Quantum Mechanical Simulations Research Articles

Origins of Periselectivity and Regioselectivity in Ambimodal Tripericyclic [8+6]/[6+4]/[4+2] Intramolecular Cycloadditions of a Heptafulvenyl-Fulvene.

Quantum mechanical calculations and molecular dynamics simulations have elucidated the reaction mechanism for intramolecular cycloadditions of a heptafulvenyl-fulvene tethered by a trimethylene chain. Prior experiments by Liu and Houk reported the formation of only an endo-[8+6] cycloadduct at 185 °C. Liu et al. later reported an exo-[4+2] Diels-Alder cycloadduct as the major product at 140 °C (Tetrahedron, 1999, 55, 9171). Cycloadditions involve Diels-Alder and an ambimodal intramolecular tripericyclic [8+6]/[6+4]/[4+2] cycloaddition. The mechanistic details explain the experimental reports of temperature dependence on the periselectivity of intramolecular cycloadditions. Additional calculations with multireference-based methods CASSCF and NEVPT2 highlight the artifacts of DFT methods and single-reference wavefunction-based CCSD(T) in the description of complete potential energy surface involving various cycloadditions of the heptafulvenyl-fulvene.

Construct exchange-correlation functional via machine learning.

Density functional theory has been widely used in quantum mechanical simulations, but the search for a universal exchange-correlation (XC) functional has been elusive. Over the last two decades, machine-learning techniques have been introduced to approximate the XC functional or potential, and recent advances in deep learning have renewed interest in this approach. In this article, we review early efforts to use machine learning to approximate the XC functional, with a focus on the challenge of transferring knowledge from small molecules to larger systems. Recently, the transferability problem has been addressed through the use of quasi-local density-based descriptors, which are rooted in the holographic electron density theorem. We also discuss recent developments using deep-learning techniques that target high-level abinitio molecular energy and electron density for training. These efforts can be unified under a general framework, which will also be discussed from this perspective. Additionally, we explore the use of auxiliary machine-learning models for van der Waals interactions.

The Role of Interface Trap States in MoS2-FET Performance: A Full Quantum Mechanical Simulation Study

The Role of Interface Trap States in MoS₂-FET Performance: A Full Quantum Mechanical Simulation Study

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.

Uncoupling system and environment simulation cells for fast-scaling modeling of complex continuum embeddings.

Continuum solvation models are becoming increasingly relevant in condensed matter simulations, allowing to characterize materials interfaces in the presence of wet electrified environments at a reduced computational cost with respect to all atomistic simulations. However, some challenges with the implementation of these models in plane-wave simulation packages still persists, especially when the goal is to simulate complex and heterogeneous environments. Among these challenges is the computational cost associated with large heterogeneous environments, which in plane-wave simulations has a direct effect on the basis-set size and, as a result, on the cost of the electronic structure calculation. Moreover, the use of periodic simulation cells is not well-suited for modeling systems embedded in semi-infinite media, which is often the case in continuum solvation models. To address these challenges, we present the implementation of a double-cell formalism, in which the simulation cell used for the continuum environment is uncoupled from the one used for the electronic-structure simulation of the quantum-mechanical system. This allows for a larger simulation cell to be used for the environment, without significantly increasing computational time. In this work, we show how the double-cell formalism can be used as an effective periodic boundary conditions correction scheme for nonperiodic and partially periodic systems. The accuracy of the double-cell formalism is tested using representative examples with different dimensionalities, both in vacuum and in a homogeneous continuum dielectric environment. Fast convergence and good speedups are observed for all the simulation setups, provided the quantum-mechanical simulation cell is chosen to completely fit the electronic density of the system.

Wet Chemical Treatment and Mg Doping of p‐InP Surfaces for Ohmic Low‐Resistive Metal Contacts

Manufacturing a low‐resistive Ohmic metal contact on p‐type InP crystals for various applications is a challenge because of the Fermi‐level pinning via surface defects and the diffusion of p‐type doping atoms in InP. Development of wet‐chemistry treatments and nanoscale control of p‐doping for InP surfaces is crucial for decreasing the device resistivity losses and durability problems. Herein, a proper combination of HCl‐based solution immersion, which directly provides an unusual wet chemical‐induced InP(100)c(2 × 2) atomic structure, and low‐temperature Mg‐surface doping of the cleaned InP before Ni‐film deposition is demonstrated to decrease the contact resistivity of Ni/p‐InP by the factor of 10 approximately as compared to the lowest reference value without Mg. Deposition of the Mg intermediate layer on p‐InP and postheating of Mg/p‐InP at 350 °C, both performed in ultrahigh‐vacuum (UHV) chamber, lead to intermixing of Mg and InP elements according to X‐ray photoelectron spectroscopy. Introducing a small oxygen gas background (O2 ≈ 10−6 mbar) in UHV chamber during the postheating of Mg/p‐InP enhances the indium outdiffusion and provides the lowest contact resistivity. Quantum mechanical simulations indicate that the presence of InP native oxide or/and metal indium alloy at the interface increases In diffusion.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations.

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Surface-induced vibrational energy redistribution in methane/surface scattering depends on catalytic activity.

Recent state-to-state experiments of methane scattering from Ni(111) and graphene-covered Ni(111) combined with quantum mechanical simulations suggest an intriguing correlation between the surface-induced vibrational energy redistribution (SIVR) during the molecule/surface scattering event and the catalytic activity for methane dissociation of the target surface (Werdecker, Phys. Rev. Res., 2020, 2, 043251). Herein, we report new quantum state and angle-resolved measurements for methane scattering from Ni(111) and Au(111) probing the extent of antisymmetric-to-symmetric conversion of methane stretching motion for two surfaces with different catalytic activities. Consistent with the expectations, the extent of SIVR occurring on the more catalytically active Ni(111) surface, as measured by the scattered population ratio, is found to be several times stronger than that on the more inert Au(111) surface. We also present additional insights on the rovibrational scattering dynamics contained in the angle- and state-resolved data. The results together highlight the power of state-resolved scattering measurements as a tool for investigating methane-surface interactions.

A Full Quantum Mechanical Approach Assessing the Chemical and Electromagnetic Effect in TERS.

Tip-enhanced Raman spectroscopy (TERS) is a valuable method for surface analysis with nanometer to angstrom-scale resolution; however, the accurate simulation of particular TERS signals remains a computational challenge. We approach this challenge by combining the two main contributors to plasmon-enhanced Raman spectroscopy and to the high resolution in TERS, in particular, the electromagnetic and the chemical effect, into one quantum mechanical simulation. The electromagnetic effect describes the sample's interaction with the strong, highly localized, and inhomogeneous electric fields associated with the plasmonic tip and is typically the thematic focus for most mechanistic studies. On the other hand, the chemical effect covers the different responses to the extremely close-range and highly position-sensitive chemical interaction between the apex tip atom(s) and the sample, and, as we could show in previous works, plays an often underestimated role. Starting from a (time-dependent) density functional theory description of the chemical model system, comprised of a tin(II) phthalocyanine sample molecule and a single silver atom as the tip, we introduce the electromagnetic effect through a series of static point charges that recreate the electric field in the vicinity of the plasmonic Ag nanoparticle. By scanning the tip over the molecule along a 3D grid, we can investigate the system's Raman response on each position for nonresonant and resonant illumination. Simulating both effects on their own already hints at the achievable signal enhancement and resolution, but the combination of both creates even stronger evidence that TERS is capable of resolving submolecular features.

Current-induced bond rupture in single-molecule junctions: Effects of multiple electronic states and vibrational modes.

Current-induced bond rupture is a fundamental process in nanoelectronic architectures, such as molecular junctions, and scanning tunneling microscopy measurements of molecules at surfaces. The understanding of the underlying mechanisms is important for the design of molecular junctions that are stable at higher bias voltages and is a prerequisite for further developments in the field of current-induced chemistry. In this work, we analyze the mechanisms of current-induced bond rupture employing a recently developed method, which combines the hierarchical equations of motion approach in twin space with the matrix product state formalism and allows accurate, fully quantum mechanical simulations of the complex bond rupture dynamics. Extending previous work [Ke et al. J. Chem. Phys. 154, 234702 (2021)], we consider specifically the effect of multiple electronic states and multiple vibrational modes. The results obtained for a series of models of increasing complexity show the importance of vibronic coupling between different electronic states of the charged molecule, which can enhance the dissociation rate at low bias voltages profoundly.

GUIDE: A GUI for automated quantum chemistry calculations.

The accuracy of quantum mechanics (QM) simulations depends heavily on the quality of initial input files. Despite the popularity of QM simulation packages, achieving precise results still heavily relies on the user's proficiency in preparing the QM simulation systems. In this work, we present an easy-to-use tool called GUIDE, a YASARA plugin to assist researchers in quantum chemistry workflow automation using ORCA and MOPAC simulation packages. GUIDE lets users compute complex QM calculation workflows via an automated graphical window system. It allows for a more integrated and streamlined research process, as researchers can easily access all the necessary tools within one software without switching between multiple programs. This tool can save time and increase efficiency in computational chemistry methods. GUIDE is written in Python and is freely available for download at https://github.com/YAMACS-SML/GUIDE. The plugin is released under a GPL-3.0 license and is supported on Windows and Linux.

Elucidating the Mystery of DNA-Templating Effects on a Silver Nanocluster.

This presentation considers the effects that DNA-templating has on the optical properties of a 16-atom silver cluster. To accomplish this, hybrid quantum mechanical and molecular mechanical simulations of a Ag16-DNA complex have been carried out and compared with pure time-dependent density functional theory calculations of two Ag16 clusters in vacuum. The presented results show that the templating DNA polymers both red-shift the one-photon absorption of the silver cluster and increase its intensity. This occurs through a change in cluster shape prompted by the structural constraints of the DNA ligands combined with silver-DNA interactions. The overall charge of the cluster also contributes to the observed optical response, as oxidation of the cluster results in a simultaneous blue-shift of the one-photon absorption and a decrease in intensity. Additionally, the changes in shape and environment also lead to a blue-shift and enhancement of the two-photon absorption.

Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions.

Having access to accurate electron densities in chemical systems, especially for dynamical systems involving chemical reactions, ion transport, and other charge transfer processes, is crucial for numerous applications in materials chemistry. Traditional methods for computationally predicting electron density data for such systems include quantum mechanical (QM) techniques, such as density functional theory. However, poor scaling of these QM methods restricts their use to relatively small system sizes and short dynamic time scales. To overcome this limitation, we have developed a deep neural network machine learning formalism, which we call deep charge density prediction (DeepCDP), for predicting charge densities by only using atomic positions for molecules and condensed phase (periodic) systems. Our method uses the weighted smooth overlap of atomic positions to fingerprint environments on a grid-point basis and map it to electron density data generated from QM simulations. We trained models for bulk systems of copper, LiF, and silicon; for a molecular system, water; and for two-dimensional charged and uncharged systems, hydroxyl-functionalized graphane, with and without an added proton. We showed that DeepCDP achieves prediction R2 values greater than 0.99 and mean squared error values on the order of 10-5e2 Å-6 for most systems. DeepCDP scales linearly with system size, is highly parallelizable, and is capable of accurately predicting the excess charge in protonated hydroxyl-functionalized graphane. We demonstrate how DeepCDP can be used to accurately track the location of charges (protons) by computing electron densities at a few selected grid points in the materials, thus significantly reducing the computational cost. We also show that our models can be transferable, allowing prediction of electron densities for systems on which it has not been trained but that contain a subset of atomic species on which it has been trained. Our approach can be used to develop models that span different chemical systems and train them for the study of large-scale charge transport and chemical reactions.

Catalytic Mechanism of Human T-Cell Leukemia Virus Type 1 Protease Investigated by Combined QM/MM Molecular Dynamics Simulations.

Combined quantum mechanical and molecular mechanical (QM/MM) molecular dynamics simulations were performed to investigate the catalytic mechanism of human T-cell leukemia virus type 1 (HTLV-1) protease, a retroviral aspartic protease that is a potential therapeutic target for curing HTLV-1-associated diseases. To elucidate the proteolytic cleavage mechanism, we determined the two-dimensional free energy surfaces of the HTLV-1 protease-catalyzed reactions through various possible pathways. The free energy simulations suggest that the catalytic reactions of the HTLV-1 protease occur in the following sequential steps: (1) a proton is transferred from the lytic water to Asp32', followed by the nucleophilic addition of the resulting hydroxyl to the carbonyl carbon of the scissile bond, forming a tetrahedral oxyanion intermediate, and (2) a proton is transferred from Asp32 to the peptide nitrogen of the scissile bond, leading to the spontaneous breakage of the scissile bond. The rate-limiting step of this catalytic process is the proton transfer from Asp32 to the peptide nitrogen of the scissile bond, with a free energy of activation of 21.1 kcal/mol. This free energy barrier is close to the experimentally determined free energy of activation (16.3 kcal/mol) calculated from the measured catalytic rate constant (kcat). This mechanistic study provides detailed dynamic and structural information that will facilitate the design of mechanism-based inhibitors for the treatment of HTLV-1-associated diseases.

Solvent Dynamics Are Critical to Understanding Carbon Dioxide Dissolution and Hydration in Water.

Simulations of carbon dioxide (CO2) in water may aid in understanding the impact of its accumulation in aquatic environments and help advance technologies for carbon capture and utilization (via, e.g., mineralization). Quantum mechanical (QM) simulations based on static molecular models with polarizable continuum solvation poorly reproduce the energetics of CO2 hydration to form carbonic acid in water, independent of the level of QM theory employed. Only with density-functional-theory-based molecular dynamics and rare-event sampling, followed by energy corrections based on embedded correlated wavefunction theory (in conjunction with density functional embedding theory), can a close agreement between theory and experiment be achieved. Such multilevel simulations can serve as benchmarks for simpler, less costly models, giving insight into potential errors of the latter. The strong influence of sampling/averaging over dynamical solvent configurations on the energetics stems from the difference in polarity of both the transition state and product (both polar) versus the reactant (nonpolar). When a solute undergoes a change in polarity during reaction, affecting its interaction with the solvent, careful assessment of the energetic contribution of the solvent response to this change is critical. We show that static models (without structural sampling) that incorporate three explicit water molecules can yield far superior results than models with more explicit water molecules because fewer water molecules yield less configurational artifacts. Static models intelligently incorporating both explicit (molecules directly participating in the reaction) and implicit solvation, along with a proper QM theory, e.g., CCSD(T) for closed-shell systems, can close the accuracy gap between static and dynamic models.

Coupling of conformation and CPD damage in nucleosomal DNA

UV-light can cause photodimerization and hence damages in DNA. Most frequent are cyclobutane pyrimidine dimer (CPD) damages, which predominantly form at TpT (thymine-thymine) steps. It is well known that CPD damage probability is different for single-stranded or double stranded DNA and depends on the sequence context. However, DNA deformation due to packing in nucleosomes can also influence CPD formation. Quantum mechanical calculations and Molecular Dynamics simulations indicate little CPD damage probability for DNA's equilibrium structure. We find that DNA needs to be deformed in a specific way to allow the HOMO → LUMO transition required for CPD damage formation. The simulation studies further show that the periodic CPD damage patterns measured in chromosomes and nucleosomes can be directly explained by the periodic deformation pattern of the DNA in the nucleosome complex. It supports previous findings on characteristic deformation patterns found in experimental nucleosome structures that relate to CPD damage formation. The result may have important implications for our understanding of UV-induced DNA mutations in human cancers.

Molecular mechanism of metamorphic alteration on traces of early life in banded iron formations

It is well-known that multi-stage metamorphism can result in the alteration of indigenous biological molecules, limiting our understanding of early life on Earth. However, the physiochemical mechanisms involved in these processes are still poorly understood. In this study, we present petrographic observations and micro- to nano-geochemical investigations on the carbonaceous matter (CM) in representative Neoarchean banded iron formations (BIFs) from North China, which have undergone significant alteration during lower amphibolite-facies prograde metamorphism, and subsequent retrograde alteration. The CM is in paragenetic equilibrium with prograde mineral phases, and is often associated with apatite that occurs in Fe-rich bands parallel to layering. This implies that the CM is most likely inherited from syn-depositional biomass, as confirmed by the nanoscale infrared spectroscopy, which shows the presence of CC, CH, and CN/NH bonds. Raman spectroscopic analyses reveal that the maximum metamorphic temperature of CM is 479 ± 50°C, which is consistent with the metamorphic peak conditions of the host BIFs from petrographic constraints (i.e., garnet-bearing amphibolite-face metamorphism). The BIFs possess average bulk δ13Corg values of −20.0 ± 0.9‰ (1σ) and δ13Ccarb values of −12.9 ± 1.8‰ (1σ), further indicating syngenetic biomass graphitization during prograde metamorphism. This thermal cracking process may have released gaseous hydrocarbons, as shown by secondary CH4 fluid inclusions in quartz. We further use quantum mechanical simulations to constrain dissociative tendencies for functional groups (CC, CH, CO, and CN) of original organic molecules to assess the stability of organic chemical bonds during prograde metamorphism (0–600°C, 0–15 kbar). The relatively high thermal durability of CH and the armoring effects of primary organic-phyllosilicate complexes may account for CH preservation in BIFs. Furthermore, the electron microscopy combined with elemental analysis reveals widespread nano-chlorite infiltration into CM during retrograde metamorphism (i.e., partial replacement of garnet by chlorite). The pervasive CM alteration is likely responsible for the absence of CO bonds, where nanopore-scale reactions might have played a key role. Altogether, we suggest that multi-stage metamorphic processes, involving mineral-organic reactions and nanoscale interface interactions, may have governed the preservation of ancient biosignatures in BIFs. Our findings highlight the importance of evaluating metamorphic effects when using molecular signals to reconstruct early life behaviors, and shed new light on the study of primordial microorganisms, particularly those found in iron-rich sediments on early Earth.

Deciphering Structural and Dynamical Properties of Hydrated Cobalt Porphyrins via Ab Initio Quantum Mechanical Charge Field Molecular Dynamics Simulation.

The present study successfully implemented the ab initio quantum mechanical charge field molecular dynamics (QMCF MD) formalism for the investigation of structural and dynamical properties of hydrated cobalt-porphyrin complexes. Considering the significance of cobalt ions in biological systems (for instance, vitamin B12), which reportedly incorporate cobalt ions in a d6, low spin, +3 state chelated in the corrin ring, an analog of porphyrin, the current study is focused on cobalt in the oxidation states +2 and +3 bound to the parent porphyrin lead structures embedded in an aqueous solution. These cobalt-porphyrin complexes were investigated in terms of their structural and dynamical properties at the quantum chemical level. The structural attributes of these hydrated complexes revealed the contrasting features of the water binding to these solutes, including a detailed evaluation of the associated dynamics. The study also yielded notable findings in regard to the respective electronic configurations vs coordination, which suggested that Co(II)-POR possesses a 5-fold square pyramidal coordination geometry in an aqueous solution containing the metal ion coordinating to four nitrogen atoms of the porphyrin ring and one axial water as the fifth ligand. On the other hand, high-spin Co(III)-POR was hypothesized to be more stable due to the smaller size-to-charge ratio of the cobalt ion, but the high-spin complex demonstrated unstable structural and dynamical behavior. However, the corresponding properties of the hydrated Co(III)LS-POR revealed a stable structure in an aqueous solution, thus suggesting the Co(III) ion to be in a low-spin state when bound to the porphyrin ring. Moreover, the structural and dynamical data were augmented by computing the free energy of water binding to the cobalt ions and the solvent-accessible surface area, which provide further information on thermochemical properties of the metal-water interaction and the hydrogen bonding potential of the porphyrin ring in these hydrated systems.

Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

Machine learning methods, and in particular neural-network force fields (NNFF), are bridging the gap between macroscopically relevant parameters and quantum mechanical simulations. This work describes an NNFF training strategy and uses it as the back end for an effective harmonic potential study of phase transitions in hafnia. While good agreement with experiment is found regarding the monoclinic-tetragonal transition, the commonly assumed transition to a Fm$\overline{3}$m cubic phase is found to be unlikely for the stoichiometric material

Exploring detailed microcosmic mechanisms of sodium chloride deposition in supercritical water reactors from the aspect of solvation structure: A combination of MD and QM simulations

The dissolution characteristics and microscopic dissolution process of salt in supercritical water (SCW) are the fundamental influence of scale formation. In this paper, molecular dynamics (MD) and quantum mechanics (QM) were used to study the solubility and solvation structure of sodium chloride in supercritical water. The potential mean force (PMF), radial distribution function (RDF) and coordination number (CN) indicate that the solubility of sodium chloride in SCW decreases with the increase of temperature and the decrease of density. As the system density decreases from 0.3 g/cm3 to 0.1 g/cm3 and the temperature increases from 600 K to 900 K, the CN of water molecules in the solvation shell decreases from 2.89 to 1.01, which is consistent with the trend of the experimental results. The electrostatic potential distribution and intermolecular interaction of the solvation shell around Na+ were obtained by QM method. The solution free energy of Na+–3H2O in implicit solvent was obtained. In the condition range of this study, when the temperature is the lowest and the dielectric constant is the largest, the free energy of dissolution is the smallest, which is −0.0991 a.u. This study will lay a foundation for the study of salt deposition in SCW.