This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

We propose a more rigorous definition for the recently introduced concept of pK(50). The value of pK(50) should be associated not with a "functional group", as originally postulated, but instead with an atom. The proposed clarification is meant to improve the interpretation and labeling of pK(50).

Nucleophilic aromatic substitution (SNAr) reactions are exploited to prepare poly(arylene sulfide)s (PAS's) via the reaction of bis-thiolates and dibrominated pyromellitic diimide (PMDI) derivatives. Small-molecule model studies reveal the reaction is well-defined and proceeds in quantitative yield in practical times at room temperature. Variation in comonomer feed ratios allowed some control over target polymer molecular weights in the step polymerization, but control was likely limited by the relatively poor polymer solubility in the dipolar aprotic solvents typically employed to promote SNAr reactions. One substitution pattern produces a steric "pocket" around the PMDI units, inducing a peculiar solubility trend in halogenated solvents; that is, greatly reduced solubility in CHCl3 relative to CH2Cl2 and C2H2Cl4. One example small-molecule readily dissolves in CHCl3 at room temperature, then rapidly grows poorly soluble crystals revealed by single-crystal XRD to contain CHCl3 molecules in the steric pockets. Finally, the recently demonstrated depolymerization of phthalonitrile-based PAS's via ipso substitution with monothiolates as chain scission agents yields quantitative molecular weight reduction to monomeric species from the polymers reported here.

The development of oxidant-free gold-catalyzed cross coupling reactions involving aryl halides have been hamstrung by the lack of gold catalysts capable of performing oxidative addition at Au(I) centers. Herein, we report the development of novel tricoordinate Au(I) catalysts supported by N,N-bidentate ligands and ligated by phosphine or arsine ligands for C-H functionalization without external oxidants to form biaryls with no homocoupling. The unsymmetrical character of the Au(I) catalyst is critical to facilitating this necessary orthogonal transformation. This study unveils yet another potential of Au(I) catalysis in biaryl synthesis.

Solutions of organic molecules containing one or more heterocycles with conjugated bonds may exist as a mixture of tautomers, but typically only a few of them are significantly populated even though the potential number grows combinatorially with the number of protonation and deprotonation sites. Generating the most stable tautomers from a given input structure is an important and challenging task, and numerous algorithms to tackle it have been proposed in the literature. This work describes a novel approach for tautomer prediction that involves the combined use of molecular mechanics, semiempirical quantum chemistry, and density functional theory. The key idea in our method is to identify the protonation and deprotonation sites using estimated micro-p Ka's for every atom in the molecule as well as in its nearest protonated and deprotonated forms. To generate tautomers in a systematic way with minimal bias, we then consider the full set of tautomers that arise from the combinatorial distribution of all such mobile protons among all protonatable sites, with efficient postprocessing to screen away high-energy species. To estimate the micro-p Ka's, we present a new method designed for the current task, but we emphasize that any alternative method can be used in conjunction with our basic algorithm. Our approach is therefore grounded in the computational prediction of physical properties in aqueous solution, in contrast to other approaches that may rely on the use of hard-coded rules of proton distribution, previously observed tautomerization patterns from a known chemical space, or human input. We present examples of the application of our algorithm to organic and drug-like molecules, with a focus on novel structures where traditional methods are expected to perform worse.

A true challenge in designing multiresponsive complex macro-molecular architectures is to tune their conformation at will by changing the gradients of external stimuli. However, the lack of a clear molecular-level understanding, establishing a delicate interplay between segment-based interaction details and large-scale macromolecular properties, has hindered the implementation of design principles for a long time. Combining molecular simulations, together with complementary polymer synthesis and characterization, we propose a molecular-level design principle of multiresponsive copolymer architectures. For this purpose, we use the co-nonsolvency concept that is associated with polymer collapse in miscible good solvents. We show how the responsiveness of different polymer blocks can provide fully flexible conformational tuning and, therefore, may serve as a guiding principle for smart material design.

Regioselective peripheral hydrogenation of a nanographene molecule with 60 contiguous sp2 carbons provides unprecedented access to peralkylated circumbiphenyl (1). Conversion to the circumbiphenyl core structure was unambiguously validated by MALDI-TOF mass spectrometry, NMR, FT-IR, and Raman spectroscopy. UV-vis absorption spectra and DFT calculations demonstrated the significant change of the optoelectronic properties upon peripheral hydrogenation. Stimulated emission from 1, observed via ultrafast transient absorption measurements, indicates potential as an optical gain material.

Numerous types of quantum chemical calculations and protocols have been successfully applied to computing of small, uncomplicated organic molecules. Here, we argue for the need to shift attention to more challenging molecules that are marked by an interplay of complicating factors such as conformational, tautomeric, steric, and other effects. The challenge is not in choosing the right quantum chemical method and solvation model but in combining the existing methods to simultaneously and accurately describe the breadth of chemical and physical phenomena that give rise to the experimentally observed . The complexity of the phenomena that must be considered begs for the need for a greater automation of prediction workflows. We review our experience with these challenges and outline paths for future progress in the direction of tackling prediction of complex organic molecules.

As a continuation of our work on developing a density functional theory-based pKa predictor, we present conceptual improvements to our previously published shell model, which is a hierarchical organization of pKa training sets and which, in principle, covers all chemical space. The improvements concern the way the studied chemical compound is associated with the data points from the training sets. By introducing a new descriptor of the local atomic environment which foregoes dependence on chemical bonding and connectivity, we are able to automatically locate molecules from the training set that are most relevant to the proton dissociation equilibrium under study. This new scheme leads to the prediction of a single pKa value weighted across multiple training sets and thus patches a defect disclosed in the formulation of our previous model. Using the new parametrization approach, the pKa prediction gets rid of outliers reported in previous applications of our approach, eliminates ambiguity in interpreting the results, and improves the overall accuracy. Our new treatment accounts for multiple conformations both on the level of energetics and parametrization. Illustrative results are shown for several types of chemical structures containing guanidine, amidine, amine, and phenol functional groups, and which are representative of practically important large and flexible drug-like molecules. Our method's performance is compared to the performance of other previously published pKa prediction methods. Further possible improvements to the organization of the training sets and the potential application of our new local atomic descriptor to other kinds of parametrizations are discussed.

Transition state search is at the center of multiple types of computational chemical predictions related to mechanistic investigations, reactivity and regioselectivity predictions, and catalyst design. The process of finding transition states in practice is, however, a laborious multistep operation that requires significant user involvement. Here, we report a highly automated workflow designed to locate transition states for a given elementary reaction with minimal setup overhead. The only essential inputs required from the user are the structures of the separated reactants and products. The seamless workflow combining computational technologies from the fields of cheminformatics, molecular mechanics, and quantum chemistry automatically finds the most probable correspondence between the atoms in the reactants and the products, generates a transition state guess, launches a transition state search through a combined approach involving the relaxing string method and the quadratic synchronous transit, and finally validates the transition state via the analysis of the reactive chemical bonds and imaginary vibrational frequencies as well as by the intrinsic reaction coordinate method. Our approach does not target any specific reaction type, nor does it depend on training data; instead, it is meant to be of general applicability for a wide variety of reaction types. The workflow is highly flexible, permitting modifications such as a choice of accuracy, level of theory, basis set, or solvation treatment. Successfully located transition states can be used for setting up transition state guesses in related reactions, saving computational time and increasing the probability of success. The utility and performance of the method are demonstrated in applications to transition state searches in reactions typical for organic chemistry, medicinal chemistry, and homogeneous catalysis research. In particular, applications of our code to Michael additions, hydrogen abstractions, Diels-Alder cycloadditions, carbene insertions, and an enzyme reaction model involving a molybdenum complex are shown and discussed.

We have recently proposed preferential binding by a cosolvent as the mechanism for chain collapse under co-non-solvency. Here we summarise our earlier works and provide further evidence that alcohol preferentially binds to PNIPAm, forming cosolvent bridges, and thus drives the transition. We also clarify some of the common misconceptions evoked in this debate with Pica and Graziano (PG), reinforcing the arguments of our earlier reply-comment [Soft Matter, 2017, 13, 2292] and published works.

We consider the conformational flexibility of molecules and its implications for micro- and macro-pK(a). The corresponding formulas are derived and discussed against the background of a comprehensive scientific and algorithmic description of the latest version of our computer program Jaguar pK(a), a density functional theory-based pK(a) predictor, which is now capable of acting on multiple conformations explicitly. Jaguar pK(a) is essentially a complex computational workflow incorporating research and technologies from the fields of cheminformatics, molecular mechanics, quantum mechanics, and implicit solvation models. The workflow also makes use of automatically applied empirical corrections which account for the systematic errors resulting from the neglect of explicit solvent interactions in the algorithm's implicit solvent model. Applications of our program to large, flexible organic molecules representing several classes of functional groups are shown, with a particular emphasis in illustrations laid on drug-like molecules. It is demonstrated that a combination of aggressive conformational: search and an explicit consideration of multiple conformations nearly eliminates the dependence of results on the initially chosen conformation. In certain cases this leads to unprecedented accuracy, which is sufficient for distinguishing stereoisomers that have slightly different pK(a) values. An application of Jaguar pK(a) to proton sponges, the pK(a) of which are strongly influenced by steric effects, showcases the advantages that pK(a) predictors based on quantum mechanical calculations have over similar empirical programs.

Second order Møller-Plesset perturbation (MP2) theory is a widely used and one of the computationally least expensive post-self-consistent field (SCF) correlated treatments for electronic structure calculations. This chapter reviews some methods used to reduce the computational expense of MP2 calculations for larger systems and then highlights efforts to graphics processing unit (GPU)-accelerate one such method termed resolution-of-the-identity MP2. It addresses the theoretical background of the approach followed by the specifics of how RI-MP2 was adapted to GPUs. The chapter focuses on matrix algebra optimizations, in particular the use of mixed precision in matrix multiplications. It discusses the computational performance of the approach with respect to mathematical precision, hardware, and molecule size. The chapter reviews large biomolecules using RI-MP2 as well as an initial study of metabolic reactions. As current graphics cards support double-precision (DP) arithmetic, mixed-precision approaches are used to obtain acceleration in a variety of quantum chemistry algorithms.

Empirical observation and quantum chemical explanation of a likely common but “overlooked” outcome of the donor–acceptor (D–A) approach to organic electronic materials are reported. The frontier molecular orbitals (FMOs) of D–A copolymers prepared from the amidated Pechmann dye unit (APD) are dictated primarily by this “acceptor,” largely because its HOMO–LUMO gap falls within the gaps of common “donor” units. D–A copolymers with common benzodithiophene and cyclopentadithiophene units as “donors” are presented, giving examples having similar optical properties regardless of backbone planarity and solid‐state packing motif, along with calculations extending to several other common donor monomers to indicate generality. According to quantum‐chemical studies, APD‐localized FMOs form the transition to the lowest excited state, and higher lying charge‐transfer (CT) states. Other existing examples of this FMO situation are likely, but with (opto)electronic properties attributed to lack of conjugation due to, e.g., polymer backbone twisting. image

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Dalton is a powerful general-purpose program system for the study of molecular electronic structure at the Hartree-Fock, Kohn-Sham, multiconfigurational self-consistent-field, Møller-Plesset, configuration-interaction, and coupled-cluster levels of theory. Apart from the total energy, a wide variety of molecular properties may be calculated using these electronic-structure models. Molecular gradients and Hessians are available for geometry optimizations, molecular dynamics, and vibrational studies, whereas magnetic resonance and optical activity can be studied in a gauge-origin-invariant manner. Frequency-dependent molecular properties can be calculated using linear, quadratic, and cubic response theory. A large number of singlet and triplet perturbation operators are available for the study of one-, two-, and three-photon processes. Environmental effects may be included using various dielectric-medium and quantum-mechanics/molecular-mechanics models. Large molecules may be studied using linear-scaling and massively parallel algorithms. Dalton is distributed at no cost from http://www.daltonprogram.org for a number of UNIX platforms.