Quantum Chemical Information Research Articles

QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information

Ions and radicals serve as key intermediates in molecular transformation, with their chemical properties being essential for understanding and predicting reaction reactivity and selectivity. In this data descriptor, we report a quantum chemical dataset named QM9star, comprising cations, anions, and radicals. This dataset is derived from the molecular structures of the QM9 dataset, created by removing terminal hydrogens followed by optimization using B3LYP-D3(BJ)/6-311 + G(d,p) level of density functional theory. The QM9star dataset includes approximately 1.9 million cations, anions, and radicals, along with 120 kilo neutral molecules prior to hydrogen removal. Each entry encompasses both molecular and atomic information: representative global properties include orbital energies, vibrational frequencies, etc., while local properties cover aspects such as charges and spin densities at each atomic site. The QM9star dataset not only serves as a comprehensive source of quantum chemical information for intermediates but also offers insights into the principle of atomic property distribution. We anticipate that these data will aid in machine learning studies related to chemical intermediates and contribute to the molecular representation learning.

CP-AFM Molecular Tunnel Junctions with Alkyl Backbones Anchored Using Alkynyl and Thiol Groups: Microscopically Different Despite Phenomenological Similarity.

In this paper, we report results on the electronic structure and transport properties of molecular junctions fabricated via conducting probe atomic force microscopy (CP-AFM) using self-assembled monolayers (SAMs) of n-alkyl chains anchored with acetylene groups (CnA; n = 8, 9, 10, and 12) on Ag, Au, and Pt electrodes. We found that the current-voltage (I-V) characteristics of CnA CP-AFM junctions can be very accurately reproduced by the same off-resonant single-level model (orSLM) successfully utilized previously for many other junctions. We demonstrate that important insight into the energy-level alignment can be gained from experimental data of transport (processed via the orSLM) and ultraviolet photoelectron spectroscopy combined with ab initio quantum chemical information based on the many-body outer valence Green's function method. Measured conductance GAg < GAu < GPt is found to follow the same ordering as the metal work function ΦAu < ΦAu < ΦPt, a fact that points toward a transport mediated by an occupied molecular orbital (MO). Still, careful data analysis surprisingly revealed that transport is not dominated by the ubiquitous HOMO but rather by the HOMO-1. This is an important difference from other molecular tunnel junctions with p-type HOMO-mediated conduction investigated in the past, including the alkyl thiols (CnT) to which we refer in view of some similarities. Furthermore, unlike in CnT and other junctions anchored with thiol groups investigated in the past, the AFM tip causes in CnA an additional MO shift, whose independence of size (n) rules out significant image charge effects. Along with the prevalence of the HOMO-1 over the HOMO, the impact of the "second" (tip) electrode on the energy level alignment is another important finding that makes the CnA and CnT junctions different. What ultimately makes CnA unique at the microscopic level is a salient difference never reported previously, namely, that CnA's alkyne functional group gives rise to two energetically close (HOMO and HOMO-1) orbitals. This distinguishes the present CnA from the CnT, whose HOMO stemming from its thiol group is well separated energetically from the other MOs.

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction

Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug discovery process and avoid depletion later in the drug development process. Nowadays, more and more researchers are starting to use machine learning methods to predict the toxicity of molecules, but these models do not fully exploit the 3D information of molecules. Quantum chemical information, which provides stereo structural information of molecules, can influence their toxicity. To this end, we propose QuantumTox, the first application of quantum chemistry in the field of drug molecule toxicity prediction compared to existing work. We extract the quantum chemical information of molecules as their 3D features. In the downstream prediction phase, we use Gradient Boosting Decision Tree and Bagging ensemble learning methods together to improve the accuracy and generalization of the model. A series of experiments on various tasks show that our model consistently outperforms the baseline model and that the model still performs well on small datasets of less than 300.

QMugs, quantum mechanical properties of drug-like molecules

Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecules alongside first-principle quantum chemical information. The open-access QMugs (Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void. The QMugs collection comprises quantum mechanical properties of more than 665 k biologically and pharmacologically relevant molecules extracted from the ChEMBL database, totaling ~2 M conformers. QMugs contains optimized molecular geometries and thermodynamic data obtained via the semi-empirical method GFN2-xTB. Atomic and molecular properties are provided on both the GFN2-xTB and on the density-functional levels of theory (DFT, ωB97X-D/def2-SVP). QMugs features molecules of significantly larger size than previously-reported collections and comprises their respective quantum mechanical wave functions, including DFT density and orbital matrices. This dataset is intended to facilitate the development of models that learn from molecular data on different levels of theory while also providing insight into the corresponding relationships between molecular structure and biological activity.

SPAHM: the spectrum of approximated Hamiltonian matrices representations.

Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the spectrum of approximated Hamiltonian matrices (SPAHM) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPAHM representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPAHM representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated “guess” Hamiltonians. These SPAHM representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database.

Connecting the quantum and classical mechanics simulation world: Applications of reactive step molecular dynamics simulations.

This article presents the application of the reactive step molecular dynamics simulation method [M. Biedermann, D. Diddens, and A. Heuer, J. Chem. Theory Comput. 17, 1074 (2021)] toward two different atomistic, chemically reactive systems. During reactive steps, transitions from reactant to product molecules are modeled according to physically correct transition probabilities based on quantum chemical information about the reactions such as molecular reaction rates via instant exchange of the employed force field and a subsequent, short relaxation of the structure. In the first application, we study the follow-up reactions of singly reduced ethylene carbonate (EC) radicals in EC solution, first, via extensive ab initio molecular dynamics simulations and, second, with the reactive step algorithm. A direct comparison of both simulation methods shows excellent agreement. Then, we employ the reactive step algorithm to simulate the enolate formation of 2-methylcyclopropanone with the base lithium diisopropylamine. Thereby, we can demonstrate that the reactive step algorithm is also capable of capturing effects from kinetic vs thermodynamic control of chemical reactions during simulation.

Co loaded on graphene with interfacial structure as high performance catalyst for 4e− ORR: a DFT study

On the basis of density functional theory (DFT), Co/C (carbon) catalyst was designed theoretically to improve the catalytic activity of the carbon-supported cobalt composite catalyst. The quantum chemical information was analyzed to explore the rules between structures, and then high-activity catalysts were selected according to molecular orbital energy. Theoretical calculation showed that the quaternary Co catalyst structure is the most stable structure with high catalyst activity. Then, the nitrogen (N) atoms were introduced to further improve the catalyst activity, and finally the best catalyst was selected as the quaternary atomically loaded N-doped Co/C catalyst. The oxygen reduction reaction (ORR) mechanism of selected Co/C catalyst was studied through theoretical calculation. The results showed that the overall process stability of the 4e− ORR pathway on C and N atoms is high, and the activation effect of the reactant O2 is optimal; the overall energy barrier span is significantly lower than that without the catalyst.

Vibronic coupling model to calculate the photoelectron spectrum of phenol

The photoelectron spectrum for the two lowest ionisation bands of phenol has been simulated using quantum dynamic methods. A vibronic coupling Hamiltonian was set up consisting of seven vibrational modes and the first two ionised states. Parameters for the model are obtained by fitting adiabatic surfaces to a series of points calculated using ab initio methods. Such a model allows non-adiabatic couplings between the states to be included. CASSCF calculations used in this work provide reliable quantum chemical information for the model and the calculated photoelectron spectrum shows good agreement to experiment. The vibrational fine structure of both bands are reassessed and different assignments to those previously reported are detailed. The existence of a conical intersection between the ionised states is reported and its role in the dynamics of phenol upon ionisation is examined.

A general intermolecular force field based on tight-binding quantum chemical calculations.

A black-box type procedure is presented for the generation of a molecule-specific, intermolecular potential energy function. The method uses quantum chemical (QC) information from our recently published extended tight-binding semi-empirical scheme (GFN-xTB) and can treat non-covalently bound complexes and aggregates with almost arbitrary chemical structure. The necessary QC information consists of the equilibrium structure, Mulliken atomic charges, charge centers of localized molecular orbitals, and also of frontier orbitals and orbital energies. The molecular pair potential includes model density dependent Pauli repulsion, penetration, as well as point charge electrostatics, the newly developed D4 dispersion energy model, Drude oscillators for polarization, and a charge-transfer term. Only one element-specific and about 20 global empirical parameters are needed to cover systems with nuclear charges up to radon (Z = 86). The method is tested for standard small molecule interaction energy benchmark sets where it provides accurate intermolecular energies and equilibrium distances. Examples for structures with a few hundred atoms including charged systems demonstrate the versatility of the approach. The method is implemented in a stand-alone computer code which enables rigid-body, global minimum energy searches for molecular aggregation or alignment.

Electrical and optical properties of a small capped (5, 0) zigzag Carbon nanotube by B, N, Ge and Sn atoms: DFT theoretical calculation

In this study we investigate the effect of atoms such as B, N, Ge and Sn on the optical and the electrical properties of capped (5, 0) zigzag carbon nanotube, using DFT calculation method. These elements were attached to the one end of the carbon nanotube. We considered four different structure designs as possible candidates for a p-n junction device. The electrical properties of these structures were investigated using the quantum chemical information analysis which leads to the energy band gap, dipole moments, electrical charges and the DOS of these structures. Further TD-DFT calculations were performed to obtain the optical properties of the structure designs to investigate the electron mobility, indicating higher conductivity and higher rectifying voltage in the CNT terminated by Sn.

Electron diffraction analysis for the molecules with degenerate large amplitude motions: Intramolecular dynamics in arsenic pentafluoride

There exists a noticeable disagreement in the difference of axial and equatorial bond lengths in D3h symmetry arsenic and phosphorus pentafluorides between the GED data and high level quantum chemical results. In order to resolve this disagreement, a new structural analysis of the original experiment of (Clippard & Bartell, Inorg. Chem., 9 (1970) 805–811) was undertaken on the basis of modern approach incorporating spectroscopic evidence and quantum chemical information and allowing for intramolecular large-amplitude motion. The results of the analysis prove the internal insufficiency of the experimental material in the description of the accurate positions of the peaks on the radial distribution function. Additional experimental investigation of pentahalide molecules, especially at high temperatures, is of interest.

COSMO-RSC: Second-Order Quasi-Chemical Theory Recovering Local Surface Correlation Effects.

The conductor-like screening model for realistic solvation (COSMO-RS) was introduced 20 years ago and meanwhile has become an important tool for the prediction of fluid phase equilibrium properties. Starting from quantum chemical information about the surface polarity of solutes and solvents, it solves the statistical thermodynamics of molecules in liquid phases by the very efficient approximation of independently pairwise interacting surfaces, which meanwhile was shown to be equivalent to Guggenheim's quasi-chemical theory. One of the basic limitations of COSMO-RS, as of any quasi-chemical model, is the neglect of neighbor information, i.e., of local correlations of surface types on the molecular surface. In this paper we present the completely novel concept of using the first-order COSMO-RS contact probabilities for the construction of local surface correlation functions. These are fed as an entropic correction for the pair interactions into a second COSMO-RS self-consistency loop, which yields new contact probabilities, enthalpies, free energies and activity coefficients recovering much of the originally lost neighbor effects. By a novel analytic correction for concentration dependent interactions, the resulting activity coefficients remain exactly Gibbs-Duhem consistent. The theory is demonstrated on the example of a lattice Monte Carlo fluid of dimerizing pseudomolecules. In this showcase the strong deviations of the lattice Monte Carlo fluid from quasi-chemical theory are almost perfectly reproduced by COSMO-RSC.

Interpolation for molecular dynamics simulations: from ions in gas phase to proteins in solution

The interpolation technique has shown many promises for simulating chemical dynamics with quantum chemical accuracy at molecular mechanics speed. This is achieved by constructing analytic potential energy surfaces with quantum chemical information at multiple conformational points, without assuming any functional form for the potentials. Here, we briefly review the course the method was developed over the past few decades, with a special focus on the activities in Korea. We also describe its strengths and weaknesses toward describing condensed phase chemical dynamics with the present implementations. Perspectives for future developments toward increasing applicability are discussed as concluding remarks. © 2015 Wiley Periodicals, Inc.

Proton pumping in cytochrome c oxidase: Energetic requirements and the role of two proton channels

Cytochrome c oxidase is a superfamily of membrane bound enzymes catalyzing the exergonic reduction of molecular oxygen to water, producing an electrochemical gradient across the membrane. The gradient is formed both by the electrogenic chemistry, taking electrons and protons from opposite sides of the membrane, and by proton pumping across the entire membrane. In the most efficient subfamily, the A-family of oxidases, one proton is pumped in each reduction step, which is surprising considering the fact that two of the reduction steps most likely are only weakly exergonic. Based on a combination of quantum chemical calculations and experimental information, it is here shown that from both a thermodynamic and a kinetic point of view, it should be possible to pump one proton per electron also with such an uneven distribution of the free energy release over the reduction steps, at least up to half the maximum gradient. A previously suggested pumping mechanism is developed further to suggest a reason for the use of two proton transfer channels in the A-family. Since the rate of proton transfer to the binuclear center through the D-channel is redox dependent, it might become too slow for the steps with low exergonicity. Therefore, a second channel, the K-channel, where the rate is redox-independent is needed. A redox-dependent leakage possibility is also suggested, which might be important for efficient energy conservation at a high gradient. A mechanism for the variation in proton pumping stoichiometry over the different subfamilies of cytochrome oxidase is also suggested. This article is part of a Special Issue entitled: 18th European Bioenergetic Conference.

Grand challenges in quantum‐classical modeling of molecule–surface interactions

A detailed understanding of the adsorption of small molecules or macromolecules to a materials surface is of importance, for example, in the context of material and biomaterial research. Classical atomistic simulations in principle provide microscopic insight in the complex entropic and enthalpic interplay at the interface. However, an application of classical atomistic simulation techniques to such interface systems is a nontrivial problem, mostly because commonly used force fields cannot be straightforwardly applied, as they are usually developed to reproduce bulk properties of either solids or liquids but not the interfacial region between two phases. Therefore, a dual-scale modeling approach has often been the method of choice in the past, in which the classical force field is parameterized such that quantum chemical information on near-surface conformations and adsorption energies is reproduced by the classical force field. We will discuss in this review the current state-of-the-art of quantum-classical modeling of molecule-surface interactions and outline the major challenges in this field. In this context, we will, among other things, lay emphasis on discussing ways to obtain representable force fields and propose systematic and system-independent strategies to optimize the quantum-classical fitting procedure.

Mechanism and microkinetics of the Fischer–Tropsch reaction

The increasing availability of quantum-chemical data on surface reaction intermediates invites one to revisit unresolved mechanistic issues in heterogeneous catalysis. One such issue of particular current interest is the molecular basis of the Fischer-Tropsch reaction. Here we review current molecular understanding of this reaction that converts synthesis gas into longer hydrocarbons where we especially elucidate recent progress due to the contributions of computational catalysis. This perspective highlights the theoretical approach to heterogeneous catalysis that aims for kinetic prediction from quantum-chemical first principle data. Discussion of the Fischer-Tropsch reaction from this point of view is interesting because of the several mechanistic options available for this reaction. There are many proposals on the nature of the monomeric single C atom containing intermediate that is inserted into the growing hydrocarbon chain as well as on the nature of the growing hydrocarbon chain itself. Two dominant conflicting mechanistic proposals of the Fischer-Tropsch reaction that will be especially compared are the carbide mechanism and the CO insertion mechanism, which involve cleavage of the C-O bond of CO before incorporation of a CHx species into the growing hydrocarbon chain (the carbide mechanism) or after incorporation into the growing hydrocarbon chain (the CO insertion mechanism). The choice of a particular mechanism has important kinetic consequences. Since it is based on molecular information it also affects the structure sensitivity of this particular reaction and hence influences the choice of catalyst composition. We will show how quantum-chemical information on the relative stability of relevant reaction intermediates and estimates of the rate constants of corresponding elementary surface reactions provides a firm foundation to the kinetic analysis of such reactions and allows one to discriminate between the different mechanistic options. The paper will be concluded with a short perspective section dealing with the needs for future research. Many of the current key questions on the physical chemistry as well as computational study of heterogeneous catalysis relate to particular topics for further research on the fundamental aspects of Fischer-Tropsch catalysis.

Prediction of liquid chromatographic retention behavior based on quantum chemical parameters using supervised self organizing maps

Self organizing maps (SOMs) in a supervised mode were applied for prediction of liquid chromatographic retention behavior of chemical compounds based on their quantum chemical information. The proposed algorithm was simple and required only a small alteration of the standard SOM algorithm. The application was illustrated by the prediction of the retention indices of bifunctionally substituted N-benzylideneanilines (NBA) and the prediction of the retention factors of some pesticides. Although the predictive ability of the supervised SOM could not be significantly greater than that of some previously established neural network methods, such as a radial basis function (RBF) neural network and a back-propagation artificial neural network (ANN), the main advantage of the proposed method was the ability to reveal non-linear structure of the model. The complex relationships between samples could be visualized using U-matrix and the influence of each variable on the predictive model could be investigated using component planes—which can provide chemical insight.

Theoretical study on the OH-initiated atmospheric reaction of 1,1,1-trichloro-2,2-bis(4-chlorophenyl) ethane (DDT)

p,p′-DDT is a member of the widely used organochlorine pesticides. In this paper, the mechanism for the OH-initiated reaction of p,p′-DDT is studied at the MPWB1K/6–311 + G(3df,2p)//MPWB1K/6–31 + G(d,p) level. The OH addition reaction to the C2 atom and H abstraction reaction from alkyl are energetically favorable pathways in OH-initiated reactions of DDT. On the basis of the quantum chemical information, the Rice–Ramsperger–Kassel–Marcus (RRKM) theory is used to calculate the rate constants over the temperature range of 200–800 K. The OH addition reaction is dominant according to the branching ratio, and the atmospheric lifetime of p,p′-DDT is 7.9 days. This work provides a comprehensive understanding of the OH-initiated atmospheric degradation of p,p′-DDT which is helpful for experiments and risk assessment.

Real‐time quantum chemistry

AbstractSignificant progress in the development of efficient and fast algorithms for quantum chemical calculations has been made in the past two decades. The main focus has always been the desire to be able to treat ever larger molecules or molecular assemblies—especially linear and sublinear scaling techniques are devoted to the accomplishment of this goal. However, as many chemical reactions are rather local, they usually involve only a limited number of atoms so that models of about 200 (or even less) atoms embedded in a suitable environment are sufficient to study their mechanisms. Thus, the system size does not need to be enlarged, but remains constant for reactions of this type that can be described by less than 200 atoms. The question then arises how fast one can obtain the quantum chemical results. This question is not directly answered by linear‐scaling techniques. In fact, ideas such as haptic quantum chemistry (HQC) or interactive quantum chemistry require an immediate provision of quantum chemical information which demands the calculation of data in “real time.” In this perspective, we aim at a definition of real‐time quantum chemistry, explore its realm and eventually discuss applications in the field of HQC. For the latter, we elaborate whether a direct approach is possible by virtue of real‐time quantum chemistry. © 2012 Wiley Periodicals, Inc.

Solvent-screening and co-crystal screening for drug development with COSMO-RS

Bringing active pharmaceutical or agrochemical ingredients (APIs) in solution often is the most demanding step in pharmaceutical and agrochemical development. The COSMO-RS method, which has been originally developed by the author during his 12 years at Bayer, is a unique combination of quantum chemical information and liquid phase thermodynamics and currently is proven to be the most accurate method for predicting the free energy of molecules in solution. Based on COSMORS theory the COSMOtherm suite of software tools is able to address a broad range of important aspects of solubilization and thus is an ideally suited toolset for rational solubilization development: Solvent screening, including mixtures and variable temperatures