On the sensitivity of computed partial charges toward basis set and (exchange-)correlation treatment.

Partial charges are a central concept in general chemistry and chemical biology, yet dozens of different computational definitions exist. In prior work [Cho et al., ChemPhysChem 21, 688-696 (2020)], we showed that these can be reduced to at most three 'principal components of ionicity'. The present study addressed the dependence of computed partial charges q on 1-particle basis set and (for WFT methods) n -particle correlation treatment or (for DFT methods) exchange-correlation functional, for several representative partial charge definitions such as QTAIM, Hirshfeld, Hirshfeld-I, HLY (electrostatic), NPA, and GAPT. Our findings show that semi-empirical double hybrids can closely approach the CCSD(T) 'gold standard' for this property. In fact, owing to an error compensation in MP2, CCSD partial charges are further away from CCSD(T) than is MP2. The nonlocal correlation is important, especially when there is a substantial amount of nonlocal exchange. Employing range separation proves to be "mostly" not advantageous, while global hybrids perform optimally for 20%-30% Hartree-Fock exchange across all charge types. Basis set convergence analysis shows that an augmented triple-zeta heavy-aug-cc-pV(T+d)Z basis set or a partially augmented jun-cc-pV(T+d)Z basis set is sufficient for Hirshfeld, Hirshfeld-I, HLY, and GAPT charges. In contrast, QTAIM and NPA display slower basis set convergence. It is noteworthy that for both NPA and QTAIM, HF exhibits markedly slower basis set convergence than the correlation components of MP2 and CCSD. Triples corrections in CCSD(T), denoted as CCSD(T)-CCSD, exhibit even faster basis set convergence.

A density-fitting implementation of the density-based basis-set correction method.

This work reports an efficient density-fitting implementation of the density-based basis-set correction (DBBSC) method in the MOLPRO software. This method consists in correcting the energy calculated by a wave-function method with a given basis set by an adapted basis-set correction density functional incorporating the short-range electron correlation effects missing in the basis set, resulting in an accelerated convergence to the complete-basis-set limit. Different basis-set correction density-functional approximations are explored and the complementary-auxiliary-basis-set single-excitation correction is added. The method is tested on a benchmark set of reaction energies at the second-order Møller-Plesset (MP2) level and a comparison with the explicitly correlated MP2-F12 method is provided. The results show that the DBBSC method greatly accelerates the basis convergence of MP2 reaction energies, without reaching the accuracy of the MP2-F12 method but with a lower computational cost.

Quantum mechanical study of transition metal hydrides: Comparison of determined molecular properties with experimental data.

This study compares results of four relativistic pseudopotential basis sets, which differ mainly by their size: double-zeta introduced by Hay and Wadt from Los Alamos National Laboratory (LANL2DZ), triple-zeta based on Stuttgart energy-consistent scalar-relativistic pseudopotential (SDD3), its extension with 2fg polarization functions, and combination of Stuttgart pseudopotentials with quintuple-zeta cc-pV5Z base (SDD5). Hydrides of transition metals from Cr to Zn group are chosen as reference molecules. The coupled cluster method (CCSD(T)) is used for evaluation of selected molecular characteristics. Interatomic distances, dissociation energies, vibration modes, and anharmonicity constants are determined and compared with available experimental data. As expected, the accuracy of basis depends mainly on its size. However, only moderate modification of SDD3 basis set significantly improves its accuracy, which becomes comparable to the largest basis set. Nevertheless, the time consumption is significantly lower.

Improved reproducibility of γ-aminobutyric acid measurement from short-echo-time proton MR spectroscopy by linewidth-matched basis sets in LCModel.

γ-Aminobutyric acid (GABA), as the primary inhibitory neurotransmitter, is extremely important for maintaining healthy brain function, and deviations from GABA homeostasis are related to various brain diseases. Short-echo-time (short-TE) proton MR spectroscopy (1 H-MRS) has been employed to measure GABA concentration from various human brain regions at high magnetic fields. The aim of this study was to investigate the effect of spectral linewidth on GABA quantification and explore the application of an optimized basis-set preparation approach using a spectral-linewidth-matched (LM) basis set in LCModel to improve the reproducibility of GABA quantification from short-TE 1 H-MRS. In contrast to the fixed-linewidth basis-set approach, the LM basis-set preparation approach, where all metabolite basis spectra were simulated with a linewidth 4 Hz narrower than that of water, showed a smaller standard deviation of estimated GABA concentration from synthetic spectra with varying linewidths and lineshapes. The test-retest reproducibility was assessed by the mean within-subject coefficient of variation, which improved from 19.2% to 12.0% in the thalamus, from 27.9% to 14.9% in the motor cortex, and from 9.7% to 2.8% in the medial prefrontal cortex using LM basis sets at 7 T. We conclude that spectral linewidth has a large effect on GABA quantification from short-TE 1 H-MRS data and that using LM basis sets in LCModel can improve the reproducibility of GABA quantification.

Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling.

A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.

An emergent temporal basis set robustly supports cerebellar time-series learning.

The cerebellum is considered a "learning machine" essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum's input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.NEW & NOTEWORTHY During movement, mossy fiber inputs to the cerebellum relay time-varying information with strong intrinsic relationships to ongoing movement. Are such mossy fibers signals sufficient to support Purkinje signals and learning? In a model, we show how the GCL greatly improves Purkinje learning of complex, temporally dynamic signals relative to mossy fibers alone. Learning-optimized GCL population activity was moderately dense, which retained intrinsic input variance while also performing pattern separation.

Development and testing of an algorithm for efficient MP2/CCSD(T) energy estimation of molecular clusters with the 2-body approach.

This work reports the development and testing of an automated algorithm for estimating the energies of weakly bound molecular clusters employing correlated theory. Firstly, the monomers and dimers of (homo/hetero) clusters are identified, and the sum of one-body and two-body contributions to correlation energy is calculated. The addition of this contribution to the Hartree-Fock full calculation (FC) energies provides a good estimate of the total energies at Møller-Plesset second-order perturbation theory (MP2)/coupled-cluster method with singles and doubles (CCSD) (T)-level theory using augmented Dunning basis sets. The estimated energies for several test clusters show an excellent agreement with their FC counterparts, with a substantial wall-clock time saving employing off-the-shelf hardware. Furthermore, the complete basis set (CBS) limit for MP2 energy computed using the two-body approach also agrees with its CBS energy with its FC counterpart.

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm.

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

The Challenge of ab Initio Calculations in Small Neon Clusters.

Weakly bound neon dimer, trimer and tetramers are studied at HF and CCSD(T) levels using Dunning, ANO and SIGMA-s basis sets. Their ground-state binding energies are studied along with some structural properties. SIGMA-s basis sets have been developed explicitly for this issue but in a manner that can be readily applied to other atoms for the study of larger weakly bound systems. The difficulties for attaining accurate results on these systems are assessed by the computation of total, atomization and correlation energies, as well as equilibrium distances, with several basis sets of increasing size, ranging from non-augmented to double-augmented versions. Extrapolations are proposed to predict stabilization energies and the results are compared with previously published data.

Impact of Biomass Burning Organic Aerosol Volatility on Smoke Concentrations Downwind of Fires.

Biomass burning particulate matter (BBPM) affects regional air quality and global climate, with impacts expected to continue to grow over the coming years. We show that studies of North American fires have a systematic altitude dependence in measured BBPM normalized excess mixing ratio (NEMR; ΔPM/ΔCO), with airborne and high-altitude studies showing a factor of 2 higher NEMR than ground-based measurements. We report direct airborne measurements of BBPM volatility that partially explain the difference in the BBPM NEMR observed across platforms. We find that when heated to 40-45 °C in an airborne thermal denuder, 19% of lofted smoke PM1 evaporates. Thermal denuder measurements are consistent with evaporation observed when a single smoke plume was sampled across a range of temperatures as the plume descended from 4 to 2 km altitude. We also demonstrate that chemical aging of smoke and differences in PM emission factors can not fully explain the platform-dependent differences. When the measured PM volatility is applied to output from the High Resolution Rapid Refresh Smoke regional model, we predict a lower PM NEMR at the surface compared to the lofted smoke measured by aircraft. These results emphasize the significant role that gas-particle partitioning plays in determining the air quality impacts of wildfire smoke.

Model Framework for Predicting Semivolatile Organic Material Emissions Indoors from Organic Aerosol Measurements: Applications to HOMEChem Stir-Frying.

Cooking activities emit myriad low-volatility, semivolatile, and highly volatile organic compounds that together form particles that can accumulate to large indoor concentrations. Absorptive partitioning thermodynamics governs the particle-phase organic aerosol concentration mainly via temperature and sorbing mass impacts. Cooking activities can increase the organic sorbing mass by 1-2 orders of magnitude, increasing particle-phase concentrations and affecting emission rate calculations. Although recent studies have begun to probe the volatility characteristics of indoor cooking particles, parametrizations of cooking particle mass emissions have largely neglected these thermodynamic considerations. Here, we present an improved thermodynamics-based model framework for estimating condensable organic material emission rates from a time series of observed concentrations, given that adequate measurements or assumptions can be made about the volatility of the emitted species. We demonstrate the performance of this methodology by applying data from stir-frying experiments performed during the House Observations of Microbial and Environmental Chemistry (HOMEChem) campaign to a two-zone box model representing the UTest House. Preliminary estimates of organic mass emitted on a per-stir-fry basis for three types of organic aerosol factors are presented. Our analysis highlights that using traditional nonvolatile particle models and emission characterizations for some organic aerosol emitting activities can incorrectly attribute concentration changes to emissions rather than thermodynamic effects.

Drivers of High Concentrations of Secondary Organic Aerosols in Northern China during the COVID-19 Lockdowns.

During the COVID-19 lockdown in early 2020, observations in Beijing indicate that secondary organic aerosol (SOA) concentrations increased despite substantial emission reduction, but the reasons are not fully explained. Here, we integrate the two-dimensional volatility basis set into a state-of-the-art chemical transport model, which unprecedentedly reproduces organic aerosol (OA) components resolved by the positive matrix factorization based on aerosol mass spectrometer observations. The model shows that, for Beijing, the emission reduction during the lockdown lowered primary organic aerosol (POA)/SOA concentrations by 50%/18%, while deteriorated meteorological conditions increased them by 30%/119%, resulting in a net decrease in the POA concentration and a net increase in the SOA concentration. Emission reduction and meteorological changes both led to an increased OH concentration, which accounts for their distinct effects on POA and SOA. SOA from anthropogenic volatile organic compounds and organics with lower volatility contributed 28 and 62%, respectively, to the net SOA increase. Different from Beijing, the SOA concentration decreased in southern Hebei during the lockdown because of more favorable meteorology. Our findings confirm the effectiveness of organic emission reductions and meanwhile reveal the challenge in controlling SOA pollution that calls for large organic precursor emission reductions to rival the adverse impact of OH increase.

Comparison of different density functional theory methods for the calculation of vibrational circular dichroism spectra.

The determination of the absolute configuration (AC) of an organic molecule is still a challenging task for which the combination of spectroscopic with quantum-mechanical methods has become a promising approach. In this study, we investigated the accuracy of DFT methods (480 overall combinations of 15 functionals, 16 basis sets, and 2 solvation models) to calculate the VCD spectra of six chiral organic molecules in order to benchmark their capability to facilitate the determination of the AC.

On the Utmost Importance of the Basis Set Choice for the Calculations of the Relativistic Corrections to NMR Shielding Constants.

The investigation of the sensitivity of the relativistic corrections to the NMR shielding constants (σ) to the configuration of angular spaces of the basis sets used on the atoms of interest was carried out within the four-component density functional theory (DFT). Both types of relativistic effects were considered, namely the so-called heavy atom on light atom and heavy atom on heavy atom effects, though the main attention was paid to the former. As a main result, it was found that the dependence of the relativistic corrections to σ of light nuclei (exemplified here by 1H and 13C) located in close vicinity to a heavy atom (exemplified here by In, Sn, Sb, Te, and I) on the basis set used on the light spectator atom was very much in common with that of the Fermi-contact contribution to the corresponding nonrelativistic spin-spin coupling constant (J). In general, it has been shown that the nonrelativistic J-oriented and σ-oriented basis sets, artificially saturated in the tight s-region, provided much better accuracy than the standard nonrelativistic σ-oriented basis sets when calculating the relativistic corrections to the NMR shielding constants of light nuclei at the relativistic four-component level of the DFT theory.

New pecJ-n (n = 1, 2) Basis Sets for Selenium Atom Purposed for the Calculations of NMR Spin-Spin Coupling Constants Involving Selenium.

We present new compact pecJ-n (n = 1, 2) basis sets for the selenium atom developed for the quantum-chemical calculations of NMR spin-spin coupling constants (SSCCs) involving selenium nuclei. These basis sets were obtained at the second order polarization propagator approximation with coupled cluster singles and doubles amplitudes (SOPPA(CCSD)) level with the property-energy consistent (PEC) method, which was introduced in our previous papers. The existing SSCC-oriented selenium basis sets are rather large in size, while the PEC method gives more compact basis sets that are capable of providing accuracy comparable to that reached using the property-oriented basis sets of larger sizes generated with a standard even-tempered technique. This is due to the fact that the PEC method is very different in its essence from the even-tempered approaches. It generates new exponents through the total optimization of angular spaces of trial basis sets with respect to the property under consideration and the total molecular energy. New basis sets were tested on the coupled cluster singles and doubles (CCSD) calculations of SSCCs involving selenium in the representative series of molecules, taking into account relativistic, solvent, and vibrational corrections. The comparison with the experiment showed that the accuracy of the results obtained with the pecJ-2 basis set is almost the same as that provided by a significantly larger basis set, aug-cc-pVTZ-J, while that achieved with a very compact pecJ-1 basis set is only slightly inferior to the accuracy provided by the former.

Automatic generation of complementary auxiliary basis sets for explicitly correlated methods.

Explicitly correlated calculations, aside from the orbital basis set, typically require three auxiliary basis sets: Coulomb-exchange fitting (JK), resolution of the identity MP2 (RI-MP2), and complementary auxiliary basis set (CABS). If unavailable for the orbital basis set and chemical elements of interest, the first two can be auto-generated on the fly using existing algorithms, but not the third. In this paper, we present a quite simple algorithm named autoCABS; a Python implementation under a free software license is offered at Github. For the cc-pVnZ-F12 (n = D,T,Q,5), the W4-08 thermochemical benchmark, and the HFREQ2014 set of harmonic frequencies, we demonstrate that autoCABS-generated CABS basis sets are comparable in quality to purpose-optimized OptRI basis sets from the literature, and that the quality difference becomes entirely negligible as n increases.

Behavior of counterpoise correction in many-body molecular clusters of organic compounds: Hartree-Fock interaction energy perspective.

The counterpoise (CP) correction by Boys and Bernardi has been well accepted as a reliable strategy to account for basis set superposition error (BSSE) in intermolecular complexes. The behavior of the CP correction was thoroughly studied in individual molecules of molecular complexes. This work studies the performance of the CP correction in many-body clusters including three-body clusters of organic compounds in the 3B-69 dataset. Additionally, we used crystal structures of polymorphs of benzene, aspirin, and oxalyl dihydrazide (ODH) to construct a many-body cluster dataset, abbreviated as the MBC-36 dataset, consisting of two, four and eight molecules, and 16 molecules in the case of benzene. A series of Dunning's basis sets-cc-pXZ and aug-cc-pXZ (X = D and T)-were used to predict CP-corrected Hartree-Fock (HF) interaction energies of the 3B-69 and MBC-36 datasets. The CP-corrected interaction energies were found to be basis-set independent, whereas the non-CP corrected interaction energies were found not to a follow a smooth exponential fitting as previously found for electronic energies of individual molecules. This observation was attributed to the presence of non-additive induction forces in some clusters. Two 2 × 2 × 2 supercells of benzene polymorphs were constructed to explore the local nature of BSSE effects. A cut-off radius of 10 Å was demonstrated to be sufficient to fully recover these effects. Although the behavior of CP correction was found to be non-conventional in many-body clusters of organic compounds, the use of a small basis set such as cc-pVDZ showed excellent performance in the prediction of HF interaction energies.

Improving the scaling and performance of multiple time stepping-based molecular dynamics with hybrid density functionals.

Density functionals at the level of the generalized gradient approximation (GGA) and a plane-wave basis set are widely used today to perform ab initio molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of density functionals from GGA (second rung) to hybrid density functionals (fourth rung) is much desired pertaining to the accuracy of the latter in describing structure, dynamics, and energetics of molecular and condensed matter systems. On the other hand, hybrid density functional based AIMD simulations are about two orders of magnitude slower than GGA based AIMD for systems containing ~100 atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on a multiple time step integrator and adaptively compressed exchange operator formalism are able to provide a speed-up of about 7-9 in performing hybrid density functional based AIMD. In this work, we report an implementation of these methods using a task-group based parallelization within the CPMD program package, with the intention to take advantage of the large number of compute cores available on modern high-performance computing platforms. We present here the boost in performance achieved through this algorithm. This work also identifies the computational bottleneck in the s-MTACE method and proposes a way to overcome it.

Tuning the quasi-harmonic treatment of crystalline ionic liquids within the density functional theory.

Five ionic liquids are selected for benchmarking the performance of quasi-harmonic density functional theory (DFT) calculations of structural, phonon, and thermodynamic properties of their crystals. Data predicted by individual computational setups are sorted, establishing a distinct hierarchy among the first-principles approaches. PBE-D3 and B3LYP-D3 functionals are coupled with various plane wave and Gaussian-type orbital (GTO) basis sets. Propagation of the basis set superposition error and of the imperfections of both functionals into finite-temperature properties is discussed in detail. PBE-D3 together with a triple-zeta GTO basis set often yields the most accurate predictions of predicted molar volume and heat capacity with errors at 1% and 8%, respectively, representing the state-of-the-art for quasi-harmonic DFT calculations for crystalline ionic liquids. Fortuitous error cancellation between the basis-set superposition (overbinding) and PBE imperfection (overexpanding) strongly affects the overall accuracy, unlike the case of B3LYP/GTO calculations, impeding systematic convergence of the methodology towards higher accuracy.

MRSCloud: A cloud-based MRS tool for basis set simulation.

PURPOSE: The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T. METHODS: The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio. RESULTS: Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96. CONCLUSIONS: Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.