Density functional tight binding: values of semi-empirical methods in an ab initio era

Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions

Silica nanoparticles (SNPs), one of the most widely researched materials in modern science, are now commonly exploited in surface coatings, biomedicine, catalysis, and engineering of novel self-assembling materials. Theoretical approaches are invaluable to enhancing fundamental understanding of SNP properties and behavior. Tremendous research attention is dedicated to modeling silica structure, the silica-water interface, and functionalization of silica surfaces for tailored applications. In this review, the range of theoretical methodologies are discussed that have been employed to model bare silica and functionalized silica. The evolution of silica modeling approaches is detailed, including classical, quantum mechanical, and hybrid methods and highlight in particular the last decade of theoretical simulation advances. It is started with discussing investigations of bare silica systems, focusing on the fundamental interactions at the silica-water interface, following with a comprehensively review of the modeling studies that examine the interaction of silica with functional ligands, peptides, ions, surfactants, polymers, and carbonaceous species. The review is concluded with the perspective on existing challenges in the field and promising future directions that will further enhance the utility and importance of the theoretical approaches in guiding the rational design of SNPs for applications in engineering and biomedicine.

Variations in proton transfer pathways and energetics on pristine and defect-rich quartz surfaces in water: Insights into the bimodal acidities of quartz

HYPOTHESIS

Understanding the mechanisms of proton transfer on quartz surfaces in water is critical for a range of processes in geochemical, environmental, and materials sciences. The wide range of surface acidities (>9 pKa units) found on the ubiquitous mineral quartz is caused by the structural variations of surface silanol groups. Molecular scale simulations provide essential tools for elucidating the origin of site-specific surface acidities.

SIMULATIONS

We used density-functional tight-binding-based molecular dynamics combined with rare-event metadynamics simulations to probe the mechanisms of deprotonation reactions from ten representative surface silanol groups found on both pristine and defect-rich quartz (101) surfaces with Si vacancies.

FINDINGS

The results show that deprotonation is a highly dynamic process where both the surface hydroxyls and bridging oxygen atoms serve as the proton acceptors, in addition to water. Deprotonation of embedded silanols through intrasurface proton transfer exhibited lower pKa values with less H-bond participation and higher energy barriers, suggesting a new mechanism to explain the bimodal acidity observed on quartz surface. Defect sites, recently shown to comprise a significant portion of the quartz (101) surface, diversify the coordination and local H-bonding environments of the surface silanols, changing both the deprotonation pathways and energetics, leading to a wider range of pKa values (2.4 to 11.5) than that observed on pristine quartz surface (10.4 and 12.1).

Exploring the Mechanisms behind Non-aromatic Fluorescence with the Density Functional Tight Binding Method

Recent experimental findings reveal nonconventional fluorescence emission in biological systems devoid of conjugated bonds or aromatic compounds, termed non-aromatic fluorescence (NAF). This phenomenon is exclusive to aggregated or solid states and remains absent in monomeric solutions. Previous studies focused on small model systems in vacuum show that the carbonyl stretching mode along with strong interaction of short hydrogen bonds (SHBs) remains the primary vibrational mode explaining NAF in these systems. In order to simulate larger model systems taking into account the effects of the surrounding environment, in this work we propose using the density functional tight-binding (DFTB) method in combination with non-adiabatic molecular dynamics (NAMD) and the mixed quantum/molecular mechanics (QM/MM) approach. We investigate the mechanism behind NAF in the crystal structure of l-pyroglutamine-ammonium, comparing it with the related nonfluorescent amino acid l-glutamine. Our results extend our previous findings to more realistic systems, demonstrating the efficiency and robustness of the proposed DFTB method in the context of NAMD in biological systems. Furthermore, due to its inherent low computational cost, this method allows for a better sampling of the nonradiative events at the conical intersection which is crucial for a complete understanding of this phenomenon. Beyond contributing to the ongoing exploration of NAF, this work paves the way for future application of this method in more complex biological systems such as amyloid aggregates, biomaterials, and non-aromatic proteins.

Three-Center Tight-Binding Together with Multipolar Auxiliary Functions

We present an ab initio tight-binding method that allows to improve on the effective potential and minimal basis approximations employed in semiempirical calculations. Three-center expansions are used to evaluate the zeroth-order Hamiltonian matrix elements and repulsive energy terms in the spirit of the Horsfield method. Self-consistency is handled by expanding atomic orbital products in an auxiliary basis following the work of Giese and York, combined with a two-center expansion of the exchange-correlation kernels. Together with nonminimal main basis sets (double-ζ plus polarization), we show that the resulting method trades a modest amount of accuracy for a significant gain in speed, compared to that of numerical atomic orbital density functional theory, in calculations on small molecules, bulk compounds, and metal nanoclusters.

Flexibility of Binding Site is Essential to the Ca2+ Selectivity in EF-Hand Calcium-Binding Proteins

High binding affinity and selectivity of metal ions are essential to the function of metalloproteins. Thus, understanding the factors that determine these binding characteristics is of major interest for both fundamental mechanistic investigations and guiding of the design of novel metalloproteins. In this work, we perform QM cluster model calculations and quantum mechanics/molecular mechanics (QM/MM) free energy simulations to understand the binding selectivity of Ca2+ and Mg2+ in the wild-type carp parvalbumin and its mutant. While a nonpolarizable MM model (CHARMM36) does not lead to the correct experimental trend, treatment of the metal binding site with the DFTB3 model in a QM/MM framework leads to relative binding free energies (ΔΔGbind) comparable with experimental data. For the wild-type (WT) protein, the calculated ΔΔGbind is ∼6.6 kcal/mol in comparison with the experimental value of 5.6 kcal/mol. The good agreement highlights the value of a QM description of the metal binding site and supports the role of electronic polarization and charge transfer to metal binding selectivity. For the D51A/E101D/F102W mutant, different binding site models lead to considerable variations in computed binding affinities. With a coordination number of seven for Ca2+, which is shown by QM/MM metadynamics simulations to be the dominant coordination number for the mutant, the calculated relative binding affinity is ∼4.8 kcal/mol, in fair agreement with the experimental value of 1.6 kcal/mol. The WT protein is observed to feature a flexible binding site that accommodates a range of coordination numbers for Ca2+, which is essential to the high binding selectivity for Ca2+ over Mg2+. In the mutant, the E101D mutation reduces the flexibility of the binding site and limits the dominant coordination number of Ca2+ to be seven, thereby leading to reduced binding selectivity against Mg2+. Our results highlight that the binding selectivity of metal ions depends on both the structural and dynamical properties of the protein binding site.

Benchmark Data Set of Crystalline Organic Semiconductors

This work reports a Benchmark Data set of Crystalline Organic Semiconductors to test calculations of the structural and electronic properties of these materials in the solid state. The data set contains 67 crystals consisting of mostly rigid molecules with a single dominant conformer, covering the majority of known structural types. The experimental crystal structure is available for the entire data set, whereas zero-temperature unit cell volume can be reliably estimated for a subset of 28 crystals. Using this subset, we benchmark r2SCAN-D3 and PBE-D3 density functionals. Then, for the entire data set, we benchmark approximate density functional theory (DFT) methods, including GFN1-xTB and DFTB3(3ob-3-1), with various dispersion corrections against r2SCAN-D3. Our results show that r2SCAN-D3 geometries are accurate within a few percent, which is comparable to the statistical uncertainty of experimental data at a fixed temperature, but the unit cell volume is systematically underestimated by 2% on average. The several times faster PBE-D3 provides an unbiased estimate of the volume for all systems except for molecules with highly polar bonds, for which the volume is substantially overestimated in correlation with the underestimation of atomic charges. Considered approximate DFT methods are orders of magnitude faster and provide qualitatively correct but overcompressed crystal structures unless the dispersion corrections are fitted by unit cell volume.

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties

The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).

Synergy of semiempirical models and machine learning in computational chemistry

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the "Andes" cluster of the Oak Ridge Leadership Computing Facility (OLCF).

Effect of surface functional groups on MXene conductivity

We report the in-plane electron transport in the MXenes (i.e., within the MXene layers) as a function of composition using the density-functional tight-binding method, in conjunction with the non-equilibrium Green's functions technique. Our study reveals that all MXene compositions have a linear relationship between current and voltage at lower potentials, indicating their metallic character. However, the magnitude of the current at a given voltage (conductivity) has different trends among different compositions. For example, MXenes without any surface terminations (Ti3C2) exhibit higher conductivity compared to MXenes with surface functionalization. Among the MXenes with -O and -OH termination, those with -O surface termination have lower conductivity than the ones with -OH surface terminations. Interestingly, conductivity changes with the ratio of -O and -OH on the MXene surface. Our calculated I-V curves and their conductivities correlate well with transmission functions and the electronic density of states around the Fermi level. The surface composition-dependent conductivity of the MXenes provides a path to tune the in-plane conductivity for enhanced pseudocapacitive performance.

Effect of vacancy defect and strain on the structural, electronic and magnetic properties of carbon nitride 2D monolayers by DFTB method

We investigate the electronic and magnetic properties of CnNm(C₆N₆, C₂N, C₃N and C₃N₄) using density functional tight-binding (DFTB) method. We find that these compounds are dynamically stable and their electronic band gaps are in the range of 0.59-3.28 eV. We show that the electronic structure is modulated by strain and the semiconducting behavior is well preserved except for C₃N at +5% biaxial strain, where a transition from semiconductor to metal was observed. Under +3% biaxial strain, C₃N₄undergoes a transition from an indirect (K-Γ) to a direct (Γ-Γ) band gap. Moreover, band gap of C₂N transforms from direct (Γ-Γ) to indirect (M-Γ) under +4% biaxial strain. However, no change in the nature of the band gap of C₆N₆. Further, when the studied materials under uniaxial tensile strain, their bandgaps reduce. Our theoretical study showed that an indirect-to-direct nature transition may occur for C₆N₆and for C₃N₄, which broadens their applications. On the other hand, magnetism is observed in all N-vacancy defected CnNm, which encourages its application in spintronic. Moreover, calculations of formation energies indicate that N-vacancy is energetically more favorable than C-vacancy in both C₂N and C₃N₄. Opposite behavior found for C₆N₆and C₃N.

Oxide- and Silicate-Water Interfaces and Their Roles in Technology and the Environment

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by "anomalies" or "non-idealities" such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide- and silicate-water interfaces. This critical review discusses how science progresses from understanding ideal solid-water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. We anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

Development of NOTCH, an all-electron, beyond-NDDO semiempirical method: Application to diatomic molecules

In this work, we develop a new semiempirical method, dubbed NOTCH (Natural Orbital Tied Constructed Hamiltonian). Compared to existing semiempirical methods, NOTCH is less empirical in its functional form as well as parameterization. Specifically, in NOTCH, (1) the core electrons are treated explicitly; (2) the nuclear-nuclear repulsion term is calculated analytically, without any empirical parameterization; (3) the contraction coefficients of the atomic orbital (AO) basis depend on the coordinates of the neighboring atoms, which allows the size of AOs to depend on the molecular environment, despite the fact that a minimal basis set is used; (4) the one-center integrals of free atoms are derived from scalar relativistic multireference equation-of-motion coupled cluster calculations instead of empirical fitting, drastically reducing the number of necessary empirical parameters; (5) the (AA|AB) and (AB|AB)-type two-center integrals are explicitly included, going beyond the neglect of differential diatomic overlap approximation; and (6) the integrals depend on the atomic charges, effectively mimicking the "breathing" of AOs when the atomic charge varies. For this preliminary report, the model has been parameterized for the elements H-Ne, giving only 8 empirical global parameters. Preliminary results on the ionization potentials, electron affinities, and excitation energies of atoms and diatomic molecules, as well as the equilibrium geometries, vibrational frequencies dipole moments, and bond dissociation energies of diatomic molecules, show that the accuracy of NOTCH rivals or exceeds those of popular semiempirical methods (including PM3, PM7, OM2, OM3, GFN-xTB, and GFN2-xTB) as well as the cost-effective ab initio method Hartree-Fock-3c.

Accelerating the density-functional tight-binding method using graphical processing units

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility with 1-6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1-2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2-, and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.

Reparameterization of the chemical-potential equalization model with DFTB3: A practical balance between accuracy and transferability

To improve the performance of the third-order density-functional tight-binding method (DFTB3) for non-covalent interactions involving organic and biological molecules, a chemical-potential equalization (CPE) approach was introduced [J. Phys. Chem. A, 116, 9131 (2012)] and parameterized for the H, C, N, O, and S chemical elements [J. Chem. Phys., 143, 084123 (2015)]. Based largely on equilibrium structures, the parameterized DFTB3/CPE models were shown to exhibit improvements in molecular polarizabilities and intermolecular interactions. With more extensive analyses, however, we observe here that the available DFTB3/CPE models have two critical limitations: (1) they lead to sharply varying potential energy surfaces, thus causing numerical instability in molecular dynamics (MD) simulations, and (2) they lead to spurious interactions at short distances for some dimer complexes. These shortcomings are attributed to the employed screening functions and the overfitting of CPE parameters. In this work, we introduce a new strategy to simplify the parameterization procedure and significantly reduce free parameters down to four global (i.e., independent of element type) ones. With this strategy, two new models, DFTB3/CPE(r) and DFTB3/CPE(r†) are parameterized. The new models lead to smooth potential energy surfaces, stable MD simulations, and alleviate the spurious interactions at short distances, thus representing consistent improvements for both neutral and ionic hydrogen bonds.

How to Stabilize Carbenes in Enzyme Active Sites without Metal Ions

Carbenes are highly reactive compounds with unique value to synthetic chemistry. However, a small number of natural enzymes have been shown to utilize carbene chemistry, and artificial enzymes engineered with directed evolution required transition metal ions to stabilize the carbene intermediates. To facilitate the design of broader classes of enzymes that can take advantage of the rich carbene chemistry, it is thus important to better understand how to stabilize carbene species in enzyme active sites without metal ions. Motivated by our recent studies of the anaerobic ergothioneine biosynthesis enzyme EanB, we examine carbene-protein interaction with both cluster models and QM/MM simulations. The cluster calculations find that an N-heterocyclic carbene interacts strongly with polar and positively charged protein motifs. In particular, the interaction between a guanidinium group and carbene is as strong as ∼30 kcal/mol, making arginine a great choice for the preferential stabilization of carbenes. We also compare the WT EanB and its mutant in which the key tyrosine was replaced by a non-natural analogue (F2Tyr) using DFTB3/MM simulations. The calculations suggest that the carbene intermediate in the F2Tyr mutant is more stable than that in the WT enzyme by ∼3.5 kcal/mol, due to active site rearrangements that enable a nearby arginine to better stabilize the carbene in the mutant. Overall, the current work lays the foundation for the pursuit of enzyme designs that can take advantage of the unique chemistry offered by carbenes without the requirement of metal ions.

Parametrization of the PM7 Semiempirical Quantum Mechanical Method for Silver Nanoclusters

Semiempirical quantum mechanical methods (SEQMs) are widely used in computational chemistry because of their low computational cost, but their accuracy depends on the quality of the parameters. The neglect of diatomic differential overlap method PM7 is among the few SEQMs that contain parameters for Ag, but the experimental reference data was insufficient to obtain reliable parameters in the original parametrization. In this work, we reparametrize the PM7 parameters for Ag to accurately reproduce the ground-state potential energy surfaces of Ag clusters. Since little experimental data is available, we use reference data obtained from the ab initio method CCSD(T). The resulting parameters significantly reduce the errors in binding energies, energies required to displace clusters along their normal modes, and relative energies of isomers compared to the default PM7 Ag parameters.

ULYSSES: An Efficient and Easy to Use Semiempirical Library for C+

We introduce ULYSSES, a user-friendly and robust C++ library for semiempirical quantum chemical calculations. In its current version, ULYSSES is equipped with a large set of different semiempirical models, most of which are based on the Neglect of Diatomic Differential Overlap (NDDO) approximation. Empirical corrections for dispersion and hydrogen bonding are available for most methods, so that higher quality is achieved in the calculation of energies of nonbonded complexes. The library is furthermore equipped with geometry optimization, as well as modules for calculating molecular properties of general interest. Ideal gas thermodynamics is available and allows single structure as well as conformer (multistructure) averaged properties to be calculated. We offer the possibility to use several vibrational partition functions according to the nature of interactions being studied: for covalent systems, the traditional harmonic oscillator approximation is available; for nonbonded complexes, we systematically extended the partition function proposed by Grimme for all thermodynamic functions. The library is also capable of running Born-Oppenheimer molecular dynamics.

A general tight-binding based energy decomposition analysis scheme for intermolecular interactions in large molecules

In this work, a general tight-binding based energy decomposition analysis (EDA) scheme for intermolecular interactions is proposed. Different from the earlier version [Xu et al., J. Chem. Phys. 154, 194106 (2021)], the current tight-binding based density functional theory (DFTB)-EDA is capable of performing interaction analysis with all the self-consistent charge (SCC) type DFTB methods, including SCC-DFTB2/3 and GFN1/2-xTB, despite their different formulas and parameterization schemes. In DFTB-EDA, the total interaction energy is divided into frozen, polarization, and dispersion terms. The performance of DFTB-EDA with SCC-DFTB2/3 and GFN1/2-xTB for various interaction systems is discussed and assessed.

Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction

Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol-1 and substantially better than SQM methods without ML correction (5.71 kcal mol-1). Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved via the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.

Factors That Determine the Variation of Equilibrium and Kinetic Properties of QM/MM Enzyme Simulations: QM Region, Conformation, and Boundary Condition

To analyze the impact of various technical details on the results of quantum mechanical (QM)/molecular mechanical (MM) enzyme simulations, including the QM region size, catechol-O-methyltransferase (COMT) is studied as a model system using an approximate QM/MM method (DFTB3/CHARMM). The results show that key equilibrium and kinetic properties for methyl transfer in COMT exhibit limited variations with respect to the size of the QM region, which ranges from ∼100 to ∼500 atoms in this study. With extensive sampling, local and global structural characteristics of the enzyme are largely conserved across the studied QM regions, while the nature of the transition state (e.g., secondary kinetic isotope effect) and reaction exergonicity are largely maintained. Deviations in the free energy profile with different QM region sizes are similar in magnitude to those observed with changes in other simulation protocols, such as different initial enzyme conformations and boundary conditions. Electronic structural properties, such as the covariance matrix of residual charge fluctuations, appear to exhibit rather long-range correlations, especially when the peptide backbone is included in the QM region; this observation holds when a range-separated DFT approach is used as the QM region, suggesting that delocalization error is unlikely the origin. Overall, the analyses suggest that multiple simulation details determine the results of QM/MM enzyme simulations with comparable contributions.

Free Energy Decomposition Analysis Based on the Fragment Molecular Orbital Method

A decomposition of the free energy is developed in the many-body expansion framework of the fragment molecular orbital (FMO) method combined with umbrella sampling molecular dynamics (MD). In FMO/MD simulations, performed with density-functional tight-binding and periodic boundary conditions, all atoms are treated quantum mechanically. The free energy is computed and decomposed for a series of S_N2 Menshutkin reactions in water. The barrier lowering by the solvent is attributed to the competition between the solvent polarization and the solute-solvent interactions including charge transfer.

A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors

Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.

Light-assisted coupling of phenols with CO2 to 2-hydroxybenzaldehydes catalyzed by a g-C3N4/NH2-MIL-101(Fe) composite

The present work described a novel photocatalytic approach for the synthesis of 2-hydroxybenzaldehydes from the coupling of phenols and CO2 in the presence of a base using a graphitic carbon nitride/NH2-MIL-101(Fe) composite under mild conditions.

Resolving Chemical Dynamics in Biological Energy Conversion: Long-Range Proton-Coupled Electron Transfer in Respiratory Complex I

Biological energy conversion is catalyzed by membrane-bound proteins that transduce chemical or light energy into energy forms that power endergonic processes in the cell. At a molecular level, these catalytic processes involve elementary electron-, proton-, charge-, and energy-transfer reactions that take place in the intricate molecular machineries of cell respiration and photosynthesis. Recent developments in structural biology, particularly cryo-electron microscopy (cryoEM), have resolved the molecular architecture of several energy transducing proteins, but detailed mechanistic principles of their charge transfer reactions still remain poorly understood and a major challenge for modern biochemical research. To this end, multiscale molecular simulations provide a powerful approach to probe mechanistic principles on a broad range of time scales (femtoseconds to milliseconds) and spatial resolutions (10¹–10⁶ atoms), although technical challenges also require balancing between the computational accuracy, cost, and approximations introduced within the model. Here we discuss how the combination of atomistic (aMD) and hybrid quantum/classical molecular dynamics (QM/MM MD) simulations with free energy (FE) sampling methods can be used to probe mechanistic principles of enzymes responsible for biological energy conversion. We present mechanistic explorations of long-range proton-coupled electron transfer (PCET) dynamics in the highly intricate respiratory chain enzyme Complex I, which functions as a redox-driven proton pump in bacterial and mitochondrial respiratory chains by catalyzing a 300 Å fully reversible PCET process. This process is initiated by a hydride (H^–) transfer between NADH and FMN, followed by long-range (>100 Å) electron transfer along a wire of 8 FeS centers leading to a quinone biding site. The reduction of the quinone to quinol initiates dissociation of the latter to a second membrane-bound binding site, and triggers proton pumping across the membrane domain of complex I, in subunits up to 200 Å away from the active site. Our simulations across different size and time scales suggest that transient charge transfer reactions lead to changes in the internal hydration state of key regions, local electric fields, and the conformation of conserved ion pairs, which in turn modulate the dynamics of functional steps along the reaction cycle. Similar functional principles, which operate on much shorter length scales, are also found in some unrelated proteins, suggesting that enzymes may employ conserved principles in the catalysis of biological energy transduction processes.

Investigating the Accuracy of Water Models through the Van Hove Correlation Function

We present molecular-simulation-based calculations of the Van Hove correlation function (VHF) of water using multiple modeling approaches: classical molecular dynamics with simple three-site nonpolarizable models, with a polarizable model, and with a reactive force field; density functional tight-binding molecular dynamics; and ab initio molecular dynamics. Due to the many orders of magnitude difference in the computational cost of these approaches, we investigate how small and short the simulations can be while still yielding sufficiently accurate and interpretable results for the VHF. We investigate the accuracy of the different models by comparing them to recently published inelastic X-ray scattering measurements of the VHF. We find that all of the models exhibit qualitative agreement with the experiments, and in some models and for some properties, the agreement is quantitative. This work lays the foundation for future simulation approaches to calculating the VHF for aqueous solutions in bulk and under nanoconfinement.

Quantum-Mechanical Structure Optimization of Protein Crystals and Analysis of Interactions in Periodic Systems

A fast quantum-mechanical approach, density-functional tight-binding combined with the fragment molecular orbital method and periodic boundary conditions, is used to optimize atomic coordinates and cell parameters for a set of protein crystals: 1ETL, 5OQZ, 3Q8J, 1CBN, and 2VB1. Good agreement between experimental and calculated structures is obtained for both atomic coordinates and cell parameters. Sterical clashes present in the experimental structures are corrected by simulations. The partition analysis is extended to treat periodic boundary conditions and applied to analyze protein-solvent interactions in crystals.

Self-assembly of rylene-decorated guanine ribbons on graphene surface for optoelectronic applications: a theoretical study

We are witnessing a change of paradigm from the conventional top-down to the bottom-up fabrication of nanodevices and particularly optoelectronic devices. A promising example of the bottom-up approach is self-assembling of molecules into layers with predictable and reproducible structural, electronic and optical properties. Nucleobases possess extraordinary ability to self-assembly into one-, two-, and three-dimensional structures. Optical properties of nucleotides are not suitable for wider application to optoelectronics and photovoltaics due to their large optical band gap, which is in contrast to rylene-based dyes that have been intensively investigated in organic optoelectronics. However, these lack the self-assembly capability of nucleobases. Combinations of covalently decorated guanine molecules with rylene type chromophores present 'the best of the both worlds'. Due to the large size of such compounds and its flexible nature their self-assemblies have not been fully understood yet. Here, we use a theoretical approach to study the structural, energetic and optical properties of rylene-based dye decorated guanine (GPDI), as self-assembled on a graphene sheet. Particularly we utilize the density-functional based tight-binding method to study atomic structure of these systems including the potential energy surface of GPDI and stability and organization of single- and multilayered GPDIs on graphene sheet. Using density-functional theory (DFT) we employ the energy decomposition analysis to gain a deeper insight into the contributions of different moieties to stability of GPDI films. Using time dependent DFT we analyze optical properties of these systems. We find that atomically thin films consisting of only a few molecular layers with large surface areas are more favorable than isolated thick islands. Our study of excited states indicates existence of charge separated states similar to ones found in the well-studied hydrogen bonded organic frameworks. The self-assembly characterized with a large homogeneous coverage and long-living charge-separated states provide the great potential for optoelectronic applications.

Understanding intermolecular interactions of large systems in ground state and excited state by using density functional based tight binding methods

A novel energy decomposition analysis scheme, named DFTB-EDA, is proposed based on the density functional based tight-binding method (DFTB/TD-DFTB), which is a semi-empirical quantum mechanical method based on Kohn-Sham-DFT for large-scale calculations. In DFTB-EDA, the total interaction energy is divided into three terms: frozen density, polarization, and dispersion. Owing to the small cost of DFTB/TD-DFTB, DFTB-EDA is capable of analyzing intermolecular interactions in large molecular systems containing several thousand atoms with high computational efficiency. It can be used not only for ground states but also for excited states. Test calculations, involving the S66 and L7 databases, several large molecules, and non-covalent bonding complexes in their lowest excited states, demonstrate the efficiency, usefulness, and capabilities of DFTB-EDA. Finally, the limits of DFTB-EDA are pointed out.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Extended Hückel Semi-Empirical Approach as an Efficient Method for Structural Defects Analysis in 4H-SiC

This paper presents an efficient method to calculate the influence of structural defects on the energy levels and energy band-gap for the 4H-SiC semiconductor. The semi-empirical extended Hückel method was applied to both ideal 4H-SiC crystal and different structures with defects like vacancies, stacking faults, and threading edge dislocations. The Synopsys QuatumATK package was used to perform the simulations. The results are in good agreement with standard density functional theory (DFT) methods and the computing time is much lower. This means that a structure with ca. 1000 atoms could be easily modeled on typical computing servers within a few hours of computing time, enabling fast and accurate simulation of non-ideal atomic structures.

Curvature Constrained Splines for DFTB Repulsive Potential Parametrization

The Curvature Constrained Splines (CCS) methodology has been used for fitting repulsive potentials to be used in SCC-DFTB calculations. The benefit of using CCS is that the actual fitting of the repulsive potential is performed through quadratic programming on a convex objective function. This guarantees a unique (for strictly convex) and optimum two-body repulsive potential in a single shot, thereby making the parametrization process robust, and with minimal human effort. Furthermore, the constraints in CCS give the user control to tune the shape of the repulsive potential based on prior knowledge about the system in question. Herein, we developed the method further with new constraints and the capability to handle sparse data. We used the method to generate accurate repulsive potentials for bulk Si polymorphs and demonstrate that for a given Slater-Koster table, which reproduces the experimental band structure for bulk Si in its ground state, we are unable to find one single two-body repulsive potential that can accurately describe the various bulk polymorphs of silicon in our training set. We further demonstrate that to increase transferability, the repulsive potential needs to be adjusted to account for changes in the chemical environment, here expressed in the form of a coordination number. By training a near-sighted Atomistic Neural Network potential, which includes many-body effects but still essentially within the first-neighbor shell, we can obtain full transferability for SCC-DFTB in terms of describing the energetics of different Si polymorphs.

Computational Methods for Biochemical Simulations Implemented in GAMESS

Computational methods for modeling biochemical processes implemented in GAMESS package are reviewed; in particular, quantum mechanics combined with molecular mechanics (QM/MM), semi-empirical, and fragmentation approaches. A detailed summary of capabilities is provided for the QM/MM implementation in QuanPol program and the fragment molecular orbital (FMO) method. Molecular modeling and visualization packages useful for biochemical simulations with GAMESS are described. GAMESS capabilities with corresponding references are tabulated for reader's convenience.

Improvement of d-d interactions in density functional tight binding for transition metal ions with a ligand field model: assessment of a DFTB3+U model on nickel coordination compounds

To improve the description of interactions among the localized d, f electrons in transition metals, we have introduced a ligand-field motivated contribution into the Density Functional Tight Binding (DFTB) model. Referred to as DFTB3+U, the approach treats the d, f electron repulsions with rotationally invariant orbital-orbital interactions and a Hartree-Fock model; this represents a major conceptual improvement over the original DFTB3 approach, which treats the d, f-shell interactions in a highly averaged fashion without orbital level of description. The DFTB3+U approach is tested using a series of nickel compounds that feature Ni(ii) and Ni(iii) oxidation states. By using parameters developed with the original DFTB3 Hamiltonian and empirical +U parameters (F0/2/4 Slater integrals), we observe that the DFTB3+U model indeed provides substantial improvements over the original DFTB3 model for a number of properties of the nickel compounds, including the population and spin polarization of the d-shell, nature of the frontier orbitals, ligand field splitting and the energy different between low and high spin states at OPBE optimized structures. This proof-of-concept study suggests that with self-consistent parameterization of the electronic and +U parameters, the DFTB3+U model can develop into a promising model that can be used to efficiently study reactive events involving transition metals ion condensed phase systems. The methodology can be integrated with other approximate QM methods as well, such as the extended tight binding (xTB) approach.

In silico approach: biological prediction of nordentatin derivatives as anticancer agent inhibitors in the cAMP pathway

A combination of computational techniques has been carried out to predict the binding of nordentatin derivatives based on pyranocoumarin semi-synthesis with the target protein from the expression of the PDE4B gene. The inhibition of the cAMP pathway is the main target of anti-cancer drugs, which is responsible for uncontrolled cell division in cancer. Modeling was done using a combination of semi-empirical methods and the density functional theory (PM3-DFT/6-31G*/B3LYP) to obtain the optimal structure of a small ligand that could be modeled. Studies on the interaction of the ligands and amino acid residues on protein targets were carried out using a combination of molecular docking and molecular dynamic simulation. Molecular docking based on functional grid scores showed a very good native ligand pose with an RMSD of 0.93 Å in determining the initial coordinates of the ligand-receptor interactions. Furthermore, the amino acid residues responsible for interaction through H-bonds were Tyr103, His104, His177, Met217, and Gln313. The binding free energy (kcal mol-1) results of the candidates were PS-1 (-36.84 ± 0.31), PS-2 (-35.34 ± 0.28), PS-3 (-26.65 ± 0.30), PS-5 (-42.66 ± 0.26), PS-7 (-35.33 ± 0.23), and PS-9 (-32.57 ± 0.20), which are smaller than that of the native ligand Z72 (-24.20 ± 0.19), and thus these have good potential as drugs that can inhibit the cAMP pathway. These results provide theoretical information for the efficient inhibition of the cAMP pathway in the future.

Density-functional tight-binding for phosphine-stabilized nanoscale gold clusters

We report a parameterization of the second-order density-functional tight-binding (DFTB2) method for the quantum chemical simulation of phosphine-ligated nanoscale gold clusters, metalloids, and gold surfaces. Our parameterization extends the previously released DFTB2 "auorg" parameter set by connecting it to the electronic parameter of phosphorus in the "mio" parameter set. Although this connection could technically simply be accomplished by creating only the required additional Au-P repulsive potential, we found that the Au 6p and P 3d virtual atomic orbital energy levels exert a strong influence on the overall performance of the combined parameter set. Our optimized parameters are validated against density functional theory (DFT) geometries, ligand binding and cluster isomerization energies, ligand dissociation potential energy curves, and molecular orbital energies for relevant phosphine-ligated Au n clusters (n = 2-70), as well as selected experimental X-ray structures from the Cambridge Structural Database. In addition, we validate DFTB simulated far-IR spectra for several phosphine- and thiolate-ligated gold clusters against experimental and DFT spectra. The transferability of the parameter set is evaluated using DFT and DFTB potential energy surfaces resulting from the chemisorption of a PH3 molecule on the gold (111) surface. To demonstrate the potential of the DFTB method for quantum chemical simulations of metalloid gold clusters that are challenging for traditional DFT calculations, we report the predicted molecular geometry, electronic structure, ligand binding energy, and IR spectrum of Au108S24(PPh3)16.

Electronic structure benchmark calculations of CO2 fixing elementary chemical steps in RuBisCO using the projector-based embedding approach

Ribulose 1,5-bisphosphate carboxylase-oxygenase (RuBisCO) is the main enzyme involved in atmospheric carbon dioxide (CO₂ ) fixation in the biosphere. This enzyme catalyzes a set of five chemical steps that take place in the same active-site within magnesium (II) coordination sphere. Here, a set of electronic structure benchmark calculations have been carried out on a reaction path proposed by Gready et al. by means of the projector-based embedding approach. Activation and reaction energies for all main steps catalyzed by RuBisCO have been calculated at the MP2, SCS-MP2, CCSD, and CCSD(T)/aug-cc-pVDZ and cc-pVDZ levels of theory. The treatment of the magnesium cation with post-HF methods is explored to determine the nature of its involvement in the mechanism. With the high-level ab initio values as a reference, we tested the performance of a set of density functional theory (DFT) exchange-correlation (xc) functionals in reproducing the reaction energetics of RuBisCO carboxylase activity on a set of model fragments. Different DFT xc-functionals show large variation in activation and reaction energies. Activation and reaction energies computed at the B3LYP level are close to the reference SCS-MP2 results for carboxylation, hydration and protonation reactions. However, for the carbon-carbon bond dissociation reaction, B3LYP and other functionals give results that differ significantly from the ab initio reference values. The results show the applicability of the projector-based embedding approach to metalloenzymes. This technique removes the uncertainty associated with the selection of different DFT xc-functionals and so can overcome some of inherent limitations of DFT calculations, complementing, and potentially adding to modeling of enzyme reaction mechanisms with DFT methods.

Potential of Mean Force Calculations for an SN2 Fluorination Reaction in Five Different Imidazolium Ionic Liquid Solvents Using Quantum Mechanics/Molecular Mechanics Molecular Dynamics Simulations

The use of ionic liquids (ILs) as both catalysts and solvents in a wide range of chemical reactions has received considerable attention over the last few years due to their positive effects in enhancing reaction rates and selectivities. In this work, hybrid quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations were carried out in conjunction with umbrella-sampling techniques to study the bimolecular nucleophilic substitution (S_N2) fluorination reaction between propyl-mesylate and potassium fluoride using five ILs as solvents, specifically, 1-butyl-3-methylimidazolium mesylate ([C₄mim][OMs]), 1-butyl-3-methylimidazolium tetrafluoroborate ([C₄mim][BF₄]), 1-butyl-3-methylimidazolium trifluoroacetate ([C₄mim][CF₃COO]), 1-butyl-3-methylimidazolium bromide ([C₄mim][Br]), and 1-butyl-3-methylimidazolium chloride ([C₄mim][Cl]) at 373.15 K. The QM region (reactive part) in all QM/MM systems was simulated using the Parametric Method 6 (PM6) semiempirical methods, and for the MM region (IL solvent), classical force fields (FF) were employed, with the FF developed within the group. The calculated activation free energy barriers (ΔG^‡) for the S_N2 reaction in the presence of [C₄mim][OMs] and [C₄mim][BF₄] ILs were in agreement with the experimental values reported in the literature. On the other hand, only predicted values were obtained for the activation energies for the [C₄mim][CF₃COO], [C₄mim][Br], and [C₄mim][Cl] ILs. These activation energies indicated that the S_N2 reaction would be more facile to proceed using the [C₄mim][Cl] and [C₄mim][OMs] ILs, in contrast with the use of [C₄mim][Br] IL, which presented the highest activation energy. Energy-pair distributions, radial distribution functions, and noncovalent interactions (NCI) were also calculated to elucidate the molecular interactions between the reactive QM region and the solvents or reaction media. From these calculations, it was found that not only the reactivity can be enhanced by selecting a specific anion to increase the K-F separation but also the cation plays a relevant role, producing a synergetic effect by forming hydrogen bonds with the fluorine atom from KF and with the oxygen atoms within the mesylate leaving group. Three interactions are significant for the IL catalytic behavior, F_QM-HX, K_QM-anion, and O_QM-HX interactions, where the F_QM and K_QM labels correspond to fluorine and potassium atoms from the KF salt, O_QM corresponds to oxygen atoms within the mesylate leaving group (reactant), and HX refers to hydrogen atoms within the IL cation. The NCI analysis revealed that K_QM-anion interactions are of weak type, indicating the importance of hydrogen bond interactions from the cation such as F_QM-HX and O_QM-HX for the catalytic behavior of ILs.

Recent developments in the general atomic and molecular electronic structure system

A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized.

Quantum Chemical and QM/MM Models in Biochemistry

Quantum chemical (QC) calculations provide a basis for deriving a microscopic understanding of enzymes and photobiological systems. Here we describe how QC models can be used to explore the electronic structure, dynamics, and energetics of biomolecules. We introduce the hybrid quantum mechanics/classical mechanics (QM/MM) approach, where a quantum mechanically described system of interest is embedded in a classically described force field representation of the biochemical surroundings. We also discuss the QM cluster model approach, as well as embedding theories, that provide complementary methodologies to model quantum mechanical effects in biomolecules. The chapter also provides some practical guides for building quantum biochemical models using the quinone reduction catalysis in respiratory complex I and a model reaction in solution as examples.

Density-functional tight-binding: basic concepts and applications to molecules and clusters

The scope of this article is to present an overview of the Density Functional based Tight Binding (DFTB) method and its applications. The paper introduces the basics of DFTB and its standard formulation up to second order. It also addresses methodological developments such as third order expansion, inclusion of non-covalent interactions, schemes to solve the self-interaction error, implementation of long-range short-range separation, treatment of excited states via the time-dependent DFTB scheme, inclusion of DFTB in hybrid high-level/low level schemes (DFT/DFTB or DFTB/MM), fragment decomposition of large systems, large scale potential energy landscape exploration with molecular dynamics in ground or excited states, non-adiabatic dynamics. A number of applications are reviewed, focusing on -(i)- the variety of systems that have been studied such as small molecules, large molecules and biomolecules, bare orfunctionalized clusters, supported or embedded systems, and -(ii)- properties and processes, such as vibrational spectroscopy, collisions, fragmentation, thermodynamics or non-adiabatic dynamics. Finally outlines and perspectives are given.

Quantum chemical molecular dynamics and metadynamics simulation of aluminium binding to amyloid-β and related peptides

We report semi-empirical tight-binding simulations of the interaction between Al(III) and biologically relevant peptides. The GFN2-XTB method is shown to accurately reproduce previously reported and density functional theory (DFT)-calculated geometries of model systems. Molecular dynamics simulations based on this method are able to sample peptide flexibility over timescales of up to nanoseconds, but these timescales are insufficient to explore potential changes in metal-peptide binding modes. To achieve this, metadynamics simulations using root mean square deviation as a collective variable were employed. With suitably chosen biasing potentials, these are able to efficiently explore diverse coordination modes, for instance, through Glu and/or Asp residues in a model peptide. Using these methods, we find that Al(III) binding to the N-terminal sequence of amyloid-β is highly fluxional, with all acidic sidechains and several backbone oxygens participating in coordination. We also show that such simulations could provide a means to predict a priori possible binding modes as a precursor to longer, atomistic simulations.

Analysis of Density Functional Tight Binding with Natural Bonding Orbitals

The description of chemical bonding by the density functional tight binding (DFTB) model is analyzed using natural bonding orbitals (NBOs) and compared to results from density functional theory (B3LYP/aug-cc-pVTZ) calculations. Several molecular systems have been chosen to represent fairly diverse bonding scenarios that include standard covalent bonds, hypervalent interactions, multicenter bonds, metal-ligand interactions (with and without the pseudo-Jahn-Teller effect), and through-space donor-acceptor interactions. Overall, the results suggest that DFTB3/3OB provides physically sound descriptions for the different bonding scenarios analyzed here, as reflected by the general agreement between DFTB3 and B3LYP NBO properties, such as the nature of the NBOs, the magnitudes of natural charges and bond orders, and the dominant donor-acceptor interactions. The degree of ligand-to-metal charge transfer and the ionic nature of pentavalent phosphate are overestimated, likely reflecting the minimal-basis nature of DFTB3/3OB. Moreover, certain orbital interactions, such as geminal interactions, are observed to be grossly overestimated by DFTB3 for hypervalent phosphate and several transition metal compounds that involve copper and nickel. The study indicates that results from NBO analysis can be instructive for identifying electronic structure descriptions at the approximate quantum-mechanical level that require improvement and thus for guiding the systematic improvement of these methods.

The Fragment Molecular Orbital Method Based on Long-Range Corrected Density-Functional Tight-Binding

The presently available linear scaling approaches to density-functional tight-binding (DFTB) based on the fragment molecular orbital (FMO) method are severely impacted by the problem of artificial charge transfer due to the self-interaction error (SIE), which hampers the simulation of zwitterionic systems such as biopolymers or ionic liquids. Here we report an extension of FMO-DFTB where we included a long-range corrected (LC) functional designed to mitigate the DFTB SIE, called the FMO-LC-DFTB method, resulting in a robust method which succeeds in simulating zwitterionic systems. Both energy and analytic gradient are developed for the gas phase and the polarizable continuum model of solvation. The scaling of FMO-LC-DFTB with system size N is shown to be almost linear, O( N^1.13-1.28), and its numerical accuracy is established for a variety of representative systems including neutral and charged polypeptides. It is shown that pair interaction energies between fragments for two mini-proteins are in excellent agreement with results from long-range corrected density functional theory. The new method was employed in long time scale (1 ns) molecular dynamics simulations of the tryptophan cage protein (PDB: 1L2Y ) in the gas phase for four different protonation states and in stochastic global minimum structure searches for 1-ethyl-3-methylimidazolium nitrate ionic liquid clusters containing up to 2300 atoms.