectroscopies. Carbamoyl phosphate initially appears stable in water at ambient conditions before transforming to cyanate and carbamate/hydrogenocarbonate species within a matter of hours. Cyanate, less labile than CP, remains a potential carbamoylating agent. In the presence of ammonia, CP decomposition occurs more rapidly and generates urea. We conclude that CP is not a likely prebiotic reagent by itself. Alternatively, cyanate and urea may be more promising substitutes for CP, because they are both “energy-rich” (high free enthalpy molecules in aqueous solutions) and kinetically inert regarding hydrolysis. Energy-rich inorganic molecules such as trimetaphosphate or phosphoramidates were also explored for their suitability as sources of carbamoyl phosphate. Although these species did not generate CP or other carbamoylating agents, they exhibited energy transduction, specifically the formation of high-energy P-N bonds. Future efforts should aim to evaluate the role of carbamoylating agents in aspartate carbamoylation, which is the following reaction in the orotate pathway
A Mega-High-Throughput Screening Platform for the Discovery of Biologically Relevant Sequence-Defined Non-Natural Polymers
Authors: Avital-Shmilovici Michal , Liu Xiaohe , Shaler Thomas , Lowenthal Andrew , Bourbon Pauline , Snider Janey , Tambo-ong Arlyn , Repellin Claire , Yniguez Kenya , Sambucetti Lidia , Madrid Peter , Collins Nathan
Published: February 2022

Combinatorial methods enable the synthesis of chemical libraries on scales of millions to billions of compounds, but the ability to efficiently screen and sequence such large libraries has remained a major bottleneck for molecular discovery. We developed a novel technology for screening and sequencing libraries of synthetic molecules of up to a billion compounds in size. This platform utilizes the Fiber-optic Array Scanning Technology (FAST) to screen bead-based libraries of synthetic compounds at a rate of 5 million compounds per minute (~83,000 Hz). This ultra-high-throughput screening platform has been used to screen libraries of synthetic “self-readable” non-natural polymers that can be sequenced at femtomole scale by chemical fragmentation and high-resolution mass spectrometry. The versatility and throughput of the platform was demonstrated by screening two libraries of non-natural polyamide polymers with sizes of 1.77M and 1B compounds against the protein targets K-Ras, asialoglycoprotein receptor 1 (ASGPR), IL-6, IL 6 receptor (IL-6R) and TNFα. Hits with low nanomolar binding affinities were found against all targets, including competitive inhibitors of K-Ras binding to Raf and functionally active uptake ligands for ASGPR facilitating intracellular delivery of a non-glycan ligand.
Rapid quantification of Psilocybin with reversed-phase HPLC and single-wavelength detection
Authors: Samuelsson Alexzander , Janusson Eric , Shah Sajni , Roggen Markus
Published: February 2022

The alkaloid psilocybin (4-phosphoryloxy-N,N-dimethyltryptamine) and the neurologically active psilocin (4-hydroxy-N,N-dimethyltryptamine) are the foremost compounds of pharmaceutical interest in Psilocybe mushrooms. As these compounds are infrequently analyzed in analytical labs, validated methods for rapid purity analysis are lacking. Newfound therapeutic use has invigorated academic and commercial interests in the molecules and new methods of production and available products are expanding. As a result, high-throughput methods of analysis for psilocybin must be improved to promptly determine chemical differences between mushroom genera or other sources of psilocybin and psilocin, as well as refined product purity. To address this, we developed an inexpensive HPLC technique for the efficient quantification of psilocybin and psilocin by using readily available equipment and dilute reagents. Aqueous ammonium formate (0.143 mM) was found to be preferable over techniques with much higher buffer concentrations or stronger acids for controlling psilocybin Zwitterion resolution. The chromatographic run time satisfied high-throughput analytical requirements with an efficient total runtime under 2 minutes. A standard octadecyl silica (C18) column provided excellent resolution between psilocybin and psilocin signals. The quality of the method was validated using certified analytical reference standards and was found to be accurate (3.5% bias, Psilocybin), reliable (0.32% RSD), and efficient (Psilocybin k’ = 1.78).
Origin of the high specific capacity in sodium manganese hexacyanomanganate
Authors: Hurlbutt Kevin , Giustino Feliciano , Volonakis George , Pasta Mauro
Published: February 2022

Sodium manganese hexacyanomanganate, NaxMn[Mn(CN)6], is an electrochemically active Prussian blue analog (PBA) that has been studied experimentally as an electrode material in rechargeable sodium-ion batteries. It has a reversible specific capacity of 209 mAh/g, which is substantially higher than the theoretical specific capacity of 172 mAh/g expected for two reduction events conventional in the PBAs. It has been suggested the high specific capacity originates from this compound's unique ability to insert a third sodium ion per formula unit. However, the plausibility of this mechanism has remained ambiguous. Here we use density-functional theory (DFT) with a hybrid functional to calculate the formation energies of various oxidation states and magnetic phases of the NaxMn[Mn(CN)6] system. We confirm that the compound Na3Mn(II)[Mn(I)(CN)6] is, indeed, thermodynamically stable. It contains manganese(I) and the sodium ions occupy the interfacial position of the lattice subcubes. We also provide strong evidence that the phase of the fully oxidized Mn[Mn(CN)6] compound is charge-disproportionated, containing manganese(II) and manganese(IV). We proceed to show that the presence of crystalline water increases the reduction potential of the system and that the hydrated compounds have theoretical crystal geometries and reduction potentials that closely match experiment. This work clarifies the charge-storage mechanism in a well-known but less-understood PBA.
Colorimetric quantification of linking in thermoreversible nanocrystal gel assemblies
Authors: Kang Jiho , Valenzuela Stephanie , Lin Emily , Dominguez Manuel , Sherman Zachary , Truskett Thomas , Anslyn Eric , Milliron Delia
Published: February 2022

Nanocrystal gel networks can be responsive, tunable materials, but deliberately designing their structure and controlling their properties have been challenging. By employing reversibly bonded molecular linkers, gelation can be realized under conditions predicted by thermodynamics. But, simulations have offered the only microscopic insights, with no experimental means to monitor linking leading to gelation. Here, we introduce a metal coordination linkage with a distinct optical signature allowing us to quantify linking in situ and establish the structural and thermodynamic basis for assembly. Due to coupling between linked indium tin oxide nanocrystals, their infrared absorption shifts abruptly at a chemically tunable gelation temperature. We quantify bonding spectroscopically and use molecular dynamics simulations to understand bonding motifs as a function of temperature, revealing that gel formation is governed by reaching a critical number of effective links that extend the nanocrystal network. Microscopic insights from our colorimetric linking chemistry enable switchable gels based on equilibrium thermodynamic principles, opening the door to rational design of programmable nanocrystal net-work assemblies.
Chemical Interpretation of Charged Point Defects in Semiconductors: A Case Study of Mg2Si
Authors: Toriyama Michael , Brod Madison , Snyder G Jeffrey
Published: February 2022

The number of excess charge carriers generated by a point defect, defined by the "charge state" of a defect, is oftentimes an important quantity used to engineer the electronic properties of semiconductors. Here, we develop a molecular orbital theory-based framework for interpreting the charge state(s) of a point defect, which is based on local chemical interactions between the defect and the atoms surrounding the defect site. We demonstrate how the framework can be applied to native defects in Mg2Si, such as interstitials, vacancies, and antisite defects, by utilizing symmetry principles and Density Functional Theory calculations. We anticipate that such an interpretive framework will guide efforts to engineer electronic and optical properties of semiconductors through manipulation of intrinsic and extrinsic defects.
From “inverted” to “superdirect“ bonds: a general concept connecting substituent angles with sigma bond strengths. The case of the CC bonds in hydrocarbons.
Authors: Laplaza Rubén , Contreras-García Julia , Fuster Franck , Volatron François , Chaquin Patrick
Published: February 2022

The C-C dissociation energy with respect to geometry frozen fragments (BE) has been calculated for C2H6 as a function of  = H-C-C angles. BE decreases rapidly when  decreases from its equilibrium value to yield the so-called “inverted bonds” for  < 90°; on the contrary BE increaseswhen  increases to yield somehow “superdirect” bonds, following a sigmoidal variation. The central bond in Si2H6, Ge2H6 and N 2H4 as well as the C-H bond in CH3-H behaves similarly. The concept of “invertedness”/”directedness” is generalized to any CC sigma bond in hydrocarbons and characterized by the mean angle value <> of substituents. Using dynamic orbital forces (DOF) as indices, the intrinsic  bond energies are studied as a function of <> for formally single bonds in apanel of 22 molecules. This energy decreases from the strongest “superdirect” bonds in butadiyne, (<> = 180°) or tetrahedrylacetylene to the weakest “inverted bond” in cyclobutene, tetrahedrane, bicyclobutane and [1.1.1]propellane (<> = 60°), according to a sigmoidal variation. The <> parameter appears as a crude, but straightforward and robust, index of strain in cyclic molecules. Sigma bonds in multiple bonds of a panel of 11 molecules have most of time <> values less than 90°and are significantly weaker than standard single bonds. Thus they can be considered as formally inverted or near inverted.
Binary-QSAR guided virtual screening of FDA approved drugs and compounds in clinical investigation against SARS-CoV-2 main protease
Authors: Durdagi Serdar , Oktay Lalehan , Erdemoglu Ece , Tolu Ilayda , Yumak Yesim , Ozcan Aysenur , Acar Elif , Buyukkilic Sehriban , Olkan Alpsu
Published: February 2022

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the MetaCoreTM/MetaDrugTM (https://portal.genego.com) platform's antiviral therapeutic activity binary QSAR model. In the 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 Main Protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docked compounds and following Molecular Mechanics Generalized Born Surface Area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivotexil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.
Prediction of Interfacial Properties of High-Performance Polymers and Flattened CNT-Reinforced Composites using Molecular Dynamics
Authors: Deshpande Prathamesh , Radue Matthew , Gaikwad Prashik , Bamane Swapnil , Patil Sagar , Pisani William , Odegard Gregory
Published: February 2022

The next generation of ultra-high strength composites for structural components of vehicles for manned missions to deep space will likely incorporate flattened carbon nanotubes (flCNTs). With a wide range of high-performance polymers to choose from as the matrix component, efficient and accurate computational modeling can be used to efficiently down-select compatible resins, drive the design of these composites by predicting interface behavior, and provide critical physical insight into the flCNT/polymer interface. In this study, molecular dynamics simulation is used to predict the interaction energy, frictional sliding resistance, and mechanical binding of flCNT/polymer interfaces for epoxy, bismaleimide (BMI), and benzoxazine high-performance resins. The results indicate that the BMI has stronger interfacial interaction and transverse tension binding with flCNT interfaces, while the benzoxazine demonstrates the strongest levels of interfacial friction resistance. Comparison of these results with similar results from the literature for other high-performance resins indicates that BMI demonstrates the best overall compatibility with flCNTs for use in high-performance structural composites.
Decoupling bulk and interfacial contributions to performance in localized high concentration electrolytes for Li metal batteries
Authors: May Richard , Hestenes Julia , Marbella Lauren
Published: February 2022

Localized high concentration electrolytes (LHCEs) are a promising class of electrolytes to enable stable cycling of the lithium metal anode. Here, we report the use of operando nuclear magnetic resonance (NMR) spectroscopy to observe electrolyte decomposition during Li stripping/plating and identify the influence of individual components in LHCEs on Li metal battery performance. Data from operando 19F solution NMR indicates that both bis(fluorosulfonyl)imide (FSI–) salt and bis(2,2,2-trifluoroethyl)ether (BTFE) diluent molecules play a key role in solid electrolyte interphase (SEI) formation, in contrast to prior reports that suggest diluents are inert. Using a combination of solution 17O NMR and cyclic voltammetry (CV), we assess differences in solvation and electrochemical reduction in LHCEs and compare to low concentration electrolytes (LCEs). We find that BTFE diluents are chemically (rather than electrochemically) reduced during Li metal battery operation, which can be detected with operando NMR, but not conventional electrochemical methods. Solid-state NMR (SSNMR) and X-ray photoelectron spectroscopy (XPS) measurements confirm that LHCEs decompose to form a SEI on Li metal that contains organic BTFE reduction products as well as high quantities of lithium fluoride from both BTFE and FSI– reduction. Insight into the (electro)chemical reduction mechanisms underpinning SEI formation in LHCEs suggests that fluorinated ethers exhibit tunable reactivity that can be leveraged to control Li deposition behavior.
V-dock: fast generation of novel drug-like molecules using machine-learning-based docking score and molecular optimization
Authors: Choi Jieun , Lee Juyong
Published: February 2022

In this work, we propose a novel drug-like molecular design workflow by combining an efficient global molecular property optimization, protein-ligand molecular docking, and machine learning. Computational drug design algorithms aim to find novel molecules satisfying various drug-like properties and have a strong binding affinity between a protein and a ligand. To accomplish this goal, various computational molecular generation methods have been developed with recent advances in deep learning and the increase of biological data. However, most existing methods heavily depend on experimental activity data, which are not available for many targets. Thus, when the number of available activity data is limited, protein-ligand docking calculations should be used. However, performing a docking calculation during molecular generation on the fly requires considerable computational resources. To address this problem, we used machine-learning models predicting docking energy to accelerate the molecular generation process. We combined this ML-assisted docking score prediction model with the efficient global molecular property optimization approach, MolFinder. We call this design approach V-dock. Using the V-dock approach, we quickly generated many molecules with high docking scores for a target protein and desirable drug-like and bespoke properties, such as similarity to a reference molecule.
Development of Specially Reinforced Magnesium Composites Prepared by Squeeze Casting Process
Authors: Mishra Akshansh , Dixit Devarrishi
Published: February 2022

A wide range of opportunities in the field of automotive and structural applications are being offered by Magnesium matrix composites because of their enhanced mechanical properties. Magnesium alloys based Metal Matrix Composites (MMCs) are the best candidates for lightweight structural applications due to their improved creep properties. In the present study, three specimens of specially reinforced magnesium composites were manufactured by using the squeeze casting process. Specimen 1 has a composition of 7 % aluminum alloy in addition to 1% zinc and the composition of reinforcement is Titanium Carbide 0.3 % in addition to 1.5% Carbon nanotubes. Specimen 2 has a composition of 12 % aluminum alloy in addition to 1 % zinc and the composition of reinforcement is 2% B_4 C in addition to 2 % Carbon nanotubes. Specimen 3 has a composition of 14 % aluminum alloy in addition to 1 % zinc and the composition of reinforcement is 2 % B_4 C in addition to 2 % Carbon nanotubes. The mechanical properties analysis showed that specimen 2 has a higher hardness value in comparison to other manufactured specimens and it was also observed that specimen 2 possesses a higher tensile strength value in comparison to the other two specimens. Microstructure analysis shows that there was a uniform distribution of the reinforcements in the matrix. So it can be inferred that this uniform distribution causes higher hardness and higher tensile strength in the manufactured specimens.
Performance of chemical structure string representations for chemical image recognition using transformers
Authors: Rajan Kohulan , Steinbeck Christoph , Zielesny Achim
Published: February 2022

The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES performs in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were carried out with publicly available datasets and the code used to train and evaluate the models has been made available to the public.
Energetics of pathways enabled by experimentally detected radicals during catalytic, plasma-assisted NH3 synthesis
Authors: Liu Tsung-Wei , Gorky Fnu , Carreon Maria , Gualdron Diego A
Published: February 2022

Plasma-assisted catalysis is emerging as an alternative to several thermocatalytic processes. For ammonia synthesis, it could make the process milder, which would help production decentralization and compatibility with renewable energy. However, one major obstacle preventing optimization of the plasma-assisted process is the incipient mechanistic understanding of ammonia formation on plasma-exposed catalysts. Here, emission spectroscopy detects N• and H• radicals in plasma phas-es generated from N2/H2 mixtures even at atmospheric pressure, which are bound to enable new catalyst involved pathways not considered in previously reported kinetic models for ammonia synthesis. Thus, we comprehensively examined, via den-sity functional theory (DFT) calculations, the energetics (favorability) of 51 reactions on Fe, Ni, Co, Pd, Ga, Sn, Cu, Au, and Ag. Enthalpic barriers for Eley-Rideal (ER) reactions involving N• and H• radicals were found to be negligible and hence supportive of: i) plausible NNH formation and consequent prominent role of the associative pathway to form NH3 (con-sistent with some experimental reports detecting surface-bound NXHY species), ii) likelihood of N• adsorption taking over N2* dissociation as the primary source of surface bound N*, and iii) probable dominance of ER hydrogenation reactions over Langmuir-Hinshelwood (LH) ones. The energetics herein presented will allow thoroughly studying pathway competition in future kinetic models, but numbers calculated here already suggest that the dominant pathway may change with metal identity. For instance, N2HY dissociation favorability becomes competitive with ER hydrogenation earlier in the hydrogena-tion sequence in the more nitrophilic the metals. Yet, the calculated favorability of ER reactions is also already consistent with the weaker dependence of initial NH3 turnover frequencies (TOFs) on metal identity compared to the thermocatalytic scenario. With practical implications for computational catalyst screening, TOFs experimentally measured herein for an atmospheric dielectric barrier discharge (DBD) reactor linearly correlate with ΔErxn for the ER hydrogenation reaction H• + HNNH2* → HNNH3*. This descriptor may be robust to exact synthesis conditions, as its correlation with TOFs was main-tained for earlier TOF data in a sub-atmospheric radio frequency (RF) reactor
Detection of single amino acid differences in haemoglobin from blood samples using a nanopore
Authors: Huang Gang , Versloot Roderick Corstiaan Abraham , Maglia Giovanni
Published: February 2022

The real-time identification of protein biomarkers is under intense investigation for the development of point-of-care and portable devices. Here we use a PlyAB biological nanopore to detect a range of folded Haemoglobin (Hb) variants in blood samples. Blockades of HbA and sickle cell anaemia HbS, which differ by just one amino acid, and fetal HbF were distinguished with more than 97% accuracy on the basis of individual events. This nanopore approach is quick and straightforward. It can count and confirm the identity of Hb variants instantaneously directly from blood samples. Therefore, PlyAB nanopores are promising single-molecule nanoreactors for real-time folded protein analysis, and identification with immediate application in point-of-care Hb counting and sickle cell anaemia diagnosis.
Protein pKa prediction with machine learning
Authors: Cai Zhitao , Luo Fangfang , Wang Yongxian , Li Enling , Huang Yandong
Published: February 2022

Protein pKa prediction is essential for the investigation of pH-associated relationship between protein structure and function. In this work, we introduce a deep learning based protein pKa predictor DeepKa, which is trained and validated with the pKa values derived from continuous constant pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang, Harris, and Shen J. Chem. Inf. Model. 2018, 58, 1372-1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pKa predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning based protein pKa predictors in future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction.
New Hypercoordinating Organostannanes for the Modular Functionalization of Mono- and Polystannanes: Synthetic and Computational Studies
Authors: Pau Jeffrey , Choi Jung-Won , Silverthorne Kaitlyn , Ranne Mokhamed , Wylie R. Stephen , Gossage Robert , Lough Alan , Foucher Daniel
Published: February 2022

In this paper we outline two major findings. The first is that we describe how designer hypercoordinate stannanes can be made from simple substitutions, and by extension, designer tin polymers, at least partially, using these methods. The second is that using advanced DFT methods, 119Sn NMR frequencies have been accurately calculated for select hypercoordinate tin compounds. This accuracy provides a means for further investigation of an important bonding interaction in the solution phase, and is of significant interest to small molecule tin chemists.
On the elasticity of Copper(II)acetylacetonate crystals
Authors: Das Susobhan , Vreeman Gerrit , Ramamurty Upadrasta , Sun Changquan Calvin , Reddy Chilla Malla
Published: February 2022

Structure-property correlations in a wide variety of elastic molecular crystals indicate that (a) corrugated structures that can interlock readily (prevent plastic deformation either through geometrical or energetic considerations) and (b) a multitude of weak and dispersive intermolecular interactions that would act as structural buffers through easy rupture and reformation during deformation, are structural features that impart a crystal the ability to accommodate large elastic strains.1-4 Based on their studies on copper(II)acetylacetonate crystals (1, hereafter), Worthy et al. claimed that these criteria are “incorrect”.5 In order to examine their claim critically, we conducted detailed experiments on crystals of 1. Our flexure experiments show that they indeed readily undergo plastic deformation when bent on the (101) face, which contradicts Worthy et al.‘s assertion that the (101) face is highly elastic. Therefore, the crystal 1 is not an ideal model system for rejecting the prevailing and widely accepted-molecular mechanisms for exceptional elastic flexibility of organic crystals.
Perfluorocycloparaphenylenes: Fully fluorinated carbon nanorings by Ni-mediated one-pot synthesis
Authors: Itami Kenichiro , Shudo Hiroki , Kuwayama Motonobu , Shimasaki Masafumi , Nishihara Taishi , Takeda Youhei , Kuwabara Takuya , Yagi Akiko , Segawa Yasutomo
Published: February 2022

Perfluorinated aromatic compounds, the so-called perfluoroarenes, are widely used in materials science owing to their high electron affinity and characteristic intermolecular interactions. However, methods to synthesize highly strained perfluoroarenes have remained elusive so far, which greatly limits their structural diversity. Herein, we report the synthesis and isolation of perfluorocycloparaphenylenes (PFCPPs) as a class of ring-shaped perfluoroarenes. Using macrocyclic nickel complexes, we succeeded in synthesizing PF[n]CPPs (n = 10, 12, 14, 16) in one-pot without noble metals. The molecular structures of PF[n]CPPs (n = 10, 12) were determined by X-ray crystallography to confirm their tubular alignment. Photophysical and electrochemical measurements revealed that PF[n]CPPs (n = 10, 12) exhibit wide HOMO–LUMO gaps, high electron affinity, and strong phosphorescence at low temperature. PFCPPs are not only useful as electron-accepting organic semiconductors but can also be used for accelerating the creation of topologically unique molecular nanocarbon materials.
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Authors: Kovacs David Peter , van der Oord Cas , Kucera Jiri , Allen Alice , Cole Daniel , Ortner Christoph , Csanyi Gabor
Published: February 2022

We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking.