Heavy Water Microdroplet Surface Enriches the Lighter Isotopologue Impurities.

Water microdroplets promote unusual chemical reactions at the air-water interface. However, the interfacial structure of water microdroplets and its potential influence on chemical processes are still enigmatic. Here, we present evidence of in-droplet fractionation of water isotopologues. Employing a sonic spray, we atomized the heavy water (D2O, 99.9 atom % D) solution of three classes of organic compounds (basic, acidic, and neutral). The analytes were predominantly desorbed from the resulting droplet surface in protonated form rather than deuterated form, as detected by mass spectrometry. This result remained unaltered upon adding formic acid-d2 (DCOOD) to the droplet. Monitoring Dakin oxidation of benzaldehyde at the surface of binary microdroplets composed of 1:1 (v/v) D2O/H218O revealed the preferred formation of phenolate-16O over phenolate-18O. Atmospheric pressure chemical ionization mass spectrometric analysis of the vapor composition in the sprayed aerosol revealed the preferential evaporation of lighter water isotopologue impurities from the surface of heavy water microdroplets. These results indicate the enrichment of lighter water isotopologue impurities (HOD/H2O) on the surface of heavy water microdroplets, implying possible future developments for water isotopologue fractionation using microdroplets.

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.

Microscopic pathways of transition from low-density to high-density amorphous phase of water.

In recent years, much attention has been devoted to understanding the pathways of phase transition between two equilibrium condensed phases (such as liquids and solids). However, the microscopic pathways of transition involving non-equilibrium, non-diffusive amorphous (glassy) phases still remain poorly understood. In this work, we have employed computer simulations, persistence homology (a tool rooted in topological data analysis), and machine learning to probe the microscopic pathway of pressure-induced non-equilibrium transition between the low- and high-density amorphous (LDA and HDA, respectively) ice phases of the TIP4P/2005 and ST2 water models. Using persistence homology and machine learning, we introduced a new order parameter that unambiguously identifies the LDA- and HDA-like local environments. The LDA phase transitions continuously and collectively into the corresponding HDA phase via a pre-ordered intermediate phase during the isothermal compression. The local order parameter susceptibilities show a maximum near the transition pressure (P*)-suggesting maximum structural heterogeneities near P*. The HDA-like clusters are structurally ramified and spatially delocalized inside the LDA phase near the transition pressure. We also found manifestations of the first-order low-density to high-density liquid transition in the sharpness of the order parameter change during the LDA to HDA transition. We further investigated the (geometrical) structures and topologies of the LDA and HDA ices formed via different protocols and also studied the dependence of the (microscopic) pathway of phase transition on the protocol followed to prepare the initial LDA phase. Finally, the method adopted here to study the phase transition pathways is not restricted to the system under consideration and provides a robust way of probing phase transition pathways involving any two condensed phases at both equilibrium and out-of-equilibrium conditions.

Rapid Molecular Evaluation of Human Kidney Tissue Sections by In Situ Mass Spectrometry and Machine Learning to Classify the Nephrotic Syndrome.

Nephrotic syndrome (NS) is classified based on morphological changes of glomeruli in biopsied kidney tissues evaluated by time-consuming microscopy methods. In contrast, we employed desorption electrospray ionization mass spectrometry (DESI-MS) directly on renal biopsy specimens obtained from 37 NS patients to rapidly differentiate lipid profiles of three prevalent forms of NS: IgA nephropathy (n = 9), membranous glomerulonephritis (n = 7), and lupus nephritis (n = 8), along with other types of glomerular diseases (n = 13). As we noted molecular heterogeneity in regularly spaced renal tissue regions, multiple sections from each biopsy specimen were collected, providing a total of 973 samples for investigation. Using multivariate analysis, we report differential expressions of glycerophospholipids, sphingolipids, and glycerolipids among the above four classes of NS kidneys, which were otherwise overlooked in several past studies correlating lipid abnormalities with glomerular diseases. We developed machine learning (ML) models with the top 100 features using the support vector machine, which enabled us to discriminate the concerned glomerular diseases with 100% overall accuracy in the training, validation, and holdout test set. This DESI-MS/ML-based tissue analysis can be completed in a few minutes, in sharp contrast to a daylong procedure followed in the conventional histopathology of NS.

Spontaneous Generation of Aryl Carbocations from Phenols in Aqueous Microdroplets: Aromatic SN1 Reactions at the Air-Water Interface.

Although phenol is stable in bulk water, we report an exceptional phenomenon in which phenol is spontaneously transformed into a phenyl carbocation (Ph+) in water microdroplets. The high electric field at the air-water interface is proposed to break the phenolic Csp2-OH bond, forming Ph+, which remains in equilibrium with phenol as deciphered by mass spectrometry. We detected up to 70% conversion of phenol to Ph+ in aqueous microdroplets, although catalyst-free activation of the phenolic Csp2-OH bond is challenging. This transformation is well tolerated by a wide range of electron-donating and -withdrawing substituents in phenolic compounds. The Ph+ in water microdroplets could be reacted with various nucleophiles (amine, pyridine, azide, thiol, carboxylic acid, alcohol, and 18O-water), yielding the ipso-substitution products of phenol through an aromatic SN1 mechanism. Despite the fleeting life of Ph+ in the bulk, this study demonstrates its unusual stability at the aqueous microdroplet surface, enabling its detection and transformation.

Mass Spectrometry Imaging of Lumpectomy Specimens Deciphers Diacylglycerols as Potent Biomarkers for the Diagnosis of Breast Cancer.

Detecting breast tumor markers with a fast turnaround time from frozen sections should foster intraoperative histopathology in breast-conserving surgery, reducing the need for a second operation. Hence, rapid label-free discrimination of the spatially resolved molecular makeup between cancer and adjacent normal breast tissue is of growing importance. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of fresh-frozen excision specimens, including cancer and paired adjacent normal sections, obtained from the lumpectomy of 73 breast cancer patients. The results demonstrate that breast cancer tissue posits sharp metabolic upregulation of diacylglycerol, a lipid second messenger that activates protein kinase C for promoting tumor growth. We identified four specific sn-1,2-diacylglycerols that outperformed all other lipids simultaneously mapped by the positive ion mode DESI-MSI for distinguishing cancers from adjacent normal specimens. This result contrasts with several previous DESI-MSI studies that probed metabolic dysregulation of glycerophospholipids, sphingolipids, and free fatty acids for cancer diagnoses. A random forest-based supervised machine learning considering all detected ion signals also deciphered the highest diagnostic potential of these four diacylglycerols with the top four importance scores. This led us to construct a classifier with 100% overall prediction accuracy of breast cancer by using the parsimonious set of four diacylglycerol biomarkers only. The metabolic pathway analysis suggested that increased catabolism of phosphatidylcholine in breast cancer contributes to diacylglycerol overexpression. These results open up opportunities for mapping diacylglycerol signaling in breast cancer in the context of novel therapeutic and diagnostic developments, including the intraoperative assessment of breast cancer margin status.

Ar+ ArH+ Reactive Collisions of Astrophysical Interest: The Case of 36 Ar.

The reactive collision between 36 Ar and the 36 ArH+ species has been investigated by means of quantum mechanical (QM), quasiclassical trajectories (QCT) and statistical quantum mechanical (SQM) approaches. Reaction probabilities, cross sections as a function of the energy and rate constants in terms of the temperature have been obtained. Cumulative distributions as a function of the collision time and the inspection of selected QCT corresponding to specific dynamical mechanisms have been analysed. Predictions by means of the SQM method are in good agreement with the QM results, thus supporting the complex-forming nature of the process.

Low-temperature kinetics for the N + NO reaction: experiment guides the way.

The reaction N(4S) + NO(X2Π) → O(3P) + N2(X1Σ+g) plays a pivotal role in the conversion of atomic to molecular nitrogen in dense interstellar clouds and in the atmosphere. Here we report a joint experimental and computational investigation of the N + NO reaction with the aim of providing improved constraints on its low temperature reactivity. Thermal rates were measured over the 50 to 296 K range in a continuous supersonic flow reactor coupled with pulsed laser photolysis and laser induced fluorescence for the production and detection of N(4S) atoms, respectively. With decreasing temperature, the experimentally measured reaction rate was found to monotonously increase up to a value of (6.6 ± 1.3) × 10-11 cm3 s-1 at 50 K. To confirm this finding, quasi-classical trajectory simulations were carried out on a previously validated, full-dimensional potential energy surface (PES). However, around 50 K the computed rates decreased which required re-evaluation of the reactive PES in the long-range part due to a small spurious barrier with a height of ∼40 K in the entrance channel. By exploring different correction schemes the measured thermal rates can be adequately reproduced, displaying a clear negative temperature dependence over the entire temperature range. The possible astrochemical implications of an increased reaction rate at low temperature are also discussed.

Molecular-level understanding of the rovibrational spectra of N2O in gaseous, supercritical, and liquid SF6 and Xe.

The transition between the gas-, supercritical-, and liquid-phase behavior is a fascinating topic, which still lacks molecular-level understanding. Recent ultrafast two-dimensional infrared spectroscopy experiments suggested that the vibrational spectroscopy of N2O embedded in xenon and SF6 as solvents provides an avenue to characterize the transitions between different phases as the concentration (or density) of the solvent increases. The present work demonstrates that classical molecular dynamics (MD) simulations together with accurate interaction potentials allows us to (semi-)quantitatively describe the transition in rotational vibrational infrared spectra from the P-/R-branch line shape for the stretch vibrations of N2O at low solvent densities to the Q-branch-like line shapes at high densities. The results are interpreted within the classical theory of rigid-body rotation in more/less constraining environments at high/low solvent densities or based on phenomenological models for the orientational relaxation of rotational motion. It is concluded that classical MD simulations provide a powerful approach to characterize and interpret the ultrafast motion of solutes in low to high density solvents at a molecular level.

Machine learning product state distributions from initial reactant states for a reactive atom-diatom collision system.

A machine-learned model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented and quantitatively tested for the N(4S) + O2(X3Σg -) → NO(X2Π) + O(3P) reaction. The reference dataset for training the neural network consists of final state distributions determined from quasi-classical trajectory (QCT) simulations for ∼2000 initial conditions. Overall, the prediction accuracy as quantified by the root-mean-squared difference (∼0.003) and the R2 (∼0.99) between the reference QCT and predictions of the STD model is high for the test set, for off-grid state-specific initial conditions, and for initial conditions drawn from reactant state distributions characterized by translational, rotational, and vibrational temperatures. Compared with a more coarse grained distribution-to-distribution (DTD) model evaluated on the same initial state distributions, the STD model shows comparable performance with the additional benefit of the state resolution in the reactant preparation. Starting from specific initial states also leads to a more diverse range of final state distributions, which requires a more expressive neural network compared with DTD. A direct comparison between QCT simulations, the STD model, and the widely used Larsen-Borgnakke (LB) model shows that the STD model is quantitative, whereas the LB model is qualitative at best for rotational distributions P(j') and fails for vibrational distributions P(v'). As such, the STD model can be well-suited for simulating nonequilibrium high-speed flows, e.g., using the direct simulation Monte Carlo method.

Photodissociation dynamics of N3.

The photodissociation dynamics of N3 + excited from its (linear) 3Σg -/(bent) 3A″ ground to the first excited singlet and triplet states is investigated. Three-dimensional potential energy surfaces for the 1A', 1A″, and 3A' electronic states, correlating with the 1Δg and 3Πu states in linear geometry, for N3 + are constructed using high-level electronic structure calculations and represented as reproducing kernels. The reference ab initio energies are calculated at the MRCI+Q/aug-cc-pVTZ level of theory. For following the photodissociation dynamics in the excited states, rotational and vibrational distributions P(v') and P(j') for the N2 product are determined from vertically excited ground state distributions. Due to the different shapes of the ground state 3A″ potential energy surface and the excited states, appreciable angular momentum j' ∼ 60 is generated in diatomic fragments. The lifetimes in the excited states extend to at least 50 ps. Notably, results from sampling initial conditions from a thermal ensemble and from the Wigner distribution of the ground state wavefunction are comparable.

The C(3P) + O2(3Σg-) → CO2 ↔ CO(1Σ+) + O(1D)/O(3P) reaction: thermal and vibrational relaxation rates from 15 K to 20 000 K.

Thermal rates for the C(3P) + O2(3Σg-) ↔ CO(1Σ+)+ O(1D)/O(3P) reaction are investigated over a wide temperature range based on quasi classical trajectory (QCT) simulations on 3-dimensional, reactive potential energy surfaces (PESs) for the 1A', (2)1A', 1A'', 3A' and 3A'' states. These five states are the energetically low-lying states of CO2 and their PESs are computed at the MRCISD+Q/aug-cc-pVTZ level of theory using a state-average CASSCF reference wave function. Analysis of the different electronic states for the CO2 → CO + O dissociation channel rationalizes the topography of this region of the PESs. The forward rates from QCT simulations match measurements between 15 K and 295 K whereas the equilibrium constant determined from the forward and reverse rates is consistent with that derived from statistical mechanics at high temperature. Vibrational relaxation, O + CO(ν = 1,2) → O + CO(ν = 0), is found to involve both, non-reactive and reactive processes. The contact time required for vibrational relaxation to take place is τ ≥ 150 fs for non-reacting and τ ≥ 330 fs for reacting (oxygen atom exchange) trajectories and the two processes are shown to probe different parts of the global potential energy surface. In agreement with experiments, low collision energy reactions for the C(3P) + O2(3Σg-, ν = 0) → CO(1Σ+) + O(1D) lead to CO(1Σ+, ν' = 17) with an onset at Ec ∼ 0.15 eV, dominated by the 1A' surface with contributions from the 3A' surface. Finally, the barrier for the COA(1Σ+) + OB(3P) → COB(1Σ+) + OA(3P) atom exchange reaction on the 3A' PES yields a barrier of ∼7 kcal mol-1 (0.300 eV), consistent with an experimentally reported value of 6.9 kcal mol-1 (0.299 eV).

Quantum and Classical Dynamics of the N(2D) + N2 Reaction on Its Ground Doublet State N3(12A″) Potential Energy Surface.

The initial state-selected dynamics of the N(2D) + N2(X1∑) → N2(X1∑) + N(2D) exchange reaction on its electronic ground doublet state N3(12A″) potential energy surface (PES) has been studied here by time-dependent quantum mechanics (TDQM) and quasi-classical trajectory (QCT) methods. Dynamical attributes such as total reaction probabilities, state-selected integral cross sections, and initial state-selected rate constants have been calculated. The presence of metastable quasi-bound complexes in the collision process is confirmed by substantial oscillatory structures in the reaction probability curves. Also, rotational excitations of reagent N2 on the reactivity have been examined by calculating the probabilities for the two-body rotational angular momentum up to j = 10. We conclude that the reagent rotational excitation increases the reactivity. The TDQM results are compared with QCT results.

Quantum and quasiclassical dynamical simulations for the Ar2H+ on a new global analytical potential energy surface.

A new analytical potential energy surface (PES) has been constructed for the Ar2H+ system from a dataset consisting of a large number of ab initio energies computed using the coupled-cluster singles, doubles and perturbative triples method and aug-cc-pVQZ basis set. The long-range interaction is added to the diatomic potentials using a standard long range expansion form to better describe the asymptotic regions. The vibrational states for the most stable structures of the Ar2H+ system have been calculated, and few low lying states are assigned to quantum numbers. Reactive scattering studies have been performed for the Ar + Ar'H+ → Ar' + ArH+ proton exchange reaction on the newly generated PES. Reaction probability, cross sections, and rate constants are calculated for the Ar + Ar'H+(v = 0, j = 0) collisions within 0.01 eV-0.6 eV of relative translational energy using exact quantum dynamical simulations as well as quasiclassical trajectory (QCT) calculations. The effect of vibrational excitation of the reactants is also explored for the reaction. State averaged rate constants are calculated for the proton exchange reaction at different temperatures using the QCT method. The mechanistic pathways for the reaction are understood by analyzing the quasiclassical trajectories.

Atom-Diatom Reactive Scattering Collisions in Protonated Rare Gas Systems.

The study of the dynamics of atom-diatom reactions involving two rare gas (Rg) atoms and protons is of crucial importance given the astrophysical relevance of these processes. In a series of previous studies, we have been investigating a number of such Rg(1)+ Rg(2)H+→ Rg(2)+ Rg(1)H+ reactions by means of different numerical approaches. These investigations comprised the construction of accurate potential energy surfaces by means of ab initio calculations. In this work, we review the state-of-art of the study of these protonated Rg systems making special emphasis on the most relevant features regarding the dynamical mechanisms which govern these reactive collisions. The aim of this work therefore is to provide an as complete as possible description of the existing information regarding these processes.

Accurate reproducing kernel-based potential energy surfaces for the triplet ground states of N2O and dynamics for the N + NO ↔ O + N2 and N2 + O → 2N + O reactions.

Accurate potential energy surfaces (PESs) have been determined for the 3A' and 3A'' states of N2O using electronic structure calculations at the multireference configuration interaction level with Davidson correction (MRCI+Q) and the augmented Dunning-type correlation consistent polarized triple zeta (aug-cc-pVTZ) basis set. More than 20 000 MRCI+Q/aug-cc-pVTZ energies are represented using a reproducing kernel Hilbert space (RKHS) scheme. The RKHS PESs successfully describe all reactant channels with high accuracy and all minima and transition states connecting them are determined. Quasiclassical trajectory (QCT) simulations are then used to determine reaction rates for N + NO and O + N2 collisions. Vibrational relaxation N2(ν = 1) → N2(ν = 0) and dissociation of N2→ 2N for O + N2 collisions are also investigated using QCT. The agreement between results obtained from the QCT simulations and from available experiments is favourable for reaction and vibrational relaxation rates, which provides a test for the accuracy of the PESs. The PESs can be used to calculate more detailed state-to-state observables relevant for applications to hypersonic reentry.

The N(4S) + O2(X3Σ) ↔ O(3P) + NO(X2Π) reaction: thermal and vibrational relaxation rates for the 2A', 4A' and 2A'' states.

The kinetics and vibrational relaxation of the N(4S) + O2(X3Σ-g) ↔ O(3P) + NO(X2Π) reaction is investigated over a wide temperature range based on quasiclassical trajectory simulations on 3-dimensional potential energy surfaces (PESs) for the lowest three electronic states. Reference energies at the multi reference configuration interaction level are represented as a reproducing kernel and the topology of the PESs is rationalized by analyzing the CASSCF wavefunction of the relevant states. The forward rate matches one measurement at 1575 K and is somewhat lower than the high-temperature measurement at 2880 K whereas for the reverse rate the computations are in good agreement for temperatures between 3000 and 4100 K. The temperature-dependent equilibrium rates are consistent with results from JANAF and CEA results. Vibrational relaxation rates for O + NO(ν = 1) → O + NO(ν = 0) are consistent with a wide range of experiments. This process is dominated by the dynamics on the 2A' and 4A' surfaces which both contribute similarly up to temperatures T ∼ 3000 K, and it is found that vibrationally relaxing and non-relaxing trajectories probe different parts of the potential energy surface. The total cross section depending on the final vibrational state monotonically decreases which is consistent with early experiments and previous simulations but at variance with other recent experiments which reported an oscillatory cross section.

Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics.

The determination of thermal and vibrational relaxation rates of triatomic systems suitable for application in hypersonic model calculations is discussed. For this, potential energy surfaces for ground and electronically excited state species need to be computed and represented with high accuracy, and quasiclassical or quantum nuclear dynamics simulations provide the basis for determining the relevant rates. These include thermal reaction rates, state-to-state cross sections, and vibrational relaxation rates. For exemplary systems (i.e., [NNO], [NOO], and [CNO]), all individual steps are described, and a literature overview for them is provided. Finally, as some of these quantities involve considerable computational expense, for the example of state-to-state cross sections, the construction of an efficient model based on neural networks is discussed. All such data is required and being used in more coarse-grained computational fluid dynamics simulations.

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions.

Machine learning based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel-, and grid-based representations of the reactant and product state distributions. All three methods predict final state distributions from explicit quasi-classical trajectory simulations with R2 > 0.998. Although a function-based approach is found to be more than two times better in computational performance, the grid-based approach is preferred in terms of prediction accuracy, practicability, and generality. For the function-based approach, the choice of parametrized functions is crucial and this aspect is explicitly probed for final vibrational state distributions. Applications of the grid-based approach to nonequilibrium, multitemperature initial state distributions are presented, a situation common to energy and state distributions in hypersonic flows. The role of such models in direct simulation Monte Carlo and computational fluid dynamics simulations is also discussed.

Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet.

Machine learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a comparative assessment of ML models based on state-of-the-art variants of deep neural networks (NNs), reproducing kernel Hilbert space (RKHS+F), and kernel ridge regression (KRR) is presented. Learning curves for energies and atomic forces indicate rapid convergence toward excellent predictions for B3LYP, MP2, and CCSD(T)-F12 reference results for modestly sized (in the hundreds) training sets. Typically, learning curve offsets decay as one goes from NN (PhysNet) to RKHS+F to KRR (FCHL). Conversely, the predictive power for extrapolation of energies toward new geometries increases in the same order with RKHS+F and FCHL performing almost equally. For harmonic vibrational frequencies, the picture is less clear, with PhysNet and FCHL yielding accuracies of ∼1 and ∼0.2 cm-1, respectively, no matter which reference method, while RKHS+F models level off for B3LYP and exhibit continued improvements for MP2 and CCSD(T)-F12. Finite-temperature molecular dynamics (MD) simulations using the PESs from the three ML methods with identical initial conditions yield indistinguishable infrared spectra with good performance compared with experiment except for the high-frequency modes involving hydrogen stretch motion which is a known limitation of MD for vibrational spectroscopy. For sufficiently large training set sizes, all three models can detect insufficient convergence ("noise") of the reference electronic structure calculations in that the learning curves level off. Transfer learning (TL) from B3LYP to CCSD(T)-F12 with PhysNet indicates that additional improvements in data efficiency can be achieved.