The Infrared (IR) and Raman spectra of various interstitial carbon defects in silicon are computed at the quantum mechanical level by using an all electron Gaussian type basis set, the hybrid B3LYP functional and the supercell approach, as implemented in the CRYSTAL code (Dovesi et al. J. Chem. Phys. 2020, 152, 204111). The list includes two 〈100〉 split interstitial IXY defects, namely ICC and ICSi , a couple of related defects that we indicate as IX IY , the so called CiCs0 in its A and B form, as well as SiCi Si and Cs Ci Cs , in which the interstitial carbon atom is twofold coordinated. The second undergoes a large relaxation, and the final configuration is close to ICC Cs . Geometries, relative stabilities, electronic, and vibrational properties are analysed. All these defects show characteristic features in their IR spectrum (above 730 cm- 1 ), whereas the Raman spectrum is dominated, in most of the cases, by the pristine silicon peak at 530 cm-1 , that hides the defect peaks.

Based on the quantum chemical topology of the modified electron localization function ELFx , an efficient and robust mechanistic methodology designed to identify the favorable reaction pathway between two reactants is proposed. We first recall and reshape how the supermolecular interaction energy can be evaluated from only three distinct terms, namely the intermolecular coulomb energy, the intermolecular exchange-correlation energy and the intramolecular energies of reactants. Thereafter, we show that the reactivity between the reactants is driven by the first-order variation in the coulomb intermolecular energy defined in terms of the response to changes in the number of electrons. Illustrative examples with the formation of the dative bond B-N involved in the BH3 NH3 molecule and the typical formation of the hydrogen bond in the canonical water dimer are presented. For these selected systems, our approach unveils a noticeable mimicking of Edual onto the DFT intermolecular interaction energy surface calculated between the both reactants. An automated reaction-path algorithm aimed to determine the most favorable relative orientations when the two molecules approach each other is also outlined.

We applied a set of advanced bonding descriptors to establish the hidden electron density features and binding energy characteristics of intermolecular DH∙∙∙A hydrogen bonds (OH∙∙∙O, NH∙∙∙O and SH∙∙∙O) in 150 isolated and solvated molecular complexes. The exchange-correlation and Pauli potentials as well as corresponding local one-electron forces allowed us to explicitly ascertain how electron exchange defines the bonding picture in the proximity of the H-bond critical point. The electron density features of DH∙∙∙A interaction are governed by alterations in the electron localization in the H-bond region displaying itself in the exchange hole. At that, they do not depend on the variations in the exchange hole mobility. The electrostatic interaction mainly defines the energy of H-bonds of different types, whereas the strengthening/weakening of H-bonds in complexes with varying substituents depends on the barrier height of the exchange potential near the bond critical point. Energy variations between H-bonds in isolated and solvated systems are also caused the electron exchange peculiarities as follows from the corresponding potential and the interacting quantum atom analyses complemented by electron delocalization index calculations. Our approach is based on the bonding descriptors associated with the characteristics of the observable electron density and can be recommended for in-depth studies of non-covalent bonding.

Computational modeling of organic interface formation on semiconductors poses a challenge to a density functional theory-based description due to structural and chemical complexity. A hierarchical approach is presented, where parts of the interface are successively removed in order to increase computational efficiency while maintaining the necessary accuracy. First, a benchmark is performed to probe the validity of this approach for three model reactions and five dispersion corrected density functionals. Reaction energies are generally well reproduced by generalized gradient approximation-type functionals but accurate reaction barriers require the use of hybrid functionals. Best performance is found for the model system that does not explicitly consider the substrate but includes its templating effects. Finally, this efficient model is used to provide coverage dependent reaction energies and suggest synthetic principles for the prevention of unwanted growth termination reactions for organic layers on semiconductor surfaces.

In the present work, the fast amide bond cleavage of [3-((1R,5S,7s)-3-azabicyclo[3.3.1]nonane-7-carbonyl)-3-azabicyclo[3.3.1]nonane-7-carboxylic acid (bi-ATDO)], through an intramolecular nucleophilic attack of an amine group is evaluated. First, six possible peptide bond cleavage mechanisms, two of them including a water molecule, are described at the ωB97XD/6-311 + G(d,p)//MP2/6-311 + G(d,p) level of theory. The reaction consisting of an intramolecular nitrogen nucleophilic attack followed by a proton transfer and the amide bond cleavage is determined as the most favorable mechanism. The activation free energy computed for the latter is 20.5 kcal mol-1 , which agrees with the reported experimental result of 24.8 kcal mol-1 . Inclusion of a water molecule to assist the first step of the reaction results in an activation free energy increase of about 17 kcal mol-1 . All the steps in the most favorable mechanism are studied more in detail employing intrinsic reaction coordinate as well as the reaction force and reaction electronic flux analysis.

Cellular membranes are composed of a wide diversity of lipid species in varying proportions and these compositions are representative of the organism, cellular type and organelle to which they belong. Because models of these molecular systems simulated by MD steadily gain in size and complexity, they are increasingly representative of specific compositions and behaviors of biological membranes. Due to the number of lipid species involved, of force fields and topologies and because of the complexity of membrane objects that have been simulated, LIMONADA has been developed as an open database allowing to handle the various aspects of lipid membrane simulation. LIMONADA presents published membrane patches with their simulation files and the cellular membrane it models. Their compositions are then detailed based on the lipid identification from LIPID MAPS database plus the lipid topologies and the force field used. LIMONADA is freely accessible on the web at https://limonada.univ-reims.fr/.

We investigate the performance of a set of recently introduced range-separated double-hybrid functionals, namely ωB2-PLYP, ωB2GP-PLYP, RSX-0DH, and RSX-QIDH models for hard-to-calculate excitation energies. We compare with the parent (B2-PLYP, B2GP-PLYP, PBE0-DH, and PBE-QIDH) and other (DSD-PBEP86) double-hybrid models as well as with some of the most widely employed hybrid functionals (B3LYP, PBE0, M06-2X, and ωB97X). For this purpose, we select a number of medium-sized intra- and inter-molecular charge-transfer excitations, which are known to be challenging to calculate using time-dependent density-functional theory (TD-DFT) and for which accurate reference values are available. We assess whether the high accuracy shown by the newest double-hybrid models is also confirmed for those cases too. We find that asymptotically corrected double-hybrid models yield a superior performance, especially for the inter-molecular charge-transfer excitation energies, as compared to standard double-hybrid models. Overall, the PBE-QIDH and its corresponding range-separated RSX-QIDH functional are recommended for general-purpose TD-DFT applications, depending on whether long-range effects are expected to play a significant role.

This work examines methods for predicting the partition coefficient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic attributes are transformed into index-invariant molecular features using a recently developed method called geometric scattering for graphs (GSG). We call this approach "ClassicalGSG" and examine its performance under a broad range of conditions and hyperparameters. We train ClassicalGSG log P predictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the log P values from four independent test sets. The ClassicalGSG method's performance is compared to a baseline model that employs graph convolutional networks. Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized force field and 2D molecular structures.

The full configuration interaction (FCI) method is only applicable to small molecules with few electrons in moderate size basis sets. One of the main alternatives to obtain approximate FCI energies for bigger molecules and larger basis sets is selected CI. However, due to: (a) the lack of a well-defined structure in a selected CI Hamiltonian, (b) the potentially large number of electrons together with c) potentially large orbital spaces, a computationally and memory efficient algorithm is difficult to construct. In the present series of papers, we describe our attempts to address these issues by exploring tree-based approaches. At the same time, we devote special attention to the issue of obtaining eigenfunctions of the total spin squared operator since this is of particular importance in tackling magnetic properties of complex open shell systems. Dedicated algorithms are designed to tackle the CI problem in terms of determinant, configuration (CFG) and configuration state function many-particle bases by effective use of the tree representation. In this paper we describe the underlying logic of our algorithm design and discuss the advantages and disadvantages of the different many particle bases. We demonstrate by the use of small examples how the use of the tree simplifies many key algorithms required for the design of an efficient selected CI program. Our selected CI algorithm, called the iterative configuration expansion, is presented in the penultimate part. Finally, we discuss the limitations and scaling characteristics of the present approach.

A heterocyclic compound mS-11 is a helix-mimetic designed to inhibit binding of an intrinsic disordered protein neural restrictive silence factor/repressor element 1 silencing factor (NRSF/REST) to a receptor protein mSin3B. We apply a generalized ensemble method, multi-dimensional virtual-system coupled molecular dynamics developed by ourselves recently, to a system consisting of mS-11 and mSin3B, and obtain a thermally equilibrated distribution, which is comprised of the bound and unbound states extensively. The lowest free-energy position of mS-11 coincides with the NRSF/REST position in the experimentally-determined NRSF/REST-mSin3B complex. Importantly, the molecular orientation of mS-11 is ordering in a wide region around mSin3B. The resultant binding scenario is: When mS-11 is distant from the binding site of mSin3B, mS-11 descends the free-energy slope toward the binding site maintaining the molecular orientation to be advantageous for binding. Then, finally a long and flexible hydrophobic sidechain of mS-11 fits into the binding site, which is the lowest-free-energy complex structure inhibiting NRSF/REST binding to mSin3B.

We present a comprehensive study of the most relevant numerical aspects influencing frequencies and intensities in the infrared spectrum of isolated polycyclic aromatic hydrocarbons (PAHs) regarding the overestimate of the IR CH-stretching bands. We use naphthalene as benchmark and show the validity of our results to different members of the PAH family. Our analysis relies on widely employed density functional theory methods and second-order vibrational perturbational theory for the computation of vibrational eigenstates. We have focused on the elucidation of the origin of the systematic overestimate of the intensities in the CH-stretching region. To rule out nonfundamental numerical errors, we have initially considered the influence of the electronic basis set and various other parameters on the different stages of the vibrational analysis. In a second stage, we have benchmarked the results of different density functional theory functionals with respect to the aforementioned overestimate taken as the ratio between the most prominent features of the spectrum, the CH-bending and the CH-stretching bands. Our results unambiguously indicate that the long-range correction plays a major role in this spurious numerical issue. More specifically, this phenomenon is due to an incorrect description of the charge distribution (and hence dipole) within the symmetrically relevant CH bonds. Long-range correction specifically remedies this issue. It improves the description of the intensities in the stretching region while at the same time it does not perturb significantly the rest of the spectrum. With respect to the frequencies, we have observed an overall improvement when compared to noncorrected functionals.

The assessment of the radiolytic stability of media is an important task in the fields of nuclear power engineering and radiochemistry. Such studies must be carried out in special laboratory conditions with the use of sources of ionizing radiation, which may increase personal doses of the staff. In addition, difficulties arise in studying the products of irradiated media. While it is impossible to abandon experiments to obtain reliable results in this area, computational methods of quantum chemistry can reduce the number of experiments and help understand the mechanisms of the reactions that occur during radiolysis. Here we would like to present a software shell of the Qb@ll program performing time-dependent density functional theory simulations of the radiolysis process.

In continuation of the previous reports on a combination of 3D-quantitative structure-activity relationships (QSAR) with computational molecular dynamics (MD) studies, a new variation of 3D-QSAR/MD method has been employed for drug-design as an alternative or supplementary for the typical experimental methods. The presented method is more cost-effective and less time-consuming than the previous methods and avoids several restrictions of experimental methods, such as validity estimation, and predictability. For this purpose, seven inhibitors for bromodomain (BRD)-containing protein, as an important protein in the development of different types of cancer and responsible for oncogenic rearrangements, have been selected to study of their interactions by docking and MD simulations using molecular mechanics/generalized born surface area (MM/GBSA) method. To build the proposed model, a common variant of 3D-QSAR methods, comparative molecular field analysis has been employed using a dataset of 100 MD-extracted ligand conformations and their corresponding MM/GBSA BRD4-binding energies. The results showed excellent predictability of the generated model for both the training set and test groups. Finally, two new inhibitors were selected among total 4000 designed derivatives (generated through evolutionary techniques) using the proposed 3D-QSAR-MD model. The potentials of these inhibitors were assessed by MD simulations, which showed the higher inhibitory of these compounds than the previous inhibitors. Therefore, this method showed high potentials for acceleration of the procedure of drug design and a basis for joining researchers in computational biology and pharmaceutical sciences.

The effects of functional groups (including OH, OCH3 , NH2 , CH2 NH2 , COOH, SO3 H, OCO(CH2 )2 COOH(E-COOH), and (CH2 )4 COOH(c-COOH)) in 3D covalent organic frameworks (3D-COFs) on CO2 adsorption and separation are investigated by grand canonical Monte Carlo (GCMC) simulations and density functional theory calculations. The results indicate that interaction between CO2 and the framework is the main factor for determining CO2 uptakes at low pressure, while pore size becomes the decisive factor at high pressure. The binding energy of CO2 with functionalized linker is correlated to CO2 uptake at 0.3 bar and 298 K on 3D-COF-1, suggesting functional groups play a key role in CO2 capture in microporous 3D-COFs. Moreover, CO2 selectivity over CH4 , N2 , and H2 can be significantly enhanced by functionalization, where CH2 NH2 , COOH, SO3 H, and E-COOH enhance CO2 adsorption more effectively at 1 bar. Among them, SO3 H is the most promising functional group in 3D-COFs for CO2 separation.

Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all-atom (e.g., CHARMM36) or coarse-grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.

SARS-CoV and SARS-CoV-2 belong to the subfamily Coronaviridae and infect humans, they are constituted by four structural proteins: Spike glycoprotein (S), membrane (M), envelope (E) and nucleocapsid (N), and nonstructural proteins, such as Nsp15 protein which is exclusively present on nidoviruses and is absent in other RNA viruses, making it an ideal target in the field of drug design. A virtual screening strategy to search for potential drugs was proposed, using molecular docking to explore a library of approved drugs available in the DrugBank database in order to identify possible NSP15 inhibitors to treat Covid19 disease. We found from the docking analysis that the antiviral drugs: Paritaprevir and Elbasvir, currently both approved for hepatitis C treatment which showed some of the lowest free binding energy values were considered as repositioning drugs to combat SARS-CoV-2. Furthermore, molecular dynamics simulations of the Apo and Holo-Nsp15 systems were performed in order to get insights about the stability of these protein-ligand complexes.

The noncovalent interactions involving heteronuclear ethylene analogues H2 CEH2 (E = Si, Ge and Sn) have been studied by the Møller-Plesset perturbation theory to investigate the competition and cooperativity between the hydrogen/halogen bond and π-hole bond. H2 CEH2 has a dual role of being a Lewis base and acid with the region of π-electron accumulation above the carbon atom and the region of π-electron depletion (π-hole) above the E atom to participate in the NCX···CE (X = H and Cl) hydrogen/halogen bond and CE···NCY (Y = H, Cl, Li and Na) π-hole bond, respectively. When HCN/ClCN interacts with H2 CEH2 by two sites, the strength of hydrogen bond/halogen bond is stronger than that of π-hole bond. The π-hole bond becomes obviously stronger when the metal substituent of YCN (Y = Li and Na) interacting with H2 CEH2 , showing the character of partial covalent, its strength is much greater than that of hydrogen/halogen bond. In the ternary complexes, both hydrogen/halogen bond and π-hole bond are simultaneously strengthened compared to those in the binary complexes, especially in the systems containing alkali metal.