The 50 most-cited articles in The Journal of Physical Chemistry A, 2020

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and,…

The covalent chemical bond is intimately linked to electron sharing between atoms. The recent independent gradient model (IGM) and its δ g descriptor provide a way to quantify locally this electron density interpenetration from wavefunction calculations. Each bond has its own IGM-δ g pair signature. The present work establishes for the first time a strong link between this bond signature and the physically grounded bond force constant concept. Analyzing a large set of compounds and bonds, the intrinsic bond strength index (IBSI) emerges from the IGM formulation. Our study shows that the IBSI does not belong to the class of conventional bond orders (like Mulliken, Wiberg, Mayer, delocalization index, or electron localization function—ELF), but is rather a new complementary index, related to the bond strength. A fundamental outcome of this research is a novel index allowing to range all two-center chemical bonds by their intrinsic strength in molecular situation. We believe that the IBSI is a powerful and robust tool for interpretation accessible to a wide community of chemists…

The accuracy of some density functional (DF) models widely used in material science depends on empirical or free parameters that are commonly tuned using reference physical properties. Grid-search methods are the standard numerical approximations used to find the optimal values of the free parameters, making the computational complexity to scale with the number of points in the grid. In this report, we illustrate that Bayesian optimization (BO), a sample-efficient machine learning algorithm, can calibrate different density functional models, e.g., hybrid exchange-correlation and range-separated density functionals. Using the atomization energies and bond lengths from the Gaussian-1 (G1) and Gaussian-2 (G2) databases, we show that BO optimizes the free parameters of the hybrid exchange-correlation functionals with approximately 55 evaluations of the error function. We also show that selecting exchange-correlation functionals for different physical systems can be done with BO. We jointly optimize and select the form of the exchange-correlation functionals and the free parameters by…

Machine learning (ML) models can potentially accelerate the discovery of tailored materials by learning a function that maps chemical compounds into their respective target properties. In this realm, a crucial step is encoding the molecular systems into the ML model, in which the molecular representation plays a crucial role. Most of the representations are based on the use of atomic coordinates (structure); however, it can increase ML training and predictions' computational cost. Herein, we investigate the impact of choosing free-coordinate descriptors based on the Simplified Molecular Input Line Entry System (SMILES) representation, which can substantially reduce the ML predictions' computational cost. Therefore, we evaluate a feed-forward neural network (FNN) model's prediction performance over five feature selection methods and nine ground-state properties (including energetic, electronic, and thermodynamic properties) from a public data set composed of ∼130k organic molecules. Our best results reached a mean absolute error, close to chemical accuracy, of ∼0.05 eV for the…

Using density functional theory calculations, we have analyzed nonlinear optical properties of a series of T-graphene quantum dots differing in their shape and size. Electronic polarizability and first-order and second-order hyperpolarizability of these systems are investigated and shed light on their stability and electronic properties. Negative cohesive energy shows that they are energetically stable. The effect of size and incident frequency on their nonlinear responses are comprehensively discussed. Most of the systems exhibit a strong NLO response, and it is enhanced in the presence of an external field. All these systems show absorption maximum ranging from UV to visible window. Overall, this theoretical framework highlighted the nonlinear optical properties of T-graphene quantum dots that may provide valuable information in designing potential NLO materials.

The calculation of redox potentials by semiempirical quantum mechanical (SQM) approaches is evaluated with a focus on the recently developed GFN n -xTB methods. The assessment is based on a data set comprising 313 experimental redox potentials of small to medium-sized organic and organometallic molecules in various solvents. This compilation is termed as ROP313 (reduction and oxidation potentials 313) and divided for analysis purposes into the organic subset OROP and the organometallic subset OMROP. Corresponding data for a few common density functional theory (DFT) functionals employing extended AO basis sets and small basis-set DFT composite schemes are computed for comparison. Continuum solvation models are used to calculate the important solvation free energy contribution. The results for ROP313 show that the GFN n -xTB methods provide a robust, efficient, and generally applicable workflow for the routine calculation of redox potentials. The GFN n -xTB methods outperform the PM x competitor for the OROP subset (mean absolute deviation from the experiment, MAD GFN2-xTB = 0.30 V,…

We investigate the physical basis, validity, and limitations of the minimum electrophilicity principle, MEP, which postulates that the sum of the electrophilicity indices, ∑ω, of the reaction products will be smaller than that of the reactants, Δω < 0. We present a much-improved understanding of the conditions for minimizing electrophilicity indices. Two indices, ω 1 = ( I + A ) 2 /8( I – A ) and ω 2 = I · A /( I – A ), are discussed, using ionization energies, I, and electron affinities, A, obtained from either ground-state (GS) or valence-state (VS) energies. The performances of ω 1 and ω 2 are compared for a wide range of chemical species from diatomic molecules, through large clusters to liquid water and solid crystals. New analytical arguments in support of MEP are found. Two new theorems are proved, and three new rules rationalize the changes Δω 1 and Δω 2 in association reactions, X + Y → XY. They explain why MEP is much more successful as a guiding rule than the maximum hardness postulate in such reactions. On the other hand, they also identify the increased electron…

X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. in operando catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of…

When dealing with organic peroxides in both laboratory and industrial applications, the relative strengths of the O-O bond are of vital importance, both from a safety and mechanistic perspective. Although it is well known that such oxidizing agents are highly reactive, reliable O-O bond dissociation energies (BDEs) have only recently been established. In an earlier report, we suggested a median O-O BDE value of ca. 45 kcal/mol for a variety of well-known peroxides based upon G2 ab initio calculations. In the present report, we have calculated the O-O BDE of twenty peroxides of varying structures at a more accurate CBS-APNO level. We have also compared these bond energies to the corresponding BDEs calculated with several DFT functionals and shown that the M06-2X functional produces O-O bond energies that compare very favorably with G4 and CBS-APNO values.

Abstract The direct method (HA(soln) ⇌ A(soln)– + H(soln)+) for calculating pKa of monoprotic acids is as efficient as thermodynamic cycles. A selective adjustment of proton free energy in solution was used with experimental pKa data. The procedure was analyzed at different levels of theory. The solvent was described by the solvation model density (SMD) model, including or not explicit water molecules, and three training sets were tested. The best performance under any condition was obtained by the G4CEP method with a mean absolute error close to 0.5 units of pKa and an uncertainty around ±1 unit of pKa for any training set including or excluding explicit solvent molecules. PM6 and AM1 performed very well with average absolute errors below 0.75 units of pKa but with uncertainties up to ±2 units of pKa, using only the SMD solvent model. Density functional theory (DFT) results were highly dependent on the basis functions and explicit water molecules. The best performance was observed for the local spin density approximation (LSDA) functional in almost all calculations and under…

The photochemical reduction of Hg(II) is an important pathway in the environmental Hg cycle because it competes with Hg methylation and potentially limits the formation of bioaccumulative methylmercury. Hg stable isotope systematics have proven to be an effective tool for investigating the transport, transformation, and bioaccumulation of Hg. The dominant cause of mass independent isotope fractionation (MIF) of Hg in nature is the photochemical reduction of various species of Hg(II). However, it is difficult to fully interpret Hg stable isotope signatures due to the lack of mechanistic information about which Hg compounds are susceptible to MIF and why. This study investigates Hg isotope fractionation during the photochemical reduction of Hg(II) complexed to organic ligands, which are representative of the available binding sites in natural dissolved organic matter. The photochemical reduction of Hg(II) in the presence of cysteine resulted in both negative and positive MIF in residual Hg(II), where the sign depended on pH and dissolved oxygen level. In the presence of serine, either…

Abstract Over the last few years, ab initio ligand field theory (AILFT) has evolved into an important tool for the extraction of ligand field models from ab initio calculations. The inclusion of dynamic correlation on top of complete active space self-consistent field (CASSCF) reference functions, which is important for accurate results, was so far realized at the level of second-order N-electron valence state perturbation theory (NEVPT2). In this work, we introduce two alternative methods for the inclusion of dynamic correlation into AILFT calculations, the second-order dynamic correlation dressed complete active space method (DCD-CAS(2)) and the Hermitian quasi-degenerate NEVPT2 (HQD-NEVPT2). These methods belong to the class of multistate perturbation theory approaches, which allow for the mixing of CASSCF states under the effect of dynamic correlation (state-mixing). The two new versions of AILFT were tested for a diverse set of transition-metal complexes. It was found that the multistate methods have, compared to NEVPT2, an AILFT fit with smaller root mean square deviations…

The standard thermodynamic parameters of inorganic ions in an aqueous solution are based on the use of a convention on the zero values of the thermodynamic functions of protons. However, this does not allow calculating the thermodynamic parameters of reactions in which, along with ions, organic and inorganic compounds participate, the thermodynamic parameters of which are determined in the standard system of values since frames of reference are different. In this work, we calculated the standard thermodynamic parameters of inorganic ions in an aqueous solution in the same system of values that is used for organic compounds. The calculation of the standard values of the Gibbs function, enthalpy, and entropy is based on the use of reliably determined thermodynamic parameters of protons in an aqueous solution according to Tissandier et al. [ J. Phys. Chem. A, 1998, 102, 7787−7794]. The results allow one to calculate the thermodynamic parameters of reactions in aqueous media with the participation of inorganic ions and organic compounds. In this work, the thermodynamic parameters of…

Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition-metal complexes. We first identify the limits of distance-based heuristics from distributions of metal-ligand bond lengths of over 2000 unique mononuclear Fe(II)/Fe(III) transition-metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal-ligand bond lengths and classify experimental ground-state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining…

Prediction models of lattice thermal conductivity (κ L ) have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. However, κ L is notoriously difficult to predict. Although classic models such as the Debye–Callaway model and the Slack model have been used to approximate the κ L of inorganic compounds, their accuracy is far from being satisfactory. Herein we propose a genetic programming-based symbolic regression (SR) approach for finding analytical κ L models and compare them with multilayer perceptron neural networks and random forest regression models using a hybrid cross-validation (CV) approach including both K -fold CV and holdout validation. Four formulae have been discovered by our SR approach that outperform the Slack formula as evaluated on our dataset. Through the analysis of our models’ performance and the formulae generated, we found that the trained formulae successfully reproduce the correct physical law that governs the lattice thermal conductivity of materials. We also systematically show that…

molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a Δ-NetFF machine learning model, where the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields, was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.

Abstract The recently synthesized cyclo[18]carbon molecule has been characterized in a number of studies by calculating electronic, spectroscopic, and mechanical properties. However, cyclo[18]carbon is only one member of the class of cyclo[n]carbons─standalone carbon allotrope representatives. Many of the larger members of this class of molecules have not been thoroughly investigated. In this work, we calculate the magnetically induced current density of cyclo[n]carbons in order to elucidate how electron delocalization and aromatic properties change with the size of the molecular ring (n), where n is an even number between 6 and 100. We find that the Hückel rules for aromaticity (4k + 2) and antiaromaticity (4k) become degenerate for large Cn rings (n > 50), which can be understood as a transition from a delocalized electronic structure to a nonaromatic structure with localized current density fluxes in the triple bonds. Actually, the calculations suggest that cyclo[n]carbons with n > 50 are nonaromatic cyclic polyalkynes. The influence of the amount of nonlocal exchange and the…

We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.

This work introduces a novel methodology for the quantification of uncertainties associated with potential energy surfaces (PESs) computed from first-principles quantum mechanical calculations. The methodology relies on Bayesian inference and machine learning techniques to construct a stochastic PES and to express the inadequacies associated with the ab initio data points and their fit. By combining high fidelity calculations and reduced-order modeling, the resulting stochastic surface is efficiently forward propagated via quasi-classical trajectory and master equation calculations. In this way, the PES contribution to the uncertainty on predefined quantities of interest (QoIs) is explicitly determined. This study is done at both microscopic (e.g., rovibrational-specific rate coefficients) and macroscopic (e.g., thermal and chemical relaxation properties) levels. A correlation analysis is finally applied to identify the PES regions that require further refinement, based on their effects on the QoI reliability. The methodology is applied to the study of singlet (1 1 A′) and quintet…

The formation of hot spots in dynamically compressed, plastic-bonded explosives is known to be the primary mechanism by which these materials ignite and initiate, but hot spots are small, fleeting, and hard to observe. Using a microscope equipped with laser-launched, miniflyer plates, we have studied hot spots in small grains of cyclotetramethylene-tetranitramine (HMX) embedded in a polyurethane binder, shocked to about 20 GPa. A nanosecond video with 4 μm spatial resolution is used to observe hot spot formation and growth, while nanosecond optical pyrometry measured temperature. Using individual ∼200 μm nominally single crystals of HMX (HMX-SC), we observed hot spots forming preferentially on corners or edges. These hot spots are about 4000 K. When there are multiple hot spots, the flame propagated along crystal edges, and the crystal is mostly combusted after about 300 ns. Using polycrystalline grains (HMX-PC), 6000 K hot spots are created near internal defects or crystal junctions. However, the thermal mass of the material at 6000 K is quite small, so after those hot spots cool…

In this study, atomic level interactions of a 1:1 choline chloride (ChCl)/acetylsalicylic acid (ASA) therapeutic deep eutectic solvent (THDES) has been investigated by combining the molecular dynamics (MD), density functional theory (DFT), and spectroscopic (Raman and IR) techniques. Atom–atom radial distribution functions (RDFs) based on MD simulation reveal that hydrogen bonds are formed between Cl – ···HO Ch + and Cl – ···HO COOH of the THDES, where Cl – works as a bridge between ASA and Ch + . Cation–anion electrostatic attractions are disrupted by highly interconnected hydrogen bonds. Cluster conformers of the THDES are isolated from MD simulation and optimized using ωB97XD/6-311++G(d,p) level of theory, in which the strongest H bonds are found among OH Ch + ···Cl – (2.37 Å) and Cl – ···HO COOH (2.40 Å). Charge transfer calculations, using CHEPLG and NBO analysis, disclose that the charge of Cl – is reduced in the cluster structures and transferred to Ch + and ASA. Further analyses are conducted using experimental and computed spectroscopic data. These confirm the formation of…

We introduce the deep post Hartree-Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.

Here, we have studied the crystalline structure of bulk ZnX (X = O, S, Se, Te) and ZnF 2 systems as a first step to understand the structures like ZnX and Zn-based systems like ZnO/ZnF 2 interfaces, which are of utmost importance for possible technological applications. In addition, an adequate methodological description based on density functional theory (DFT) calculations is necessary. It is well known that plain DFT calculations based on local or semilocal exchange–correlation functionals fail to describe the correct band gap energy for these systems, whereas nonlocal approaches, such as hybrid-based functionals, can compensate the underestimation of band gap. To contribute to the assessment, DFT studies were performed within semilocal Perdew–Burke–Ernzerhof (PBE) and two nonlocal functionals, hybrid Heyd–Scuseria–Ernzerhof (HSE) and PBE + U functionals. Our results confirm that PBE underestimates the energy band gap values, from 33.0 to 42.8% for ZnX compounds compared to the experimental values. Applying the hybrid HSE functional, we obtained a band gap dependency in relation…

In this work, machine learning (ML), materials informatics (MI), and thermochemical data are combined to screen potential candidates of energetic materials. To directly characterize energetic performance, the heat of explosion Δ H e is used as the target property. The critical descriptors of cohesive energy, averaged over all constituent elements and the oxygen balance, are found by forward stepwise selection from a large number of possible descriptors. With them and a theoretically labeled Δ H e training data set, a satisfactory surrogate ML model is trained. The ML model is applied to large databases ICSD and PubChem to predict Δ H e . At the gross-level filtering by the ML model, 2732 molecular candidates based on carbon, hydrogen, nitrogen, and oxygen (CHNO) with high Δ H e values are predicted. Afterward, a fine-level thermochemical screening is carried out on the 2732 materials, resulting in 262 candidates with TNT equivalent power index P e (TNT) greater than 1.5. Raising P e (TNT) further to larger than 1.8, 29 potential candidates are found from the 2732 materials, all are…

Monoterpenes are a group of volatile organic compounds (VOCs) emitted to the atmosphere in large amounts. Studies have linked the autoxidation of monoterpenes to the formation of secondary organic aerosols, which impact Earth’s climate and human health. Here, we study the hydroxy peroxy radicals formed by OH- and O 2 -addition to the six atmospherically relevant monoterpenes α-pinene, β-pinene, Δ 3 -carene, camphene, limonene, and terpinolene. The six monoterpenes all have a six-membered ring but differ in the binding pattern of the four remaining carbon atoms and the position of the double bond(s). We use a multiconformer transition state theory approach to calculate the rate coefficients of the peroxy radical hydrogen-shift (H-shift) and endoperoxide formation reactions of these peroxy radicals. Our results suggest that primarily the isomers with a carbon–carbon double bond remaining after OH- and O 2 -addition undergo unimolecular reactions with rate coefficients large enough to be of atmospheric importance. This greatly limits the number of potentially important unimolecular…

High Resolution Image Download MS PowerPoint Slide This work demonstrates how push – pull substitution can induce spectral tuning toward the visible range and improve the photoisomerization efficiency of azobenzene-based photoswitches, making them good candidates for technological and biological applications. The red-shifted bright ππ* state (S 2 ) behaves like the lower and more productive dark nπ* (S 1 ) state because less potential energy along the planar bending mode is available to reach higher energy unproductive nπ*/S 0 crossing regions, which are responsible for the lower quantum yield of the parent compound. The stabilization of the bright ππ* state and the consequent increase in isomerization efficiency may be regulated via the strength of push – pull substituents. Finally, the torsional mechanism is recognized here as the unique productive route because structures with bending values attributable to the inversion mechanism were never detected, out of the 280 ππ* time-dependent density functional theory (RASPT2-validated) dynamics simulations.

The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multi-fidelity approach to re-evalute the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen bonding organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1…

We tested the influence of various parameters on the new particle formation rate predicted for the sulfuric acid-ammonia system using quantum chemistry and cluster distribution dynamics simulations, in our case, Atmospheric Cluster Dynamics Code (ACDC). We found that consistent consideration of the rotational symmetry number of monomers (sulfuric acid and ammonia molecules, and bisulfate and ammonium ions) leads to a significant rise in the predicted particle formation rate, whereas inclusion of the rotational symmetry number of the clusters only changes the results slightly, and only in conditions where charged clusters dominate the particle formation rate. This is because most of the clusters stable enough to participate in new particle formation have a rotational symmetry number of 1, and few exceptions to this rule are positively charged clusters. In contrast, the application of the quasi-harmonic correction for low-frequency vibrational modes tends to generally decrease predicted new particle formation rates and also significantly alters the slope of the formation rate curve…

A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic environment descriptors within SNAP are obtained from a basis function expansion of the weighted density of neighboring atoms. This new formulation instead partitions the neighbor density into partial densities for each chemical element, thus leading to explicit multielement descriptors. For N elem chemical elements, the number of descriptors increases as, while the computational cost of the force calculation as implemented in LAMMPS is limited to and the favorable linear scaling in the number of atoms is retained. We demonstrate these chemically aware descriptors by producing an interatomic potential for indium phosphide capable of capturing high-energy defects that result from radiation damage cascades. This new explicit multielement SNAP method reproduces the relaxed defect formation energies with substantially greater accuracy than weighted-density SNAP, while retaining accurate representation of the…

No direct method for estimating the individual O–H···O hydrogen bond (H-bond) energies in water clusters (W n ) exists in the literature. In this work, we propose such a direct method based on the molecular tailoring approach, which also enables the estimation of the cooperativity contributions. The calculated H-bond energies at MP2(full)/aug-cc-pVTZ and CCSD(T)/aug-cc-pVDZ levels for W n, n = 3 to 8, agree well with one another and fall between 0.3 and 11.6 kcal mol –1 with the cooperativity contributions in the range of −1.2 and 7.0 kcal mol –1 . For gauging the accuracy of our H-bond energies for a cluster, the H-bond energy sum is added to the sum of monomer energies, and the results are compared with the respective total energy. These two values agree with each other to within 8.3 mH (∼5 kcal mol –1 ), testifying the accuracy of our estimated H-bond energies. Further, these H-bond strengths show a good correlation with the respective O–H stretching frequencies and the molecular electron density values at the (3, −1) O–H···O H-bond critical point.

A thorough understanding of the kinetics and dynamics of combusting mixtures is of considerable interest, especially in regimes beyond the reach of current experimental validation. The ReaxFF reactive force field method has provided a way to simulate large-scale systems of hydrogen combustion via a parametrized potential that can simulate bond breaking. This modeling approach has been applied to hydrogen combustion, as well as myriad other reactive chemical systems. In this work, we benchmark the performance of several common parametrizations of this potential against higher-level quantum mechanical (QM) approaches. We demonstrate instances where these parametrizations of the ReaxFF potential fail both quantitatively and qualitatively to describe reactive events relevant for hydrogen combustion systems.

Photoexcitation of molecular chromophore aggregates can form excimer states that play a significant role in photophysical processes such as charge and energy transfer as well as singlet fission. An excimer state is commonly defined as a superposition of Frenkel exciton and charge transfer states. In this work, we investigate the dynamics of excimer formation and decay in π-stacked 9,10-bis(phenylethynyl)anthracene (BPEA) covalent dimers appended to a xanthene spacer, where the electronic coupling between the two BPEA molecules is adjusted by changing their longitudinal molecular slip distances. Using exciton coupling calculations, we quantify the relative contributions of Frenkel excitons and charge transfer states and find that there is an upper and lower threshold of the charge transfer contribution for efficient excimer formation to occur. Knowing these thresholds can aid the design of molecular aggregates that optimize singlet fission.

Abstract Avobenzone (AB) is a widely used UVA filter known to undergo irreversible photodegradation. Here, we investigate the detailed pathways by which AB photodegrades by applying UV laser-interfaced mass spectrometry to protonated AB ions. Gas-phase infrared multiple-photon dissociation (IRMPD) spectra obtained with the free electron laser for infrared experiments, FELIX, (600–1800 cm–1) are also presented to confirm the geometric structures. The UV gas-phase absorption spectrum (2.5–5 eV) of protonated AB contains bands that correspond to selective excitation of either the enol or diketo forms, allowing us to probe the resulting, tautomer-dependent photochemistry. Numerous photofragments (i.e., photodegradants) are directly identified for the first time, with m/z 135 and 161 dominating, and m/z 146 and 177 also appearing prominently. Analysis of the production spectra of these photofragments reveals that that strong enol to keto photoisomerism is occurring, and that protonation significantly disrupts the stability of the enol (UVA active) tautomer. Close comparison of fragment…

Fluorescence lifetime imaging (FLIM) has become an important microscopy technique in bioimaging. The two most important of its applications are lifetime-multiplexing for imaging many different structures in parallel, and lifetime-based measurements of Förster resonance energy transfer. There are two principal FLIM techniques, one based on confocal-laser scanning microscopy (CLSM) and time-correlated single-photon counting (TCSPC) and the other based on wide-field microscopy and phase fluorometry. Although the first approach (CLSM-TCSPC) assures high sensitivity and allows one to detect single molecules, it is slow and has a small photon yield. The second allows, in principal, high frame rates (by 2-3 orders of magnitude faster than CLSM), but it suffers from low sensitivity, which precludes its application for single-molecule imaging. Here, we demonstrate that a novel wide-field TCSPC camera (LINCam25, Photonscore GmbH) can be successfully used for single-molecule FLIM, although its quantum yield of detection in the red spectral region is only ∼5%. This is due to the virtually…

This work presents an in-depth discussion on the nonequilibrium dissociation of O 2 molecules colliding with O atoms, combining quasi-classical trajectory calculations, master equation, and dimensionality reduction. A rovibrationally resolved database for all of the elementary collisional processes is constructed by including all nine adiabatic electronic states of O 3 in the QCT calculations. A detailed analysis of the ab initio data set reveals that for a rovibrational level, the probability of dissociating is mostly dictated by its deficit in internal energy compared to the centrifugal barrier. Because of the assumption of rotational equilibrium, the conventional vibrational-specific calculations fail to characterize such a dependence. Based on this observation, a new physics-based grouping strategy for application to coarse-grained models is proposed. By relying on a hybrid technique made of rovibrationally resolved excitation coupled to coarse-grained dissociation, the new approach is compared to the vibrational-specific model and the direct solution of the rovibrational…

Organic reactions in microdroplets can be orders of magnitude faster than their bulk counterparts. We hypothesize that solvation energy differences between bulk and interface play a key role in the intrinsic rate constant increase and test the hypothesis with explicit solvent calculations. We demonstrate for both the protonated phenylhydrazine reagent and the hydrazone transition state (TSB) that molecular orientations which place the charge sites at the surface confer high energy. A pathway in which this high-energy form transforms into a fully solvated TSB has a lower activation energy than bulk by some 59 kJ/mol, a result that is consistent with experimental rate acceleration studies.

After the experimental evidence of polyynic as the stable form of cyclo[18]carbon, in the present paper, using ab initio electronic structure calculations, we show that this result is a symmetry breaking event, a consequence of the second-order Jahn–Teller effect. We show that the eigenfunctions associated with lowest unoccupied molecular orbitals (LUMO) and LUMO + 1, the excited states of this ring molecule, interact with the eigenfunctions associated with the ground state (occupied states), and this interaction stabilizes the less symmetric polyynic form of cyclo[18]carbon with D 9 h symmetry, instead of the cumulenic form. The frontier state interactions are responsible for the distortions in the symmetry in the electronic structures, lowering the energy and making the polyynic form the stable one with alternating triple and single bonds.

The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package. We demonstrate that this method can yield a similar accuracy for both molecules and solids at a much lower computational cost. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly two orders of magnitude compared to QRCP, resulting in a speedup of ∼10 times for ISDF-based HFX calculations.

Absorption spectra of liquid water at 300 K are calculated from both classical and density functional theory molecular dynamics simulation data, which together span from 1 MHz to hundreds of THz, agreeing well with experimental data qualitatively and quantitatively over the entire range, including the IR modes, the microwave peak, and the intermediate THz bands. The spectra are decomposed into single-molecular and collective components, as well as into components due to molecular reorientations and changes in induced molecular dipole moments. These decompositions shed light on the motions underlying the librational and translational (hydrogen-bond stretching) bands at 20 and 5 THz, respectively; interactions between donor protons and acceptor lone pair electrons are shown to be important for the line shape in both librational and translational regimes, and in- and out-of-phase librational dimer modes are observed and explored.

The abnormal level of cysteine (Cys) in the human body will cause a series of diseases, and the study of the sensing mechanism is of great significance for the design of efficient fluorescent probes. Here, we used time-dependent density functional theory to study the sensing mechanism of a newly synthesized imidazo [1,5-α] pyridine-based fluorescent probe (MZC-AC) for the detection of Cys, which is proposed to be designed based on excited-state intramolecular proton transfer (ESIPT). We first show that the fluorescence quenching mechanism of MZC-AC is due to a nonclassical photoinduced electron transfer (PET) process in which the curve crossing between local excited and charge-transfer states is observed and the acrylate group acts as an electron acceptor. When the acrylate group is replaced by the hydroxyl group due to the reaction between MZC-AC and Cys, the PET is off and a significant fluorescence enhancement of the formed MZC is observed. Our theoretical results indicate that the fluorescence enhancement mechanism of MZC is not based on the ESIPT. The calculated potential…

In this contribution, we present a computational protocol to predict anti-Kasha photoluminescence. The herein developed protocol is based on state-of-the-art quantum chemical calculations and excited-state decay rate theories (i.e., thermal vibration correlation function formalism), along with appropriate kinetic models which include all relevant electronic states. This protocol is validated for a series of azulene derivatives. For this series, we have computed absorption and emission spectra for both their first and second excited states, their radiative and nonradiative rates, as well as fluorescence yields from the two different excited states. All the studied azulene derivatives are predicted to exclusively display anomalous anti-Kasha S 2 emission. A quantitative agreement for the herein computed excited-state spectra, lifetimes, and fluorescence quantum yields is obtained with respect to the experimental values. Given the increasing interest in anti-Kasha emitters, we foresee that the herein developed computational protocol can be used to prescreen dyes with the desired…

Isoprene hydroxy hydroperoxides (ISOPOOH) formed by the photooxidation of isoprene under low-NO conditions play an important role in the formation and evolution of secondary organic aerosols, yet multiphase processes of ISOPOOH are poorly understood. By applying electron paramagnetic resonance spectroscopy, we observe that ISOPOOH undergoes aqueous-phase decomposition upon interacting with Fe(II) ions to form OH and organic radicals at room temperature. To reproduce the measured dependence of OH formation on the Fe concentrations by kinetic modeling, we postulate that Fe(II) ions react with ISOPOOH via Fenton-like reactions to form OH radicals with a rate constant of 7.3 × 10 –18 cm 3 s –1 . At low concentrations, oxalate forms monocomplexes with Fe(II) ions, which can promote OH formation by ISOPOOH. However, at high concentrations, oxalate scavenges OH radicals, thereby lowering aqueous OH concentrations. These findings provide new insight for the atmospheric fate of ISOPOOH and reactive oxygen species generation in the aqueous phase.

Single-particle trapping is an effective strategy to explore the physical and optical properties of aerosol with high precision. Laser-based methods are commonly used to probe the size, optical properties, and composition of nonlight-absorbing droplets in optical and electrodynamic traps. However, these methods cannot be applied to droplets containing photoactive chromophores, and thus, single-particle methods have been restricted to only a subset of atmospherically relevant particle compositions. In this work, we explore the application of a broadband light scattering approach, Mie resonance spectroscopy, to simultaneously probe the size and the refractive index (RI) of droplets in a linear quadrupole electrodynamic balance. We examine the evaporation of poly(ethylene glycol)s and compare the calculated vapor pressures with literature values to benchmark the size accuracy without prior constraint on the RI. We then explore the hygroscopic growth and deliquescence of sodium chloride droplets, measuring RI at the deliquescence relative humidity and demonstrating agreement to…

Using chirped and cavity microwave spectroscopies, automated double resonance, new high-speed fitting and deep learning algorithms, and large databases of computed structures, the discharge products of benzene alone, or in combination with molecular oxygen or nitrogen, have been exhaustively characterized between 6.5 and 26 GHz. In total, more than 3300 spectral features were observed; 89% of these, accounting for 97% of the total intensity, have now been assigned to 152 distinct chemical species and 60 of their variants (i.e., isotopic species and vibrationally excited states). Roughly 50 of the products are entirely new or poorly characterized at high resolution, including many heavier by mass than the precursor benzene. These findings provide direct evidence for a rich architecture of two- and three-dimensional carbon and indicate that benzene growth, particularly the formation of ring–chain molecules, occurs facilely under our experimental conditions. The present analysis also illustrates the utility of microwave spectroscopy as a precision tool for complex mixture analysis,…

The use of surface enhanced Raman spectroscopy in the development of low cost, portable sensor devices that can be used in the field for nitroguanidine neonicotinoid insecticide detection is appealing. However, a key challenge to achieving this goal is the lack of detailed analysis and vibrational assignment for the most popular neonicotinoids. To make progress toward this goal, this paper presents an analysis of the bulk Raman and SERS spectra of two neonicotinoids, namely clothianidin and imidacloprid. Combined with first-principles simulations, this allowed assignment of all Raman spectral modes for both molecules. To our knowledge, this is the first report of SERS analysis and vibrational assignment of clothianidin, and a comprehensive assignment and analysis is provided for imidacloprid. Silver nanostructured surfaces were fabricated for qualitative SERS analysis, which provides the characteristic spectra of the target molecules and demonstrates the ability of SERS to sense these molecules at concentrations of 1 ng/mL. These concentrations are on par with high-end…

The response of high-energy-density materials to thermal or mechanical insults involves coupled thermal, mechanical, and chemical processes with disparate temporal and spatial scales that no single model can capture. Therefore, we developed a multiscale model for 1,3,5-trinitro-1,3,5-triazinane, RDX, where a continuum description is informed by reactive and nonreactive molecular dynamics (MD) simulations to describe chemical reactions and thermal transport. Reactive MD simulations under homogeneous isothermal and adiabatic conditions are used to develop a reduced-order chemical kinetics model. Coarse graining is done using unsupervised learning via non-negative matrix factorization. Importantly, the components resulting from the analysis can be interpreted as reactants, intermediates, and products, which allows us to write kinetics equations for their evolution. The kinetics parameters are obtained from isothermal MD simulations over a wide temperature range, 1200-3000 K, and the heat evolved is calibrated from adiabatic simulations. We validate the continuum model against MD…

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models; instead, we introduce various useful concepts and ideas brought by modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging…

Abstract The H + CH3OH reaction, which plays an important role in combustion and the interstellar medium, presents a prototypical system with multi channels and tight transition states. However, no globally reliable potential energy surface (PES) has been available to date. Here we develop global analytical PESs for this system using the permutation-invariant polynomial neural network (PIP-NN) and the high-dimensional neural network (HD-NN) methods based on a large number of data points calculated at the level of the explicitly correlated unrestricted coupled cluster single, double, and perturbative triple level with the augmented correlation corrected valence triple-ζ basis set (UCCSD(T)-F12a/AVTZ). We demonstrate that both machine learning PESs are able to accurately describe all dynamically relevant reaction channels. At a collision energy of 20 kcal/mol, quasi-classical trajectory calculations reveal that the dominant channel is the hydrogen abstraction from the methyl site, yielding H2 + CH2OH. The reaction of this major channel takes place mainly via the direct rebound…

Multireference electronic structure calculations consistent with known experimental data have elucidated a novel mechanism for photo-triggered Ni(II)–C homolytic bond dissociation in Ni 2,2’-bipyridine (bpy) photoredox catalysts. Previously, a thermally assisted dissociation from the lowest energy triplet ligand field excited state was proposed and supported by density functional theory (DFT) calculations that reveal a barrier of ~30 kcal mol-1. In contrast, multireference ab initio calculations suggest this process is disfavored, with barrier heights of ~70 kcal mol-1, and highlight important ligand noninnocent contributions to excited state relaxation and bond dissociation processes that are not captured with DFT. In the multireference description, photo-triggered Ni(II)–C homolytic bond dissociation occurs via initial population of a singlet Ni(II)-to-bpy metal-to-ligand charge transfer (1MLCT) excited state followed by intersystem crossing and aryl-to-Ni(III) charge transfer, overall a formal two-electron transfer process driven by a single photon. This results in repulsive…

Organic peroxy radicals (RO 2 ) are key intermediates in the chemistry of the atmosphere. One of the main sink reactions of RO 2 is the recombination reaction RO 2 + R′O 2, which has three main channels (all with O 2 as a coproduct): (1) R –H ═O + R′OH, (2) RO + R′O, and (3) ROOR′. The RO + R′O “alkoxy” channel promotes radical and oxidant recycling, while the ROOR′ “dimer” channel leads to low-volatility products relevant to aerosol processes. The ROOR′ channel has only recently been discovered to play a role in the gas phase. Recent computational studies indicate that all of these channels first go through an intermediate complex 1 (RO··· 3 O 2 ···OR′). Here, 3 O 2 is very weakly bound and will likely evaporate from the system, giving a triplet cluster of two alkoxy radicals: 3 (RO···OR′). In this study, we systematically investigate the three reaction channels for an atmospherically representative set of RO + R′O radicals formed in the corresponding RO 2 + R′O 2 reaction. First, we systematically sample the possible conformations of the RO···OR′ clusters on the triplet potential…