The accuracy of a dispersion-corrected density functional theory method is validated against 241 experimental organic crystal structures from Acta Cryst. Section E.
This paper describes the validation of a dispersion-corrected density functional theory (d-DFT) method for the purpose of assessing the correctness of experimental organic crystal structures and enhancing the information content of purely experimental data. 241 experimental organic crystal structures from the August 2008 issue of Acta Cryst. Section E were energy-minimized in full, including unit-cell parameters. The differences between the experimental and the minimized crystal structures were subjected to statistical analysis. The r.m.s. Cartesian displacement excluding H atoms upon energy minimization with flexible unit-cell parameters is selected as a pertinent indicator of the correctness of a crystal structure. All 241 experimental crystal structures are reproduced very well: the average r.m.s. Cartesian displacement for the 241 crystal structures, including 16 disordered structures, is only 0.095 Å (0.084 Å for the 225 ordered structures). R.m.s. Cartesian displacements above 0.25 Å either indicate incorrect experimental crystal structures or reveal interesting structural features such as exceptionally large temperature effects, incorrectly modelled disorder or symmetry breaking H atoms. After validation, the method is applied to nine examples that are known to be ambiguous or subtly incorrect.
dispersion-corrected density functional theory; organic structures
We have computationally investigated the structure and stability of all 16 combinations of two out of the four natural DNA bases A, T, G and C in a di-2′-deoxyribonucleoside-monophosphate model DNA strand as well as in 10 double-strand model complexes thereof, using dispersion-corrected density functional theory (DFT-D). Optimized geometries with B-DNA conformation were obtained through the inclusion of implicit water solvent and, in the DNA models, of sodium counterions, to neutralize the negative charge of the phosphate groups. The results obtained allowed us to compare the relative stability of isomeric single and double strands. Moreover, the energy of the Watson–Crick pairing of complementary single strands to form double-helical structures was calculated. The latter furnished the following increasing stability trend of the double-helix formation energy: d(TpA)2
density functional calculations; DNA structures; hydrogen bonds; stacking interactions; Watson–Crick base pairs
NiS, exhibiting a text-book example of a first-order transition with many unusual properties at low temperatures, has been variously described in terms of conflicting descriptions of its ground state during the past several decades. We calculate these physical properties within first-principle approaches based on the density functional theory and conclusively establish that all experimental data can be understood in terms of a rather unusual ground state of NiS that is best described as a self-doped, nearly compensated, antiferromagnetic metal, resolving the age-old controversy. We trace the origin of this novel ground state to the specific details of the crystal structure, band dispersions and a sizable Coulomb interaction strength that is still sub-critical to drive the system in to an insulating state. We also show how the specific antiferromagnetic structure is a consequence of the less-discussed 90° and less than 90° superexchange interactions built in to such crystal structures.
A series of novel 1,4-dihydro-2,6- dimethyl-3,5-pyridinedicarboxamides were synthesized and characterized by infrared absorption spectrum (IR), proton nuclear magnetic resonance (1H NMR), elemental analysis, ultraviolet spectrum (UV), and fluorescence techniques, together with X-ray single crystal diffraction. The results of density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations provided a reasonable explanation on the molecular structures, the molecular frontier orbital, and the spectra of electronic absorption and emission. The present work will be helpful to systematically understanding of the structures and the optical properties of 1,4-dihydropyridines for studying the structure-activity relationship and to develop new drugs and their analytical methods.
The rapid access to intrinsic physicochemical properties of molecules is highly desired for large scale chemical data mining explorations such as mass spectrum prediction in metabolomics, toxicity risk assessment and drug discovery. Large volumes of data are being produced by quantum chemistry calculations, which provide increasing accurate estimations of several properties, e.g. by Density Functional Theory (DFT), but are still too computationally expensive for those large scale uses. This work explores the possibility of using large amounts of data generated by DFT methods for thousands of molecular structures, extracting relevant molecular properties and applying machine learning (ML) algorithms to learn from the data. Once trained, these ML models can be applied to new structures to produce ultra-fast predictions. An approach is presented for homolytic bond dissociation energy (BDE).
Machine learning models were trained with a data set of >12,000 BDEs calculated by B3LYP/6-311++G(d,p)//DFTB. Descriptors were designed to encode atom types and connectivity in the 2D topological environment of the bonds. The best model, an Associative Neural Network (ASNN) based on 85 bond descriptors, was able to predict the BDE of 887 bonds in an independent test set (covering a range of 17.67–202.30 kcal/mol) with RMSD of 5.29 kcal/mol, mean absolute deviation of 3.35 kcal/mol, and R2 = 0.953. The predictions were compared with semi-empirical PM6 calculations, and were found to be superior for all types of bonds in the data set, except for O-H, N-H, and N-N bonds. The B3LYP/6-311++G(d,p)//DFTB calculations can approach the higher-level calculations B3LYP/6-311++G(3df,2p)//B3LYP/6-31G(d,p) with an RMSD of 3.04 kcal/mol, which is less than the RMSD of ASNN (against both DFT methods). An experimental web service for on-line prediction of BDEs is available at http://joao.airesdesousa.com/bde.
Knowledge could be automatically extracted by machine learning techniques from a data set of calculated BDEs, providing ultra-fast access to accurate estimations of DFT-calculated BDEs. This demonstrates how to extract value from large volumes of data currently being produced by quantum chemistry calculations at an increasing speed mostly without human intervention. In this way, high-level theoretical quantum calculations can be used in large-scale applications that otherwise would not afford the intrinsic computational cost.
BDE; Bond dissociation energy; Neural network; Random forest; Machine learning; Chemoinformatics; DFT; DFTB; Big data
Although the dispersal of animals is influenced by a variety of factors, few studies have used a condition-dependent approach to assess it. The mechanisms underlying dispersal are thus poorly known in many species, especially in large mammals. We used 10 microsatellite loci to examine population density effects on sex-specific dispersal behavior in the American black bear, Ursus americanus. We tested whether dispersal increases with population density in both sexes. Fine-scale genetic structure was investigated in each of four sampling areas using Mantel tests and spatial autocorrelation analyses. Our results revealed male-biased dispersal pattern in low-density areas. As population density increased, females appeared to exhibit philopatry at smaller scales. Fine-scale genetic structure for males at higher densities may indicate reduced dispersal distances and delayed dispersal by subadults.
Black bear; dispersal; inbreeding avoidance; philopatry; population density; Ursus americanus
Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures.
Molecular dynamics simulations provide insight into the dynamic behavior of molecules, e.g., into the adopted spatial arrangements of its atoms over time. Methods differ in the approximations they employ, resulting in a trade-off between accuracy and speed that ranges from highly accurate but expensive quantum mechanical calculations to fast but more inaccurate molecular mechanics force fields. Machine learning, a sub-discipline of artificial intelligence, provides algorithms that learn from data, that is, make predictions based on previously seen examples. By starting with a few expensive quantum mechanical calculations, training a machine learning algorithm on them, and then using the resulting model to carry out the molecular dynamics simulation, one can improve the accuracy/speed trade-off. We have developed and applied such a hybrid quantum mechanics/machine learning approach to Archazolid A, a natural product from the myxobacterium Archangium gephyra and a potent inhibitor of vacuolar-type ATPase. By dynamically refining our model over the course of the simulation, we achieve errors of less than 1 kcal/mol while saving over 40% of the quantum mechanical calculations. Our study demonstrates the feasibility of predictive machine learning models for the dynamics of structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of even larger biomolecular structures.
Goethite is a common and reactive mineral in the environment. The transport of contaminants and anaerobic respiration of microbes are significantly affected by adsorption and reduction reactions involving goethite. An understanding of the mineral-water interface of goethite is critical for determining the molecular-scale mechanisms of adsorption and reduction reactions. In this study, periodic density functional theory (DFT) calculations were performed on the mineral goethite and its (010) surface, using the Vienna Ab Initio Simulation Package (VASP).
Calculations of the bulk mineral structure accurately reproduced the observed crystal structure and vibrational frequencies, suggesting that this computational methodology was suitable for modeling the goethite-water interface. Energy-minimized structures of bare, hydrated (one H2O layer) and solvated (three H2O layers) (010) surfaces were calculated for 1 × 1 and 3 × 3 unit cell slabs. A good correlation between the calculated and observed vibrational frequencies was found for the 1 × 1 solvated surface. However, differences between the 1 × 1 and 3 × 3 slab calculations indicated that larger models may be necessary to simulate the relaxation of water at the interface. Comparison of two hydrated surfaces with molecularly and dissociatively adsorbed H2O showed a significantly lower potential energy for the former.
Surface Fe-O and (Fe)O-H bond lengths are reported that may be useful in surface complexation models (SCM) of the goethite (010) surface. These bond lengths were found to change significantly as a function of solvation (i.e., addition of two extra H2O layers above the surface), indicating that this parameter should be carefully considered in future SCM studies of metal oxide-water interfaces.
The major objective of this paper is to address a controversial binding
sequence between nucleic acid bases (NABs) and C60 by investigating
adsorptions of NABs and their cations on C60 fullerene with a variety
of density functional theories including two novel hybrid meta-GGA functionals,
M05-2x and M06-2x, as well as a dispersion-corrected density functional, PBE-D.
The M05-2x/6-311++G** provides the same binding
sequence as previously reported, guanine(G) > cytosine(C) > adenine (A)
> thymine (T); however, M06-2x switches the binding strengths of A and C, and
PBE-D eventually results in the following sequence, G>A>T>C, which is
the same as the widely accepted hierarchy for the stacking of NABs on other
carbon nanomaterials such as single-walled carbon nanotube and graphite. The
results indicate that the questionable relative binding strength is due to
insufficient electron correlation treatment with the M05-2x or even the M06-2x
method. The binding energy of G@C60 obtained with the
M06-2x/6-311++G(d,p) and the PBE-D/cc-pVDZ is −7.10 and
−8.07 kcal/mol, respectively, and the latter is only slightly weaker
than that predicted by the MP2/6-31G(d,p) (−8.10kca/mol). Thus, the
PDE-D performs better than the M06-2x for the observed NAB@C60
π-stacked complexes. To discuss whether C60 could prevent
NABs from radiation-induced damage, ionization potentials of NABs and
C60, and frontier molecular orbitals of the complexes
NABs@C60 and (NABs@C60)+ are also
extensively investigated. These results revealed that when an electron escapes
from the complexes, a hole was preferentially created in C60 for T
and C complexes, while for G and A the hole delocalizes over the entire complex,
rather than a localization on the C60 moiety. The interesting finding
might open a new strategy for protecting DNA from radiation-induced damage and
offer a new idea for designing C60-based antiradiation drugs.
radiation-induced damage; NAB; C60; dispersion-corrected DFT; binding sequence
In this work, we studied a copper complex-based dye, which is proposed for potential photovoltaic applications and is named Cu (I) biquinoline dye. Results of electron affinities and ionization potentials have been used for the correlation between different levels of calculation used in this study, which are based on The Density Functional Theory (DFT) and time-dependent (TD) DFT. Further, the maximum absorption wavelengths of our theoretical calculations were compared with the experimental data. It was found that the M06/LANL2DZ + DZVP level of calculation provides the best approximation. This level of calculation was used to find the optimized molecular structure and to predict the main molecular vibrations, the molecular orbitals energies, dipole moment, isotropic polarizability and the chemical reactivity parameters that arise from Conceptual DFT.
molecular structure; absorption spectra; polarizability; chemical reactivity; dipole moment; copper complex; dye-sensitized
The normal mode spectrum for the four-coordinated heme compound Fe(II) octaethylporphyrin, Fe(OEP), has been determined by refining force constants to the experimental Fe vibrational density of states measured with nuclear resonance vibrational spectroscopy (NRVS). Convergence of the calculated spectrum to the data was achieved by first imposing D4 symmetry on the model structure as well as the force constants, progressively including different internal coordinates of motion, then allowing the true Ci (or S2) point group symmetry of the Ci1 Fe(OEP) crystal structure. The NRVS-refined normal modes are in good agreement with Raman and IR spectra at high frequencies. Prior density functional theory predictions for a model porphyrin are similar to the core modes computed with the best-fit force field, but significant differences between D4 and Ci modes underline the sensitivity of porphyrin Fe normal modes to structural details. Some differences between the Ci best fit and the NRVS data can be attributed to intermolecular contacts not included in the normal mode analysis.
Based on the minimum shear criterion, a direct and simple method is proposed to calculate twinning elements from the experimentally determined twinning plane for Type I twins or the twinning direction for Type II twins. It is generic and applicable to any crystal structure.
The fundamental theory of crystal twinning has been long established, leading to a significant advance in understanding the nature of this physical phenomenon. However, there remains a substantial gap between the elaborate theory and the practical determination of twinning elements. This paper proposes a direct and simple method – valid for any crystal structure and based on the minimum shear criterion – to calculate various twinning elements from the experimentally determined twinning plane for Type I twins or the twinning direction for Type II twins. Without additional efforts, it is generally applicable to identify and predict possible twinning modes occurring in a variety of crystalline solids. Therefore, the present method is a promising tool to characterize twinning elements, especially for those materials with complex crystal structure.
twinning; minimum shear; interface structure; transmission electron microscopy; scanning electron microscopy/electron backscatter diffraction
The title compound, C13H10BrNO2, exists as an enol–imine form in the crystal and adopts an E configuration with respect to the C=N double bond. The molecule is close to planar, with a dihedral angle of 6.88 (14)° between the aromatic rings. Intramolecular O—H⋯N and O—H⋯O hydrogen bonds generate S(6) and S(5) ring motifs, respectively. The crystal structure is stabilized by intermolecular O—H⋯O hydrogen-bond interactions, forming R
2(10) and R
2(20) chains along . ab initio Hartree–Fock (HF), density-functional theory (DFT) and semi-empirical (AM1 and PM3) calculations and full-geometry optimizations were also performed. Although there are some discrepancies between the experimental and calculated parameters, caused presumably by the O—H⋯O hydrogen-bond interactions, there is an acceptable general agreement between them.
Circular Dichroism (CD) spectroscopy is a powerful method for investigating conformational changes in proteins and therefore has numerous applications in structural and molecular biology. Here a computational investigation of the CD spectrum of the Human Carbonic Anhydrase II (HCAII), with main focus on the near-UV CD spectra of the wild-type enzyme and it seven tryptophan mutant forms, is presented and compared to experimental studies. Multilevel computational methods (Molecular Dynamics, Semiempirical Quantum Mechanics, Time-Dependent Density Functional Theory) were applied in order to gain insight into the mechanisms of interaction between the aromatic chromophores within the protein environment and understand how the conformational flexibility of the protein influences these mechanisms. The analysis suggests that combining CD semi empirical calculations, crystal structures and molecular dynamics (MD) could help in achieving a better agreement between the computed and experimental protein spectra and provide some unique insight into the dynamic nature of the mechanisms of chromophore interactions.
Dispersion is well known to be important in biological systems, but the effect of electron correlation in such systems remains unclear. In order to assess the relationship between the structure of a protein and its electron correlation energy, we employed both full system Hartree-Fock (HF) and second-order Møller-Plesset perturbation (MP2) calculations in conjunction with the Polarizable Continuum Model (PCM) on the native structures of two proteins and their corresponding computer-generated decoy sets. Due to the expense of the MP2 calculation, we have utilized the fragment molecular orbital method (FMO) in this study. We show that the sum of the Hartree-Fock (HF) energy and force field (LJ6) derived dispersion energy (HF + LJ6) is well correlated with the energies obtained using second-order Møller-Plesset perturbation (MP2) theory. In one of the two examples studied the correlation energy as well as the empirical dispersive energy term was able to discriminate between native and decoy structures. On the other hand, for the second protein we studied, neither the correlation energy nor dispersion energy showed discrimination capabilities; however, the ab initio MP2 energy and the HF+LJ6 both ranked the native structure correctly. Furthermore, when we randomly scrambled the Lennard-Jones parameters, the correlation between the MP2 energy and the sum of the HF energy and dispersive energy (HF+LJ6) significantly drops, which indicates that the choice of Lennard-Jones parameters is important.
We present experimental results and theoretical simulations of the adsorption behavior of the metal–organic precursor Co2(CO)8 on SiO2 surfaces after application of two different pretreatment steps, namely by air plasma cleaning or a focused electron beam pre-irradiation. We observe a spontaneous dissociation of the precursor molecules as well as autodeposition of cobalt on the pretreated SiO2 surfaces. We also find that the differences in metal content and relative stability of these deposits depend on the pretreatment conditions of the substrate. Transport measurements of these deposits are also presented. We are led to assume that the degree of passivation of the SiO2 surface by hydroxyl groups is an important controlling factor in the dissociation process. Our calculations of various slab settings, using dispersion-corrected density functional theory, support this assumption. We observe physisorption of the precursor molecule on a fully hydroxylated SiO2 surface (untreated surface) and chemisorption on a partially hydroxylated SiO2 surface (pretreated surface) with a spontaneous dissociation of the precursor molecule. In view of these calculations, we discuss the origin of this dissociation and the subsequent autocatalysis.
Co2(CO)8; deposition; dissociation; EBID; FEBID; precursor; radiation-induced nanostructures
In this work we studied three dyes which are proposed for potential photovoltaic applications and named Dye7, Dye7-2t and Dye7-3t. The Density Functional Theory (DFT) was utilized, using the M05-2X hybrid meta-GGA functional and the 6–31+G(d,p) basis set. This level of calculation was used to find the optimized molecular structure and to predict the main molecular vibrations, the absorption and emission spectra, the molecular orbitals energies, dipole moment, isotropic polarizability and the chemical reactivity parameters that arise from Conceptual DFT. Also, the pKa values were calculated with the semi-empirical PM6 method.
molecular structure; absorption spectrum; polarizability; chemical reactivity; dipole moment; triphenylamine; dye sensitizers
We report self-assembly and phase transition behavior of lower diamondoid molecules and their primary derivatives using molecular dynamics (MD) simulation and density functional theory (DFT) calculations. Two lower diamondoids (adamantane and diamantane), three adamantane derivatives (amantadine, memantine and rimantadine) and two artificial molecules (ADM•Na and DIM•Na) are studied separately in 125-molecule simulation systems. We performed DFT calculations to optimize their molecular geometries and obtained atomic electronic charges for the corresponding MD simulation, by which we predicted self-assembly structures and simulation trajectories for the seven different diamondoids and derivatives. Our radial distribution function and structure factor studies showed clear phase transitions and self-assemblies for the seven diamondoids and derivatives.
adamantane; amantadine; density functional theory; diamantane; diamondoids; MD simulation; memantine; nanotechnology; RDF, rimantadine; self-assembly; simulation annealing; structure factor
The title compound, C13H10Cl2O2, is a mixed derivative of orthocarbonic acid. The non-crystallographic symmetry of the molecule is close to C
2v. The aromatic residues are oriented in a syn conformation with respect to the Cl atoms. The least-squares planes through the phenyl rings enclose an angle of 36.11 (10)°. The C—O bonds at the central carbon are relatively short, and the O—C—O and Cl—C—Cl angles are smaller than the tetrahedral angle. These metrical peculiarities including a molecular symmetry close to C
2v are also observed in density functional theory (DFT) calculations, thus ruling out the decisive influence of intermolecular forces in the crystal structure. Accordingly, only few and weak intermolecular interactions are found. At distances smaller than the sum of the van der Waals radii, only two attractive interactions are detected: a weak C—H⋯O and a weak C—H⋯Cl hydrogen bond to one of the two potential acceptor atoms each.
A physical theory explaining the anisotropic dispersion of water and solutes in biological tissues is introduced based on the phenomena of Taylor dispersion, in which highly diffusive solutes cycle between flowing and stagnant regions in the tissue, enhancing dispersion in the direction of microvascular flow. An effective diffusion equation is derived, for which the coefficient of dispersion in the axial direction (direction of capillary orientation) depends on the molecular diffusion coefficient, tissue perfusion, and vessel density. This analysis provides a homogenization that represents three-dimensional transport in capillary beds as an effectively one-dimensional phenomenon. The derived dispersion equation may be used to simulate the transport of solutes in tissues, such as in pharmacokinetic modeling. In addition, the analysis provides a physically based hypothesis for explaining dispersion anisotropy observed in diffusion-weighted imaging (DWI) and diffusion-tensor magnetic resonance imaging (DTMRI) and suggests a means of obtaining quantitative functional information on capillary vessel density from measurements of dispersion coefficients. It is shown that a failure to account for flow-mediated dispersion in vascular tissues may lead to misinterpretations of imaging data and significant overestimates of directional bias in molecular diffusivity in biological tissues. Measurement of the ratio of axial to transverse diffusivity may be combined with an independent measurement of perfusion to provide an estimate of capillary vessel density in the tissue.
Potassium ion channels form pores
in cell membranes, allowing potassium
ions through while preventing the passage of sodium ions. Despite
numerous high-resolution structures, it is not yet possible to relate
their structure to their single molecule function other than at a
qualitative level. Over the past decade, there has been a concerted
effort using molecular dynamics to capture the thermodynamics and
kinetics of conduction by calculating potentials of mean force (PMF).
These can be used, in conjunction with the electro-diffusion theory,
to predict the conductance of a specific ion channel. Here, we calculate
seven independent PMFs, thereby studying the differences between two
potassium ion channels, the effect of the CHARMM CMAP forcefield correction,
and the sensitivity and reproducibility of the method. Thermodynamically
stable ion–water configurations of the selectivity filter can
be identified from all the free energy landscapes, but the heights
of the kinetic barriers for potassium ions to move through the selectivity
filter are, in nearly all cases, too high to predict conductances
in line with experiment. This implies it is not currently feasible
to predict the conductance of potassium ion channels, but other simpler
channels may be more tractable.