Frontiers in electronic structure theory

Chemical-physical behavior of Hydroxyapatite: A modeling approach

Hydroxyapatite (HAp) is a ceramic composed of calcium phosphate, frequently employed as a bone substitute material due to its biocompatibility and bioactivity. Over the past century, there has been substantial attention in fields such as orthopedics and plastic surgery. Remarkably, synthetic HAp exhibits properties akin to those found in natural bone and teeth. Computational theoretical chemistry focuses on numerically computing molecular electronic structures and interactions. As chemistry education evolves, it's imperative to acknowledge the increasing significance of computational tools in research. Density Functional Theory (DFT) stands out as the most widely adopted method in contemporary computational chemistry. In this study, we synthesized Hydroxyapatite (HAp) via the double decomposition method using synthetic sources. The synthesized materials underwent thorough characterization, including X-ray Diffraction (XRD), UV-visible spectroscopy, and Fourier Transform Infrared (FTIR) spectroscopy under various conditions. Additionally, we performed quantum mechanical computations on the HAp molecule using density functional theory. Our results were then compared with experimental data. Our experimental findings highlight the successful synthesis of HAp, particularly under specific temperature conditions. Moreover, the quantum chemistry calculations exhibited excellent agreement with the experimental results, especially in terms of spectroscopic characterizations.

Gas phase H+, H3O+ and NH4+ affinities of oxygen-bearing volatile organic compounds; DFT calculations for soft chemical ionisation mass spectrometry

Quantum chemistry calculations were performed using the density functional theory, DFT, to understand the structures and energetics of organic ions relevant to gas phase ion chemistry in soft chemical ionisation mass spectrometry analytical methods. Geometries of a range of neutral volatile organic compound molecules and ions resulting from protonation, the addition of H3O+ and the addition of NH4+ were optimised using the B3LYP hybrid DFT method. Then, the total energies and the normal mode vibrational frequencies were determined, and the total enthalpies of the neutral molecules and ions were calculated for the standard temperature and pressure. The calculations were performed for several feasible structures of each of the ions. The proton affinities of several benchmark molecules agree with the accepted values within ±4 kJ mol-1, indicating that B3LYP/6-311++G(d,p) provides chemical accuracy for oxygen-containing volatile organic compounds. It was also found that the binding energies of H3O+ and NH4+ to molecules correlate with their proton affinities. The results contribute to the understanding of ligand switching ion-molecule reactions important for secondary electrospray ionisation, SESI, and selected ion flow tube, SIFT, mass spectrometries.

Multicomponent Cholesky Decomposition: Application to Nuclear-Electronic Orbital Theory

The Cholesky decomposition technique is commonly used to reduce the memory requirement for storing two-particle repulsion integrals in quantum chemistry calculations that use atomic orbital bases. However, when quantum methods use multicomponent bases, such as nuclear-electronic orbitals, additional challenges are introduced due to asymmetric two-particle integrals. This work proposes several multicomponent Cholesky decomposition methods for calculations using nuclear-electronic orbital density functional theory. To analyze the errors in different Cholesky decomposition components, benchmark calculations using water clusters are carried out. The largest benchmark calculation is a water cluster (H2O)27 where all 54 protons are treated quantum mechanically. This study provides energetic and complexity analyses to demonstrate the accuracy and performance of the proposed multicomponent Cholesky decomposition method.

Relativistic resolution-of-the-identity with Cholesky integral decomposition

In this study, we present an efficient integral decomposition approach called the restricted-kinetic-balance resolution-of-the-identity (RKB-RI) algorithm, which utilizes a tunable RI method based on the Cholesky integral decomposition for in-core relativistic quantum chemistry calculations. The RKB-RI algorithm incorporates the restricted-kinetic-balance condition and offers a versatile framework for accurate computations. Notably, the Cholesky integral decomposition is employed not only to approximate symmetric large-component electron repulsion integrals but also those involving small-component basis functions. In addition to comprehensive error analysis, we investigate crucial conditions, such as the kinetic balance condition and variational stability, which underlie the applicability of Dirac relativistic electronic structure theory. We compare the computational cost of the RKB-RI approach with the full in-core method to assess its efficiency. To evaluate the accuracy and reliability of the RKB-RI method proposed in this work, we employ actinyl oxides as benchmark systems, leveraging their properties for validation purposes. This investigation provides valuable insights into the capabilities and performance of the RKB-RI algorithm and establishes its potential as a powerful tool in the field of relativistic quantum chemistry.

Finite Difference Interpolation for Reduction of Grid-Related Errors in Real-Space Pseudopotential Density Functional Theory

The real-space pseudopotential approach is a well-known method for large-scale density functional theory (DFT) calculations. One of its main limitations, however, is the introduction of errors associated with the positioning of the underlying real-space grid, a phenomenon usually known as the "egg-box" effect. The effect can be controlled by using a finer grid, but this raises the cost of the calculations or even undermines their feasibility altogether. Therefore, there is ongoing interest in the reduction of the effect per a given real-space grid. Here, we present a finite difference interpolation of electron orbitals as a means of exploiting the high resolution of the pseudopotential to reduce egg-box effects systematically. We implement the method in PARSEC, a finite difference real-space pseudopotential DFT code, and demonstrate error mitigation and improved convergence at a low additional computational cost.

Experimental Studies on TiO2 NT with Metal Dopants through Co-Precipitation, Sol-Gel, Hydrothermal Scheme and Corresponding Computational Molecular Evaluations

In the last decade, TiO₂ nanotubes have attracted the attention of the scientific community and industry due to their exceptional photocatalytic properties, opening a wide range of additional applications in the fields of renewable energy, sensors, supercapacitors, and the pharmaceutical industry. However, their use is limited because their band gap is tied to the visible light spectrum. Therefore, it is essential to dope them with metals to extend their physicochemical advantages. In this review, we provide a brief overview of the preparation of metal-doped TiO₂ nanotubes. We address hydrothermal and alteration methods that have been used to study the effects of different metal dopants on the structural, morphological, and optoelectrical properties of anatase and rutile nanotubes. The progress of DFT studies on the metal doping of TiO₂ nanoparticles is discussed. In addition, the traditional models and their confirmation of the results of the experiment with TiO₂ nanotubes are reviewed, as well as the use of TNT in various applications and the future prospects for its development in other fields. We focus on the comprehensive analysis and practical significance of the development of TiO₂ hybrid materials and the need for a better understanding of the structural-chemical properties of anatase TiO₂ nanotubes with metal doping for ion storage devices such as batteries.

Design of Zr-MOFs by Introducing Multiple Ligands for Efficient and Selective Capturing of Pb(II) from Aqueous Solutions

The existence of lead ions seriously affects the quality of many metal products in metallurgical enterprises. Currently, the various methods of lead-ion removal tried by researchers will affect valuable metals in the removal process, thus resulting in low economic efficiency. In this study, a novel metal-organic framework adsorbent (UiO-FHD) which efficiently and selectively captures lead ions is developed by introducing multiple ligands. The maximum adsorption capacity of lead ions is 433.15 mg/g at pH 5. The adsorption process accords with the pseudo-second-order kinetic and the Langmuir isotherm models at room temperature. Thermodynamic experiments indicate that the removal of Pb(II) is facilitated by appropriate temperature reduction. The performance tests indicate that UiO-FHD maintains a high removal rate of 90.35% for Pb(II) after four consecutive adsorption-desorption cycles. The distribution coefficient of lead ions (26.7 L/g) shows that UiO-FHD has excellent selective adsorption for lead ions. It is revealed that the chelation of the sulfhydryl groups and the electrostatic interaction of the hydroxyl groups are the dominant factors to improve the removal rate of Pb(II) by density functional theory calculations. This study clarifies the value of self-designed novel organic ligands in metal-organic framework materials that selectively capture heavy-metal ions.

Machine Learned Composite Methods for Electronic Structure Theory

Because of the prohibitive scaling of ab initio techniques for modeling chemical species with high accuracy, they are not generally tractable for large systems. It is therefore of considerable interest to develop high-accuracy computational models with low computational cost that can afford predictions of electronic structure and properties of macromolecular species. Composite methods, as first introduced by Pople [Pople, J. A.; Head-Gordon, M.; Fox, D. J.; Raghavachari, K.; Curtiss, L. A. J. Chem. Phys.1989, 90, 5622.], are an intuitive solution to this problem as they seek to systematically increase accuracy in model chemistries by taking advantage of favorable error cancellation among reasonably low-cost models. By linearly combining a series of carefully chosen model chemistries, the result of a prohibitive-scaling correlated model chemistry with a large basis set may be approximated with relatively good fidelity. However, the full extent to which the choice of low-cost models dictates the predictive accuracy of composite methods is not known, and a full exploration of all model chemistries would be advantageous for the design and validation of a generalizable composite method for widespread application. Here, we show that remarkable accuracy can be generally achieved with composite methods that are more judiciously constructed, leading to increased accuracy with significantly reduced computational cost. By designing a systematic procedure for the automated generation and assessment of over 10 billion unique composite methods, we have extensively explored the space of modern model chemistries to elucidate important design principles in the construction of reliable composite procedures. We anticipate our work to be the starting point in the pursuit of creative approaches to modeling large chemical systems with high accuracy by using novel combinatorial modeling.

Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning

Accurate thermochemistry is essential in many chemical disciplines, such as astro-, atmospheric, or combustion chemistry. These areas often involve fleetingly existent intermediates whose thermochemistry is difficult to assess. Whenever direct calorimetric experiments are infeasible, accurate computational estimates of relative molecular energies are required. However, high-level computations, often using coupled cluster theory, are generally resource-intensive. To expedite the process using machine learning techniques, we generated a database of energies for small organic molecules at the CCSD(T)/cc-pVDZ, CCSD(T)/aug-cc-pVDZ, and CCSD(T)/cc-pVTZ levels of theory. Leveraging the power of deep learning by employing graph neural networks, we are able to predict the effect of perturbatively included triples (T), that is, the difference between CCSD and CCSD(T) energies, with a mean absolute error of 0.25, 0.25, and 0.28 kcal mol^-1 (R² of 0.998, 0.997, and 0.998) with the cc-pVDZ, aug-cc-pVDZ, and cc-pVTZ basis sets, respectively. Our models were further validated by application to three validation sets taken from the S22 Database as well as to a selection of known theoretically challenging cases.

Accelerating Ab Initio Quantum Mechanical and Molecular Mechanical (QM/MM) Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semiempirical QM/MM Hamiltonian

Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.

Quantum computing of Hückel molecular orbitals of π-electron systems

In order to demonstrate an applicability of quantum computing to fundamental electronic structure problems of molecules, we describe the Hückel Hamiltonian matrix in terms of quantum gates and obtain the orbital energies of fundamental π-electron molecules (C₂H₄, C₃H₄, C₄H₄, C₄H₆, and C₆H₆) using a superconducting-qubit-type quantum computer (ibm_kawasaki) with a post-selection error mitigation method. We show that the orbital energies are obtained with sufficiently high accuracy and small uncertainties and that characteristic features of the electronic structure of the π-electron molecules can be extracted by quantum computing in a straightforward manner.

Δ-Quantum machine-learning for medicinal chemistry

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.

Tracing the Primordial Chemical Life of Glycine: A Review from Quantum Chemical Simulations

Glycine (Gly), NH₂CH₂COOH, is the simplest amino acid. Although it has not been directly detected in the interstellar gas-phase medium, it has been identified in comets and meteorites, and its synthesis in these environments has been simulated in terrestrial laboratory experiments. Likewise, condensation of Gly to form peptides in scenarios resembling those present in a primordial Earth has been demonstrated experimentally. Thus, Gly is a paradigmatic system for biomolecular building blocks to investigate how they can be synthesized in astrophysical environments, transported and delivered by fragments of asteroids (meteorites, once they land on Earth) and comets (interplanetary dust particles that land on Earth) to the primitive Earth, and there react to form biopolymers as a step towards the emergence of life. Quantum chemical investigations addressing these Gly-related events have been performed, providing fundamental atomic-scale information and quantitative energetic data. However, they are spread in the literature and difficult to harmonize in a consistent way due to different computational chemistry methodologies and model systems. This review aims to collect the work done so far to characterize, at a quantum mechanical level, the chemical life of Gly, i.e., from its synthesis in the interstellar medium up to its polymerization on Earth.

Small-Basis Set Density-Functional Theory Methods Corrected with Atom-Centered Potentials

Density functional theory (DFT) is currently the most popular method for modeling noncovalent interactions and thermochemistry. The accurate calculation of noncovalent interaction energies, reaction energies, and barrier heights requires choosing an appropriate functional and, typically, a relatively large basis set. Deficiencies of the density-functional approximation and the use of a limited basis set are the leading sources of error in the calculation of noncovalent and thermochemical properties in molecular systems. In this article, we present three new DFT methods based on the BLYP, M06-2X, and CAM-B3LYP functionals in combination with the 6-31G* basis set and corrected with atom-centered potentials (ACPs). ACPs are one-electron potentials that have the same form as effective-core potentials, except they do not replace any electrons. The ACPs developed in this work are used to generate energy corrections to the underlying DFT/basis-set method such that the errors in predicted chemical properties are minimized while maintaining the low computational cost of the parent methods. ACPs were developed for the elements H, B, C, N, O, F, Si, P, S, and Cl. The ACP parameters were determined using an extensive training set of 118655 data points, mostly of complete basis set coupled-cluster level quality. The target molecular properties for the ACP-corrected methods include noncovalent interaction energies, molecular conformational energies, reaction energies, barrier heights, and bond separation energies. The ACPs were tested first on the training set and then on a validation set of 42567 additional data points. We show that the ACP-corrected methods can predict the target molecular properties with accuracy close to complete basis set wavefunction theory methods, but at a computational cost of double-ζ DFT methods. This makes the new BLYP/6-31G*-ACP, M06-2X/6-31G*-ACP, and CAM-B3LYP/6-31G*-ACP methods uniquely suited to the calculation of noncovalent, thermochemical, and kinetic properties in large molecular systems.

Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree-Fock Methods Corrected with Atom-Centered Potentials

There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bioorganic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*) and the HF-3c method. The new ACPs are trained on a very large set (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for noncovalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set density functional theory (DFT) but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and noncovalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.

Toward the Minimal Floating Operation Count Cholesky Decomposition of Electron Repulsion Integrals

As quantum chemistry calculations deal with molecular systems of increasing size, the memory requirement to store electron-repulsion integrals (ERIs) greatly outpaces the physical memory available in computing hardware. The Cholesky decomposition of ERIs provides a convenient yet accurate technique to reduce the storage requirement of integrals. Recent developments of a two-step algorithm have drastically reduced the memory operation (MOP) count, leaving the floating operation (FLOP) count as the last frontier of cost reduction in the Cholesky ERI algorithm. In this report, we introduce a dynamic integral tracking, reusing, and compression/elimination protocol embedded in the two-step Cholesky ERI method. Benchmark studies suggest that this technique becomes particularly advantageous when the basis set consists of many computationally expensive high-angular-momentum basis functions. With this dynamic-ERI improvement, the Cholesky ERI approach proves to be a highly efficient algorithm with minimal FLOP and MOP count.

Computational Study of Structural, Molecular Orbitals, Optical and Thermodynamic Parameters of Thiophene Sulfonamide Derivatives

Thiophene and sulfonamide derivatives serve as biologically active compounds, used for the manufacture of large numbers of new drugs. In this study, 11 selected derivatives of thiophene sulfonamide were computed for their geometric parameters, such as hyperpolarizability, chemical hardness (ƞ), electronic chemical potential (μ), electrophilicity index (ω), ionization potential (I), and electron affinity (A). In addition, FT-IR and UV-Vis spectra were also simulated through theoretical calculations. The geometrical parameters and vibrational frequencies with assignments of the vibrational spectra strongly resemble the experimentally calculated values. Besides, the frontier molecular orbitals were also determined for various intramolecular interactions that are responsible for the stability of the compounds. The isodensity surfaces of the frontier molecular orbitals (FMOs) are the same pattern in most of the compounds, but in some compounds are disturbed due to the presence of highly electronegative hetero-atoms. In this series of compounds, 3 shows the highest HOMO–LUMO energy gap and lowest hyperpolarizability, which leads to the most stable compound and less response to nonlinear optical (NLO), while 7 shows the lowest HOMO–LUMO energy gap and highest hyperpolarizability, which leads to a less stable compound and a high NLO response. All compounds have their extended three-dimensional p-electronic delocalization which plays an important role in studying NLO responses.

Computational Surface Modelling of Ices and Minerals of Interstellar Interest—Insights and Perspectives

The universe is molecularly rich, comprising from the simplest molecule (H2) to complex organic molecules (e.g., CH3CHO and NH2CHO), some of which of biological relevance (e.g., amino acids). This chemical richness is intimately linked to the different physical phases forming Solar-like planetary systems, in which at each phase, molecules of increasing complexity form. Interestingly, synthesis of some of these compounds only takes place in the presence of interstellar (IS) grains, i.e., solid-state sub-micron sized particles consisting of naked dust of silicates or carbonaceous materials that can be covered by water-dominated ice mantles. Surfaces of IS grains exhibit particular characteristics that allow the occurrence of pivotal chemical reactions, such as the presence of binding/catalytic sites and the capability to dissipate energy excesses through the grain phonons. The present know-how on the physicochemical features of IS grains has been obtained by the fruitful synergy of astronomical observational with astrochemical modelling and laboratory experiments. However, current limitations of these disciplines prevent us from having a full understanding of the IS grain surface chemistry as they cannot provide fundamental atomic-scale of grain surface elementary steps (i.e., adsorption, diffusion, reaction and desorption). This essential information can be obtained by means of simulations based on computational chemistry methods. One capability of these simulations deals with the construction of atom-based structural models mimicking the surfaces of IS grains, the very first step to investigate on the grain surface chemistry. This perspective aims to present the current state-of-the-art methods, techniques and strategies available in computational chemistry to model (i.e., construct and simulate) surfaces present in IS grains. Although we focus on water ice mantles and olivinic silicates as IS test case materials to exemplify the modelling procedures, a final discussion on the applicability of these approaches to simulate surfaces of other cosmic grain materials (e.g., cometary and meteoritic) is given.

Techniques for high-performance construction of Fock matrices

This paper presents techniques for Fock matrix construction that are designed for high performance on shared and distributed memory parallel computers when using Gaussian basis sets. Four main techniques are considered. (1) To calculate electron repulsion integrals, we demonstrate batching together the calculation of multiple shell quartets of the same angular momentum class so that the calculation of large sets of primitive integrals can be efficiently vectorized. (2) For multithreaded summation of entries into the Fock matrix, we investigate using a combination of atomic operations and thread-local copies of the Fock matrix. (3) For distributed memory parallel computers, we present a globally accessible matrix class for accessing distributed Fock and density matrices. The new matrix class introduces a batched mode for remote memory access that can reduce the synchronization cost. (4) For density fitting, we exploit both symmetry (of the Coulomb and exchange matrices) and sparsity (of 3-index tensors) and give a performance comparison of density fitting and the conventional direct calculation approach. The techniques are implemented in an open-source software library called GTFock.

The Comparison between Single Atom Catalysis and Surface Organometallic Catalysis

Single atom catalysis (SAC) is a recent discipline of heterogeneous catalysis for which a single atom on a surface is able to carry out various catalytic reactions. A kind of revolution in heterogeneous catalysis by metals for which it was assumed that specific sites or defects of a nanoparticle were necessary to activate substrates in catalytic reactions. In another extreme of the spectrum, surface organometallic chemistry (SOMC), and, by extension, surface organometallic catalysis (SOMCat), have demonstrated that single atoms on a surface, but this time with specific ligands, could lead to a more predictive approach in heterogeneous catalysis. The predictive character of SOMCat was just the result of intuitive mechanisms derived from the elementary steps of molecular chemistry. This review article will compare the aspects of single atom catalysis and surface organometallic catalysis by considering several specific catalytic reactions, some of which exist for both fields, whereas others might see mutual overlap in the future. After a definition of both domains, a detailed approach of the methods, mostly modeling and spectroscopy, will be followed by a detailed analysis of catalytic reactions: hydrogenation, dehydrogenation, hydrogenolysis, oxidative dehydrogenation, alkane and cycloalkane metathesis, methane activation, metathetic oxidation, CO₂ activation to cyclic carbonates, imine metathesis, and selective catalytic reduction (SCR) reactions. A prospective resulting from present knowledge is showing the emergence of a new discipline from the overlap between the two areas.

Role of Mineral Surfaces in Prebiotic Chemical Evolution. In Silico Quantum Mechanical Studies

There is a consensus that the interaction of organic molecules with the surfaces of naturally-occurring minerals might have played a crucial role in chemical evolution and complexification in a prebiotic era. The hurdle of an overly diluted primordial soup occurring in the free ocean may have been overcome by the adsorption and concentration of relevant molecules on the surface of abundant minerals at the sea shore. Specific organic⁻mineral interactions could, at the same time, organize adsorbed molecules in well-defined orientations and activate them toward chemical reactions, bringing to an increase in chemical complexity. As experimental approaches cannot easily provide details at atomic resolution, the role of in silico computer simulations may fill that gap by providing structures and reactive energy profiles at the organic⁻mineral interface regions. Accordingly, numerous computational studies devoted to prebiotic chemical evolution induced by organic⁻mineral interactions have been proposed. The present article aims at reviewing recent in silico works, mainly focusing on prebiotic processes occurring on the mineral surfaces of clays, iron sulfides, titanium dioxide, and silica and silicates simulated through quantum mechanical methods based on the density functional theory (DFT). The DFT is the most accurate way in which chemists may address the behavior of the molecular world through large models mimicking chemical complexity. A perspective on possible future scenarios of research using in silico techniques is finally proposed.

Energy Gaps of Polyradicals from an Effective and Transferable Hamiltonian with through-Bond Interactions

Current model Hamiltonians and ab initio many-body quantum treatments of π-conjugated polyradicals formed from hydrocarbons produce divergent results because of numerical complexity and large size of the basis-function set used. We propose an alternative, three-term Hamiltonian, to describe these various polyradicals that simplifies considerably the computational cost while providing a physical interpretation for all three terms and a high degree of model universality. The essential feature of this Hamiltonian is a term, not present in previous models, describing the three-sited through-bond interaction that governs the noninteracting spin-up and spin-down sectors. A computation of the lowest energy gaps and spin configurations for the smaller polyradicals demonstrates the efficacy of the model and its potential in applications in revealing electrical conductivity and ferromagnetism of the more complicated substituted polyradicals.

Ultrafast dynamics of low-energy electron attachment via a non-valence correlation-bound state

The primary electron-attachment process in electron-driven chemistry represents one of the most fundamental chemical transformations with wide-ranging importance in science and technology. However, the mechanistic detail of the seemingly simple reaction of an electron and a neutral molecule to form an anion remains poorly understood, particularly at very low electron energies. Here, time-resolved photoelectron imaging was used to probe the electron-attachment process to a non-polar molecule using time-resolved methods. An initially populated diffuse non-valence state of the anion that is bound by correlation forces evolves coherently in ∼30 fs into a valence state of the anion. The extreme efficiency with which the correlation-bound state serves as a doorway state for low-energy electron attachment explains a number of electron-driven processes, such as anion formation in the interstellar medium and electron attachment to fullerenes.

Perspective: Quantum mechanical methods in biochemistry and biophysics

In this perspective article, I discuss several research topics relevant to quantum mechanical (QM) methods in biophysical and biochemical applications. Due to the immense complexity of biological problems, the key is to develop methods that are able to strike the proper balance of computational efficiency and accuracy for the problem of interest. Therefore, in addition to the development of novel ab initio and density functional theory based QM methods for the study of reactive events that involve complex motifs such as transition metal clusters in metalloenzymes, it is equally important to develop inexpensive QM methods and advanced classical or quantal force fields to describe different physicochemical properties of biomolecules and their behaviors in complex environments. Maintaining a solid connection of these more approximate methods with rigorous QM methods is essential to their transferability and robustness. Comparison to diverse experimental observables helps validate computational models and mechanistic hypotheses as well as driving further development of computational methodologies.

Long-range interactions from the many-pair expansion: A different avenue to dispersion in DFT

One of the several problems that plague majority of density functional theory calculations is their inability to properly account for long-range correlations giving rise to dispersion forces. The recently proposed many-pair expansion (MPE) [T. Zhu et al., Phys. Rev. B 93, 201108(R) (2016)] is a hierarchy of approximations that systematically corrects any deficiencies of an approximate functional to finally converge to the exact energy. This is achieved by decomposing the total density into a sum of two-electron densities and accounting for successive two-, four-, six-,… electron interactions. Here, we show that already low orders of MPE expansion recover the dispersion energy accurately. To this end, we employ the Pariser-Parr-Pople Hamiltonian and study the behavior of long-range interactions in trans-polyacetylene as well as stacks of ethylene and benzene molecules. We also show how convergence of the expansion is affected by electron conjugation and the choice of the density partitioning.

Atomic Spectral Methods for Ab Initio Molecular Electronic Energy Surfaces: Transitioning From Small-Molecule to Biomolecular-Suitable Approaches

Continuing attention has addressed incorportation of the electronically dynamical attributes of biomolecules in the largely static first-generation molecular-mechanical force fields commonly employed in molecular-dynamics simulations. We describe here a universal quantum-mechanical approach to calculations of the electronic energy surfaces of both small molecules and large aggregates on a common basis which can include such electronic attributes, and which also seems well-suited to adaptation in ab initio molecular-dynamics applications. In contrast to the more familiar orbital-product-based methodologies employed in traditional small-molecule computational quantum chemistry, the present approach is based on an "ex-post-facto" method in which Hamiltonian matrices are evaluated prior to wave function antisymmetrization, implemented here in the support of a Hilbert space of orthonormal products of many-electron atomic spectral eigenstates familiar from the van der Waals theory of long-range interactions. The general theory in its various forms incorporates the early semiempirical atoms- and diatomics-in-molecules approaches of Moffitt, Ellison, Tully, Kuntz, and others in a comprehensive mathematical setting, and generalizes the developments of Eisenschitz, London, Claverie, and others addressing electron permutation symmetry adaptation issues, completing these early attempts to treat van der Waals and chemical forces on a common basis. Exact expressions are obtained for molecular Hamiltonian matrices and for associated energy eigenvalues as sums of separate atomic and interaction-energy terms, similar in this respect to the forms of classical force fields. The latter representation is seen to also provide a long-missing general definition of the energies of individual atoms and of their interactions within molecules and matter free from subjective additional constraints. A computer code suite is described for calculations of the many-electron atomic eigenspectra and the pairwise-atomic Hamiltonian matrices required for practical applications. These matrices can be retained as functions of scalar atomic-pair separations and employed in assembling aggregate Hamiltonian matrices, with Wigner rotation matrices providing analytical representations of their angular degrees of freedom. In this way, ab initio potential energy surfaces are obtained in the complete absence of repeated evaluations and transformations of the one- and two-electron integrals at different molecular geometries required in most ab inito molecular calculations, with large Hamiltonian matrix assembly simplified and explicit diagonalizations avoided employing partitioning and Brillouin-Wigner or Rayleigh-Schrödinger perturbation theory. Illustrative applications of the important components of the formalism, selected aspects of the scaling of the approach, and aspects of "on-the-fly" interfaces with Monte Carlo and molecular-dynamics methods are described in anticipation of subsequent applications to biomolecules and other large aggregates.

Quantum chemistry and charge transport in biomolecules with superconducting circuits

We propose an efficient protocol for digital quantum simulation of quantum chemistry problems and enhanced digital-analog quantum simulation of transport phenomena in biomolecules with superconducting circuits. Along these lines, we optimally digitize fermionic models of molecular structure with single-qubit and two-qubit gates, by means of Trotter-Suzuki decomposition and Jordan-Wigner transformation. Furthermore, we address the modelling of system-environment interactions of biomolecules involving bosonic degrees of freedom with a digital-analog approach. Finally, we consider gate-truncated quantum algorithms to allow the study of environmental effects.

Recent application of calculations of metal complexes based on density functional theory

Recent application of density functional theory (DFT) for metal complexes is reviewed to show the achievements of DFT and the challenges for it, as well as the methods for selecting proper functionals.

Theory and practice of uncommon molecular electronic configurations

The electronic configuration of the molecule is the foundation of its structure and reactivity. The spin state is one of the key characteristics arising from the ordering of electrons within the molecule's set of orbitals. Organic molecules that have open-shell ground states and interesting physicochemical properties, particularly those influencing their spin alignment, are of immense interest within the up-and-coming field of molecular electronics. In this advanced review, we scrutinize various qualitative rules of orbital occupation and spin alignment, viz., the aufbau principle, Hund's multiplicity rule, and dynamic spin polarization concept, through the prism of quantum mechanics. While such rules hold in selected simple cases, in general the spin state of a system depends on a combination of electronic factors that include Coulomb and Pauli repulsion, nuclear attraction, kinetic energy, orbital relaxation, and static correlation. A number of fascinating chemical systems with spin states that fluctuate between triplet and open-shell singlet, and are responsive to irradiation, pH, and other external stimuli, are highlighted. In addition, we outline a range of organic molecules with intriguing non-aufbau orbital configurations. In such quasi-closed-shell systems, the singly occupied molecular orbital (SOMO) is energetically lower than one or more doubly occupied orbitals. As a result, the SOMO is not affected by electron attachment to or removal from the molecule, and the products of such redox processes are polyradicals. These peculiar species possess attractive conductive and magnetic properties, and a number of them that have already been developed into molecular electronics applications are highlighted in this review. WIREs Comput Mol Sci 2015, 5:440-459. doi: 10.1002/wcms.1233 For further resources related to this article, please visit the WIREs website.