An integrated design environment (IDE) has been developed that allows the capture of design ideas, virtual compounds, and design hypotheses for medicinal chemistry projects. Specific consideration for rational molecular design, including design strategy and tactics, as well as comparator reference compounds have been incorporated to more easily convey the proposed design idea. A hierarchical tree architecture and customizable layouts allow for facile browsing across multiple programs and rapid examination of both ongoing and newly designed virtual compounds enabling centralized team discussions to ensure the most efficient prosecution of a queue of these target compounds. Additionally, a "whiteboard" module was incorporated for the rapid evaluation of virtual compounds against a suite of computational models enabling real-time design and triage. Finally, aggregation of cross-project design data enables broader analyses that can indicate portfolio-wide design challenges.

The chemical stability and the low-lying singlet and triplet excited states of BN-n-acenes (n = 1-7) were studied using single reference and multireference methodologies. From the calculations, descriptors such as the singlet-triplet splitting, the natural orbital (NO) occupations and aromaticity indexes are used to provide structural and energetic analysis. The boron and nitrogen atoms form an isoelectronic pair of two carbon atoms, which was used for the complete substitution of these units in the acene series. The structural analysis confirms the effects originated from the insertion of a uniform pattern of electronegativity difference within the molecular systems. The covalent bonds tend to be strongly polarized which does not happen in the case of a carbon-only framework. This effect leads to a charge transfer between neighbor atoms resulting in a more strengthened structure, keeping the aromaticity roughly constant along the chain. The singlet-triplet splitting also agrees with this stability trend, maintaining a consistent gap value for all molecules. The BN-n-acenes molecules possess a ground state with monoconfigurational character indicating their electronic stability. The low-lying singlet excited states have charge transfer character, which proceeds from nitrogen to boron.

The moving components of combustion engines are operated under harsh conditions of high pressures and temperatures. Extreme-pressure anti-wear additives, such as tricresyl phosphate (TCP), are mixed with base oil to prevent wear through the formation of a lubricant film on the substrate. We studied the effect of liquid pressure on the decomposition pathway of TCP in base oil molecules (2,5-dimethylhexane) using hybrid quantum-classical simulations with density functional theory for electrons. At a temperature of 300 K, we found that: (i) bond-breaking barrier energies of both the OC and PO bonds of TCP decrease monotonically as the liquid pressure increases; (ii) the bond-breaking barrier energy of PO is lower than that of OC at pressures of 0 and 2.0 GPa, but is higher at a pressure of 5.0 GPa; and (iii) the applied pressure significantly lowers the bond-breaking barrier energies of both OC and PO when the PO bond of TCP is directed upward from the substrate. These findings are explained by the inhomogeneous distribution of base oil molecules around TCP and the steric repulsion of the PO bond of TCP. These results indicate that the internal structures of the lubricant films are pressure-dependent.

We present a Gaussian process regression (GPR) scheme with an adaptive regularization scheme applied to the QM7 and QM9 test set, several protonated water clusters and specifically to the problem of atomic hydrogen adsorption on graphene sheets. For the last system our goal is to achieve good predictive accuracy with only a few training points. Therefore, we assess for these systems a self-correcting multilayer GPR model, in which the prediction is corrected by a chain of additional GPR models. In our adaptive regularization scheme, we impose no noise on the training data, but use an approach based on the data itself to account for its impurity. The strength of this strategy is that the data points are treated differently based on their importance and that the regularization can still be controlled by a single parameter. We assess how the accuracy of the prediction depends on this parameter. We can show that the new regularization scheme as well as the multilayer approach results in more robust predictors. Furthermore, we demonstrate that the predictor can be in good agreement with the density-functional theory results.

Electronic excited states in the series of polyene molecules were explored. Optimal ground-state geometry was used for the evaluation of vertical excitation energies. Results of a chosen set of functionals were compared to post-HF methods (EOM-CCSD, NEVPT2, CASPT2, and MRCI). In addition, the semiempirical OM2/MNDO method using MRCISD computational level was confronted with the above-mentioned techniques. Despite the fact that the first excited state has a significant double-excitation character some functionals were able to qualitatively determine the correct state order (where the lowest excited state has a Ag- character). The most successful functionals in transition energies predictions were PBE, TPSS and BLYP in Tamm-Dancoff approach (TDA), which had the smallest root-mean-square deviation (RMSD) scoring towards the experimental values. Regarding RMSD scoring, the OM2/MNDO method performed fairly well, too. Besides absorption spectra, lifetimes of the first two excited states were estimated based on a stochastic approach exploring a swarm of OM2/MNDO hopping dynamics using the Tully fewest switch algorithm for each molecule. The longest lifetime of the first excited state (S1 ) was found for decapentaene (about 5 ps). Further elongation of the conjugated chain caused a mild decrease of this value to ca 1.5 ps for docosaundecaene.

Since atomic or functional-group properties in the bulk are generally not available from experimental methods, computational approaches based on partitioning schemes have emerged as a rapid yet accurate pathway to estimate the materials behavior from chemically meaningful building blocks. Among several applications, a comprehensive and systematically built database of atomic or group polarizabilities and related opto-electronic quantities would be very useful not only to envisage linear or non-linear optical properties of biomacromolecules but also to improve the accuracy of classical force fields devoted to simulate biochemical processes. In this work, we propose the first entries of such database that contains distributed polarizabilities and dipole moments extracted from fragments of peptides. Twenty three prototypical conformers of the dipeptides alanine-alanine and glycine-glycine were used to extract functional groups such as CH2 , CHCH3 , NH2 , COOH, CONH, thus allowing construction of a diversity of chemically relevant environments. To evaluate the accuracy of our database, reconstructed properties of larger peptides containing up to six residues of alanine and glycine were tested against density functional theory calculations at the M06-HF/aug-cc-pVDZ level of theory. The procedure is particularly accurate for the diagonal components of the polarizability tensor with errors up to 15%. In order to include solvent effects explicitly, the peptides were also surrounded by a box of water molecules whose distribution was optimized using the CHARMM force field. Solvent effects introduced by a classical dipole-dipole interaction model were compared to those obtained from polarizable-continuum model calculations.

A class of adamantane-like molecular materials attracts attention because they exhibit an extreme non-linear optical response and emit a broad white-light spectrum after illumination with a continuous-wave infrared laser source. According to recent studies, not only the nature of the cluster molecules, but also the macroscopic structure of the materials determines their non-linear optical properties. Here we present a systematic study of cluster dimers of the compounds AdR4 and [(RT)4 S6 ] (T = Si, Ge, Sn) with R = methyl, phenyl or 1-naphthyl to gain fundamental knowledge about the interactions in the materials. For all compounds, a similar type of dimer structures with a staggered arrangement of substituents was determined as the energetically most favorable configuration. The binding energy between the dimers, determined by including London dispersion interactions, increases with the size of the core and the substituents. The cluster interactions can be classified as substituent-substituent-dominated (small cores, large substituents) or core-core-dominated (large cores, small substituents). Among various possible dimer conformers, those with small core-core distances are energetically preferred. Trimer and tetramer clusters display similar trends regarding the minimal core-core distances and binding energies. The much lower energy barrier determined for the rotation of substituents as compared to the rotation of the cluster dimers past each other indicates that the rotation of substituents more easily leads to different conformers in the material. Thus, understanding the interaction of the cluster dimers allows an initial assessment of the interactions in the materials.

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti6 O12 cluster, doping Al in Si11 (4Al@Si11 ) and Na10 supported on graphene (Na10 @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo8 C4 cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.

Activation of the dinitrogen triple bond is a crucial step in the overall fixation of atmospheric nitrogen into usable forms for industrial and biological applications. Current synthetic catalysts incorporate metal ions to facilitate the activation and cleavage of dinitrogen. The high price of metal-based catalysts and the challenge of catalyst recovery during industrial catalytic processes has led to increasing interest in metal-free catalysts. One step toward metal-free catalysis is the use of frustrated Lewis pairs (FLPs). In this study, we have examined 18 functionalized carbenes as FLPs to elucidate the influence of steric and electronic effects on the activation of dinitrogen. To test the effects of functionalization on dinitrogen activation, we have performed density functional theory (DFT), multireference, non and extended transition state-natural orbital for chemical valence (ETS-NOCV) calculations. Our results suggest that functional groups which introduce strong electron-withdrawing effects and/or engage in extended π/π* systems lead to the lowering of the dissociation energy of the dinitrogen bond, which further contributes to greater nitrogen activation. We conjecture that these effects are due to enhanced back-bonding capability of the p orbital of the carbene carbon atoms to the adjacent nitrogen atoms (increasing Lewis basicity of the carbene carbon atom) and enhanced stability of dissociated products. Our concluding remarks include opportunities to extend this activation study to explore the entire catalytic cycle with promising functionalized carbenes for experimental evaluation.

Considering the importance of elucidating the heat transfer in living cells, we evaluated the thermal conductivity κ and conductance G of hydrated protein through all-atom non-equilibrium molecular dynamics simulation. Extending the computational scheme developed in earlier studies for spherical protein to cylindrical one under the periodic boundary condition, we enabled the theoretical analysis of anisotropic thermal conduction and also discussed the effects of protein size correction on the calculated results. While the present results for myoglobin and green fluorescent protein (GFP) by the spherical model were in fair agreement with previous computational and experimental results, we found that the evaluations for κ and G by the cylindrical model, in particular, those for the longitudinal direction of GFP, were enhanced substantially, but still keeping a consistency with experimental data. We also studied the influence by salt addition of physiological concentration, finding insignificant alteration of thermal conduction of protein in the present case.

The fragment molecular orbital (FMO) method is a fast quantum-mechanical method that divides systems into pieces of fragments and performs ab initio calculations. The system truncation enables further speed improvement. In this article, we systematically study the effects of system truncations on binding affinity calculations obtained with FMO in combination with either the polarizable continuum model (FMO/PCM) or in combination with the Møller-Plesset method (FMO-MP2). We have used five protein complexes with ligands of several charged states. The calculated binding energies of the size variants of the truncated system, including only a restricted number of atoms around the ligand, are compared to the energy obtained from a full system. The result shows that the systems could be truncated to a radius of 8 Å from neutral ligands within an error of 0.7 kcal/mol, and 12 Å from charged ligands within an error of 1.1 kcal/mol for calculating the binding energy in solution.

Molecular electrostatic potential (MEP) is a significant and crucial physical quantity that can be applied to a large number of scenarios, such as the prediction of nucleophilic or electrophilic attacks, fitting atomic charges, σ-hole, and so forth. The computational cost for the MEP has an O(N2 ) scaling with the increase of atoms, which is intractable and laborious for macromolecules. Herein, a density fitting molecular electrostatic potential (DF-MEP) is used to reduce the computational costs for the macromolecular MEP. It is found that the accuracy of DF-MEP is almost identical to the conventional molecular electrostatic potential (Conv-MEP), while the computational costs can be reduced to an O(N) scaling, for example, the computational time of 699,200 grids for the Trp-cage molecule (304 atoms) only takes 16.6 s at the B3LYP-D3(BJ)/def2-SVP level of theory with 16 CPU cores compared with 3060.2 s for the Conv-MEP method.