DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

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.

Benchmark Investigation of SCC-DFTB against Standard and Hybrid DFT to Model Electronic Properties in Two-Dimensional MOFs for Thermoelectric Applications

Recent studies have shown that metal-organic frameworks (MOFs) have potential as thermoelectric materials, and the topic has received increasing attention. The main motivation for this project is to further our knowledge of thermoelectric properties in MOFs and find which available self-consistent-charge density functional tight binding (SCC-DFTB) method can best predict (at least trends in) the electronic properties of MOFs at a lower computational cost than standard density functional theory (DFT). In this work, the electronic properties of monolayer, serrated, AA-stacked, and/or AB-stacked Zn3C6O6, Cd3C6O6, Zn-NH-MOF─for which no previous calculations of thermoelectric performance exist─and Ni3(HITP)2 MOFs are modeled with DFT-PBE, DFT-HSE06, GFN1-xTB, GFN2-xTB, and DFTB-3ob/mio. The band structures, density of states, and their relative orbital contributions, as well as the electrical conductivity, Seebeck coefficient, and power factor, are compared across methods and geometries. Our results suggest that GFN-xTB is adequate to predict the MOFs' band structure shape and density of states but not band gap. Our calculations further indicate that Zn3C6O6, Cd3C6O6, and Zn-NH-MOF have higher power factor values than Ni3(HITP)2, one of the highest performing synthesized MOFs, and are therefore promising for thermoelectric applications.

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations

Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great promise for advanced technologies, provided their structure remains uniform throughout their length. Their growth takes place at high temperatures across a tube-catalyst interface. Structural defects formed during growth alter CNT properties. These defects are believed to form and heal at the tube-catalyst interface but an understanding of these mechanisms at the atomic-level is lacking. Here we present DeepCNT-22, a machine learning force field (MLFF) to drive molecular dynamics simulations through which we unveil the mechanisms of CNT formation, from nucleation to growth including defect formation and healing. We find the tube-catalyst interface to be highly dynamic, with large fluctuations in the chiral structure of the CNT-edge. This does not support continuous spiral growth as a general mechanism, instead, at these growth conditions, the growing tube edge exhibits significant configurational entropy. We demonstrate that defects form stochastically at the tube-catalyst interface, but under low growth rates and high temperatures, these heal before becoming incorporated in the tube wall, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available through experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms.

DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference

We present a modification to self-consistent charge density functional-based tight binding (SCC-DFTB), which allows computation based on approximate atomic charges. We obtain these charges by means of a machine learning (ML) process that combines a Coulomb model with a neural network. This allows us to avoid the SCC cycles in the SCC-DFTB calculation while maintaining its accuracy. The main input of the model is the atomic positions characterized by a set of atom-centered symmetry functions. The charge inference from our ML algorithm is as close as 10-2 units of charge from the exact SCC solution. Our ML-DFTB approach provides a good approximation of the density matrix and of the energy and forces with only a single diagonalization. This is a significant computational saving with respect to the complete SCC algorithm, which allows us to investigate a bigger ensemble of atoms. We show the quality of our approach in the case of charged silicon carbide (SiC) clusters. The ML-DFTB potential energy surface (PES) mimics the SCC-DFTB PES rather well, despite its simplicity. This allows us to obtain the same geometric structure ordering with respect to energy for small clusters. The dissociation barriers for ion emission are well-reproduced, which opens the way to investigating ion field emission and charged cluster stability. The ML-DFTB approach is obviously not limited to charged clusters or SiC materials. It opens a new route to investigate larger clusters than those investigated by standard SCC-DFTB, as well as surface and solid-state chemistry at the atomic level.

A DFTB study on the electronic response of encapsulated DNA nucleobases onto chiral CNTs as a sequencer

Sequencing the DNA nucleobases is essential in the diagnosis and treatment of many diseases related to human genes. In this article, the encapsulation of DNA nucleobases with some of the important synthesized chiral (7, 6), (8, 6), and (10, 8) carbon nanotubes were investigated. The structures were modeled by applying density functional theory based on tight binding method (DFTB) by considering semi-empirical basis sets. Encapsulating DNA nucleobases on the inside of CNTs caused changes in the electronic properties of the selected chiral CNTs. The results confirmed that van der Waals (vdW) interactions, π-orbitals interactions, non-bonded electron pairs, and the presence of high electronegative atoms are the key factors for these changes. The result of electronic parameters showed that among the CNTs, CNT (8, 6) is a suitable choice in sequencing guanine (G) and cytosine (C) DNA nucleobases. However, they are not able to sequence adenine (A) and thymine (T). According to the band gap energy engineering approach and absorption energy, the presence of G and C DNA nucleobases decreased the band gap energy of CNTs. Hence selected CNTs suggested as biosensor substrates for sequencing G and C DNA nucleobases.

Long-range proton and hydroxide ion transfer dynamics at the water/CeO2 interface in the nanosecond regime: reactive molecular dynamics simulations and kinetic analysis

The structural properties, dynamical behaviors, and ion transport phenomena at the interface between water and cerium oxide are investigated by reactive molecular dynamics (MD) simulations employing neural network potentials (NNPs). The NNPs are trained to reproduce density functional theory (DFT) results, and DFT-based MD (DFT-MD) simulations with enhanced sampling techniques and refinement schemes are employed to efficiently and systematically acquire training data that include diverse hydrogen-bonding configurations caused by proton hopping events. The water interfaces with two low-index surfaces of (111) and (110) are explored with these NNPs, and the structure and long-range proton and hydroxide ion transfer dynamics are examined with unprecedented system sizes and long simulation times. Various types of proton hopping events at the interface are categorized and analyzed in detail. Furthermore, in order to decipher the proton and hydroxide ion transport phenomena along the surface, a counting analysis based on the semi-Markov process is formulated and applied to the MD trajectories to obtain reaction rates by considering the transport as stochastic jump processes. Through this model, the coupling between hopping events, vibrational motions, and hydrogen bond networks at the interface are quantitatively examined, and the high activity and ion transport phenomena at the water/CeO2 interface are unequivocally revealed in the nanosecond regime.

Prolonged Exciton Lifetime Is Achieved in Porphyrin Nanoring by Template Engineering: A Nonadiabatic Tight Binding Approach

Porphyrin nanoring has been attracting immense attention due to its light harvesting capacity and potential applications in optical, catalysis, sensor, and electronic devices. We demonstrate by nonadiabatic quantum dynamics simulations that the photovoltaic efficiency can be enhanced by template engineering. Altering the hexadentate template (T6) with two tridentate templates (2T3) within the porphyrin ring (P6) cavity accelerated the electron transfer twice and suppressed the electron-hole recombination by nearly three times. The atomistic tight-binding simulation rationalized the dynamics by different localizations of charge of the band edge states, changes in nonadiabatic coupling, alteration in quantum coherence, and involvement of diverse electron-phonon vibrational modes. Further 2T3 templates more strongly hold the P6 ring than T6, reducing the structural fluctuation. As a result, the nonadiabatic coupling becomes weaker and suppresses the carrier recombination. Current atomistic simulation presents a template engineering strategy to enhance the exciton lifetime along with ultrafast charge separation, crucial factors for photovoltaic applications.

Excited state properties of an A-D-A non-fullerene electron acceptor: a LC-TD-DFTB study

Understanding charge transfer processes is essential to estimate the performance of organic photovoltaic technologies. Although experimental production is on the rise, predictability strongly relies on theoretical modeling, which is limited to the size of semiconductors. As a computationally favorable approach, we benchmarked the long-range corrected (LC) time-dependent (TD) formulation of the semi-empirical density functional-based tight-binding method (DFTB) for three polycyclic aromatic hydrocarbons (PAHs) and studied the DTP-IC-4Ph molecule, a PAH-based non-fullerene electron acceptor (NFA) with an A-D-A backbone structure. After a thorough investigation into the long-range parameter (ω) tuning for naphthalene, anthracene and pyrene, the excitation energies, oscillator strengths and Natural Transition Orbitals (NTOs) were compared with the standard ωB97X-D/6-31G(d,p) level of theory and the ADC2/6-31G(d,p) multiconfigurational method. We estimated mobility-related properties of the NFA and considered 1000 thermally accessible configurations to qualitatively reproduce the experimental absorption profile and investigate the energetic disorder. Finally, we conducted a fragment-based analysis using the one-electron transition density matrix (1TDM) to determine the character of the excited states and investigate the effect of side chains on exciton formation. Our results are sensitive to the level of theory and highly dependent on the long-range parameter but suggest that the presence of alkyl chains promotes a higher average charge delocalization and allows for additional hopping mechanisms, favoring the charge transfer dynamics.

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

Family behavior and Dirac bands in armchair nanoribbons with 4-8 defect lines

Bottom-up synthesis from molecular precursors is a powerful route for the creation of novel synthetic carbon-based low-dimensional materials, such as planar carbon lattices. The wealth of conceivable precursor molecules introduces a significant number of degrees-of-freedom for the design of materials with defined physical properties. In this context,a prioriknowledge of the electronic, vibrational and optical properties provided by modernab initiosimulation methods can act as a valuable guide for the design of novel synthetic carbon-based building blocks. Using density functional theory, we performed simulations of the electronic properties of armchair-edged graphene nanoribbons (AGNR) with a bisecting 4-8 ring defect line. We show that the electronic structures of the defective nanoribbons of increasing width can be classified into three distinct families of semiconductors, similar to the case of pristine AGNR. In contrast to the latter, we find that every third nanoribbon is a zero-gap semiconductor with Dirac-type crossing of linear bands at the Fermi energy. By employing tight-binding models including interactions up to third-nearest neighbors, we show that the family behavior, the formation of direct and indirect band gaps and of linear band crossings in the defective nanoribbons is rooted in the electronic properties of the individual nanoribbon halves on either side of the defect lines, and can be effectively through introduction of additional 'interhalf' coupling terms.

Prediction of induced magnetism in 2D Ti2C based MXenes by manipulating the mixed surface functionalization and metal substitution computed by xTB model Hamiltonian of the DFTB method

We employed the recently developed density functional tight binding (DFTB) method's Hamiltonian, GFN1-xTB, for modeling the mixed termination in Ti2C MXenes, namely three types of termination by combining -O and -OH, -O and -F, and -F and -OH. We demonstrated that the approach yields reliable predictions for the electronic and magnetic properties of such MXenes. The first highlighted result is that the mixed surface functionalization in Ti2CAxBy MXenes induces spin polarization with diverse magnetic alignments, including ferromagnetism and two types of antiferromagnetism. We further identified the magnetic alignment for the investigated MXene in terms of the compositions of the terminal groups. Moreover, the effect of the transition metal (Ti) substituted by the Sc atom on the electronic and magnetic properties was also investigated. We found that the studied systems maintain the magnetism and the metallic characteristics. A magnetic transition from antiferromagnetic (AFM) to ferrimagnetic (FiM) ordering was found for ScTi15C8F8(OH)8 and ScTi15C8F12(OH)4 compounds. Finally, we proved that incorporating the Sc atom into the lattice of Ti2CO2 and the mixed surface termination in Ti2CAxBy is an effective strategy to induce magnetism. Our study may provide a new potential application for designing MXene-based spintronics.

Characterization of the Coordination and Solvation Dynamics of Solvated Systems─Implications for the Analysis of Molecular Interactions in Solutions and Pure H2O

The characterization of solvation shells of atoms, ions, and molecules in solution is essential to relate solvation properties to chemical phenomena such as complex formation and reactivity. Different definitions of the first-shell coordination sphere from simulation data can lead to potentially conflicting data on the structural properties and associated ligand exchange dynamics. The definition of a solvation shell is typically based on a given threshold distance determined from the respective solute-solvent pair distribution function g(r) (i.e., GC). Alternatively, a nearest neighbor (NN) assignment based on geometric properties of the coordination complex without the need for a predetermined cutoff criterion, such as the relative angular distance (RAD) or the modified Voronoi (MV) tessellation, can be applied. In this study, the effect of different NN algorithms on the coordination number and ligand exchange dynamics evaluated for a series of monatomic ions in aqueous solution, carbon dioxide in aqueous and dichloromethane solutions, and pure liquid water has been investigated. In the case of the monatomic ions, the RAD approach is superior in achieving a well separated definition of the first solvation layer. In contrast, the MV algorithm provides a better separation of the NNs from a molecular point of view, leading to better results in the case of solvated CO2. When analyzing the coordination environment in pure water, the cutoff-based GC framework was found to be the most reliable approach. By comparison of the number of ligand exchange reactions and the associated mean ligand residence times (MRTs) with the properties of the coordination number autocorrelation functions, it is shown that although the average coordination numbers are sensitive to the different definitions of the first solvation shell, highly consistent estimates for the associated MRT of the solvated system are obtained in the majority of cases.

Formulation of transition dipole gradients for non-adiabatic dynamics with polaritonic states

A general formulation of the strong coupling between photons confined in a cavity and molecular electronic states is developed for the state-interaction state-average spin-restricted ensemble-referenced Kohn-Sham method. The light-matter interaction is included in the Jaynes-Cummings model, which requires the derivation and implementation of the analytical derivatives of the transition dipole moments between the molecular electronic states. The developed formalism is tested in the simulations of the nonadiabatic dynamics in the polaritonic states resulting from the strong coupling between the cavity photon mode and the ground and excited states of the penta-2,4-dieniminium cation, also known as PSB3. Comparison with the field-free simulations of the excited-state decay dynamics in PSB3 reveals that the light-matter coupling can considerably alter the decay dynamics by increasing the excited state lifetime and hindering photochemically induced torsion about the C=C double bonds of PSB3. The necessity of obtaining analytical transition dipole gradients for the accurate propagation of the dynamics is underlined.

Reversible actuation of α-borophene nanoscrolls

In this work, we proposed and investigated the structural and electronic properties of boron-based nanoscrolls (armchair and zigzag) using the DFTB+ method. We also investigated the electroactuation process (injecting and removing charges). A giant electroactuation was observed, but the results show relevant differences between the borophene and carbon nanoscrolls. The molecular dynamics simulations showed that the scrolls are thermally and structurally stable for a large range of temperatures (up to 600 K), and the electroactuation process can be easily tuned and can be entirely reversible for some configurations.

Electronic Lieb lattice signatures embedded in two-dimensional polymers with a square lattice

Exotic band features, such as Dirac cones and flat bands, arise directly from the lattice symmetry of materials. The Lieb lattice is one of the most intriguing topologies, because it possesses both Dirac cones and flat bands which intersect at the Fermi level. However, the synthesis of Lieb lattice materials remains a challenging task. Here, we explore two-dimensional polymers (2DPs) derived from zinc-phthalocyanine (ZnPc) building blocks with a square lattice (sql) as potential electronic Lieb lattice materials. By systematically varying the linker length (ZnPc-xP), we found that some ZnPc-xP exhibit a characteristic Lieb lattice band structure. Interestingly though, fes bands are also observed in ZnPc-xP. The coexistence of fes and Lieb in sql 2DPs challenges the conventional perception of the structure-electronic structure relationship. In addition, we show that manipulation of the Fermi level, achieved by electron removal or atom substitution, effectively preserves the unique characteristics of Lieb bands. The Lieb Dirac bands of ZnPc-4P shows a non-zero Chern number. Our discoveries provide a fresh perspective on 2DPs and redefine the search for Lieb lattice materials into a well-defined chemical synthesis task.

Closed-loop recyclability of a biomass-derived epoxy-amine thermoset by methanolysis

Epoxy resin thermosets (ERTs) are an important class of polymeric materials. However, owing to their highly cross-linked nature, they suffer from poor recyclability, which contributes to an unacceptable level of environmental pollution. There is a clear need for the design of inherently recyclable ERTs that are based on renewable resources. We present the synthesis and closed-loop recycling of a fully lignocellulose-derivable epoxy resin (DGF/MBCA), prepared from dimethyl ester of 2,5-furandicarboxylic acid (DMFD), 4,4'-methylenebis(cyclohexylamine) (MBCA), and glycidol, which displays excellent thermomechanical properties (a glass transition temperature of 170°C, and a storage modulus at 25°C of 1.2 gigapascals). Notably, the material undergoes methanolysis in the absence of any catalyst, regenerating 90% of the original DMFD. The diamine MBCA and glycidol can subsequently be reformed by acetolysis. Application and recycling of DGF/MBCA in glass and plant fiber composites are demonstrated.

Surface Depassivation via B-O Dative Bonds Affects the Friction Performance of B-Doped Carbon Coatings

Boron doping of diamond-like carbon coatings has multiple effects on their tribological properties. While boron typically reduces wear in cutting applications, some B-doped coatings show poor tribological performance compared with undoped films. This is the case of the tribological tests presented in this work in which an alumina ball is placed in frictional contact with different undoped and B-doped amorphous carbon coatings in humid air. With B-doped coatings, a higher friction coefficient at a steady state with respect to their undoped counterparts was observed. Estimates of the average contact shear stress based on experimental friction coefficients, surface topographies, and Persson's contact theory suggest that the increased friction is compatible with the formation of a sparse network of interfacial ether bonds leading to a mild cold-welding friction regime, as documented in the literature. Tight binding and density functional theory simulations were performed to investigate the chemical effect of B-doping on the interfacial properties of the carbon coatings. The results reveal that OH groups that normally passivate carbon surfaces in humid environments can be activated by boron and form B-O dative bonds across the tribological interfaces, leading to a mild cold-welding friction regime. Simulations performed on different tribological pairs suggest that this mechanism could be valid for B-doped carbon surfaces in contact with a variety of materials. In general, this study highlights the impact that subtle modifications in surface and interface chemistry caused by the presence of impurities can have on macroscopic properties, such as friction and wear.

Computational Design of Photosensitive Polymer Templates To Drive Molecular Nanofabrication

Molecular electronics promises the ultimate level of miniaturization of computers and other machines as organic molecules are the smallest known physical objects with nontrivial structure and function. But despite the plethora of molecular switches, memories, and motors developed during the almost 50-years long history of molecular electronics, mass production of molecular computers is still an elusive goal. This is mostly due to the lack of scalable nanofabrication methods capable of rapidly producing complex structures (similar to silicon chips or living cells) with atomic precision and a small number of defects. Living nature solves this problem by using linear polymer templates encoding large volumes of structural information into sequence of hydrogen bonded end groups which can be efficiently replicated and which can drive assembly of other molecular components into complex supramolecular structures. In this paper, we propose a nanofabrication method based on a class of photosensitive polymers inspired by these natural principles, which can operate in concert with UV photolithography used for fabrication of current microelectronic processors. We believe that such a method will enable a smooth transition from silicon toward molecular nanoelectronics and photonics. To demonstrate its feasibility, we performed a computational screening of candidate molecules that can selectively bind and therefore allow the deterministic assembly of molecular components. In the process, we unearthed trends and design principles applicable beyond the immediate scope of our proposed nanofabrication method, e.g., to biologically relevant DNA analogues and molecular recognition within hydrogen-bonded systems.

First-Principles Models of Polymorphism of Pharmaceuticals: Maximizing the Accuracy-to-Cost Ratio

Accuracy and sophistication of in silico models of structure, internal dynamics, and cohesion of molecular materials at finite temperatures increase over time. Applicability limits of ab initio polymorph ranking that would be feasible at reasonable costs currently represent crystals of moderately sized molecules (less than 20 nonhydrogen atoms) and simple unit cells (containing rather only one symmetry-irreducible molecule). Extending the applicability range of the underlying first-principles methods to larger systems with a real-life significance, and enabling to perform such computations in a high-throughput regime represent additional challenges to be tackled in computational chemistry. This work presents a novel composite method that combines the computational efficiency of density-functional tight-binding (DFTB) methods with the accuracy of density-functional theory (DFT). Being rooted in the quasi-harmonic approximation, it uses a cheap method to perform all of the costly scans of how static and dynamic characteristics of the crystal vary with respect to its volume. Such data are subsequently corrected to agree with a higher-level model, which must be evaluated only at a single volume of the crystal. It thus enables predictions of structural, cohesive, and thermodynamic properties of complex molecular materials, such as pharmaceuticals or organic semiconductors, at a fraction of the original computational cost. As the composite model retains the solid physical background, it suffers from a minimum accuracy deterioration compared to the full treatment with the costly approach. The novel methodology is demonstrated to provide consistent results for the structural and thermodynamic properties of real-life molecular crystals and their polymorph ranking.

Long range piezoelectricity effects in van der Waals heterobilayer systems beyond 1000 atoms

Twist angle is a relevant design and control component for the piezoelectric coefficients of van der Waals (vdW) heterostructures. This theoretical work assesses in high detail the impact of the twist angle on the piezoelectricity of two-dimensional (2D) heterobilayer systems. We expand the density-functional based tight-binding method to predict the piezoelectric coefficients of twisted and corrugated 2D heterobilayer structures with more than 1000 atoms. We showcase the method on hexagonal III-V/transition metal dichalcogenide vdW heterosystems. Our calculations yield a periodic relationship between the in-plane piezoelectric coefficients and the corresponding twist angles, indicating the tunability of the in-plane piezoelectricity. In contrast, the out-of-plane piezoelectricity is not twist angle dependent, but nonlinearly changes with the average interlayer distance.

Unraveling the anti-corrosion mechanisms of a novel hydrazone derivative on steel in contaminated concrete pore solutions: An integrated study

INTRODUCTION

Corrosion-induced deterioration of infrastructure is a growing global concern. The development and application of corrosion inhibitors are one of the most effective approaches to protect steel rebar from corrosion. Hence, this study focuses on a novel hydrazone derivative, (E)-N'-(4-(dimethylamino)benzylidene)-2-(5-methoxy-2-methyl-1H-indol-3-yl)aceto-hydrazide (HIND), and its potential application to mitigate corrosion in steel rebar exposed to chloride-contaminated concrete pore solutions (ClSCPS).

OBJECTIVES

The research aims to evaluate the anti-corrosion capabilities of HIND on steel rebar within a simulated corrosive environment, focusing on the mechanisms of its inhibitory effect.

METHODS

The corrosion of steel rebar exposed to the ClSCPS was studied through weight loss and electrochemical methods. The surface morphology of steel rebar surface was characterized by FE-SEM-EDS, AFM; oxidation states of the steel rebar and crystal structures were examined using X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) methods. Further, experimental findings were complemented by theoretical studies using self-consistent-charge density-functional tight-binding (SCC-DFTB) simulations. The performance of HIND was monitored at an optimal concentration over a period of 30 days.

RESULTS

The results indicated a significant reduction in steel rebar corrosion upon introducing HIND. The inhibitor molecules adhered to the steel surface, preventing further deterioration and achieving an inhibition efficiency of 88.4% at 0.5 mmol/L concentration. The surface morphology analysis confirmed the positive effect of HIND on the rebar surface, showing a decrease in the surface roughness of the steel rebar from 183.5 in uninhibited to 50 nm in inhibited solutions. Furthermore, SCC-DFTB simulations revealed the presence of coordination between iron atoms and HIND active sites.

CONCLUSION

The findings demonstrate the potential of HIND as an effective anti-corrosion agent in chloride-contaminated environments. Its primary adsorption mechanism involves charge transfer from the inhibitor molecules to iron atoms. Therefore, applying HIND could be an effective strategy to address corrosion-related challenges in reinforced infrastructure.

Electronic Excited States from Physically Constrained Machine Learning

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly with physically grounded operations. We present an example of an integrated modeling approach in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those on which it is trained and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parametrization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency and providing a blueprint for developing ML-augmented electronic-structure methods.

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.

Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface

Structural optimization has been a crucial component in computational materials research, and structure predictions have relied heavily on this technique, in particular. In this study, we introduce a novel method that enhances the efficiency of local optimization by integrating extra fingerprint space into the optimization process. Our approach utilizes a mixed energy concept in the hyper potential energy surface (PES), combining real energy and a newly introduced fingerprint energy derived from the symmetry of the local atomic environment. This method strategically guides the optimization process toward high-symmetry, low-energy structures by leveraging the intrinsic symmetry of the atomic configurations. The effectiveness of our approach was demonstrated through structural optimizations of silicon, silicon carbide, and Lennard-Jones cluster systems. Our results show that the fingerprint space biasing technique significantly enhances the performance and probability of discovering energetically favorable, high-symmetry structures as compared to conventional optimizations. The proposed method is anticipated to streamline the search for new materials and facilitate the discovery of novel energetically favorable configurations.

Conformational energies of biomolecules in solution: Extending the MPCONF196 benchmark with explicit water molecules

A prerequisite for the computational prediction of molecular properties like conformational energies of biomolecules is a reliable, robust, and computationally affordable method usually selected according to its performance for relevant benchmark sets. However, most of these sets comprise molecules in the gas phase and do not cover interactions with a solvent, even though biomolecules typically occur in aqueous solution. To address this issue, we introduce a with explicit water molecules solvated version of a gas-phase benchmark set containing 196 conformers of 13 peptides and other relevant macrocycles, namely MPCONF196 [J. Řezáč et al., JCTC 2018, 14, 1254-1266], and provide very accurate PNO-LCCSD(T)-F12b/AVQZ' reference values. The novel solvMPCONF196 benchmark set features two additional challenges beyond the description of conformers in the gas phase: conformer-water and water-water interactions. The overall best performing method for this set is the double hybrid revDSDPBEP86-D4/def2-QZVPP yielding conformational energies of almost coupled cluster quality. Furthermore, some (meta-)GGAs and hybrid functionals like B97M-V and ω B97M-D with a large basis set reproduce the coupled cluster reference with an MAD below 1 kcal mol- 1  . If more efficient methods are required, the composite DFT-method r2 SCAN-3c (MAD of 1.2 kcal mol- 1 ) is a good alternative, and when conformational energies of polypeptides or macrocycles with more than 500-1000 atoms are in the focus, the semi-empirical GFN2-xTB or the MMFF94 force field (for very large systems) are recommended.

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials

Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

Positivity preserving density matrix minimization at finite temperatures via square root

We present a Wave Operator Minimization (WOM) method for calculating the Fermi-Dirac density matrix for electronic structure problems at finite temperature while preserving physicality by construction using the wave operator, i.e., the square root of the density matrix. WOM models cooling a state initially at infinite temperature down to the desired finite temperature. We consider both the grand canonical (constant chemical potential) and canonical (constant number of electrons) ensembles. Additionally, we show that the number of steps required for convergence is independent of the number of atoms in the system. We hope that the discussion and results presented in this article reinvigorate interest in density matrix minimization methods.

Control over Charge Separation by Imine Structural Isomerization in Covalent Organic Frameworks with Implications on CO2 Photoreduction

Two-dimensional covalent organic frameworks (COFs) are an emerging class of photocatalytic materials for solar energy conversion. In this work, we report a pair of structurally isomeric COFs with reversed imine bond directions, which leads to drastic differences in their physical properties, photophysical behaviors, and photocatalytic CO2 reduction performance after incorporating a Re(bpy)(CO)3Cl molecular catalyst through bipyridyl units on the COF backbone (Re-COF). Using the combination of ultrafast spectroscopy and theory, we attributed these differences to the polarized nature of the imine bond that imparts a preferential direction to intramolecular charge transfer (ICT) upon photoexcitation, where the bipyridyl unit acts as an electron acceptor in the forward imine case (f-COF) and as an electron donor in the reverse imine case (r-COF). These interactions ultimately lead the Re-f-COF isomer to function as an efficient CO2 reduction photocatalyst, while the Re-r-COF isomer shows minimal photocatalytic activity. These findings not only reveal the essential role linker chemistry plays in COF photophysical and photocatalytic properties but also offer a unique opportunity to design photosensitizers that can selectively direct charges.

Comprehensive Study on the Adsorption and Degradation of Dichlorodiphenyltrichloroethane on Bifunctional Adsorption-Photocatalysis Material TiO2/MCM-41 Using Quantum Chemical Methods

The adsorption and degradation capacities of dichlorodiphenyltrichloroethane (DDT) on a photocatalyst composed of TiO2 supported on the mesoporous material MCM-41 (TiO2/MCM-41) were investigated using density functional theory and real-time density functional theory methods. The van der Waals interactions within the PBE functional were adjusted by using the Grimme approach. The adsorption of DDT was evaluated through analyses involving adsorption energy, Hirshfeld atomic charges, Wiberg bond orders, molecular electrostatic potential, noncovalent interaction analysis, and bond path analysis. The findings reveal that DDT undergoes physical adsorption on pristine MCM-41 or MCM-41 modified with Al or Fe due to the very small bond order (only about 0.15-0.18) as well as the change in total charge of DDT after adsorption is close to 0. However, it chemically adsorbs onto the TiO2/MCM-41 composite through the formation of Ti···Cl coordination bonds because the maximum bond order is very large, about 1.0 (it can be considered as a single bond). The adsorption process is significantly influenced by van der Waals interactions (accounting for approximately 30-40% of the interaction energy), hydrogen bonding, and halogen bonding. MCM-41 is demonstrated to concurrently function as a support for the TiO2 photocatalyst, creating a synergistic effect that enhances the photocatalytic activity of TiO2. Based on the computational results, a novel photocatalytic mechanism for the degradation of DDT on the TiO2/MCM-41 catalyst system was proposed.

Pathway for a martensitic quartz-coesite transition

An atomistic pathway for a strain-induced subsolidus martensitic transition between quartz and coesite was found by computing the set of the smallest atomic displacements required to transform a quartz structure into a coesite structure. A minimal transformation cell with 24 [Formula: see text] formula units is sufficient to describe the diffusionless martensitic transition from quartz to coesite. We identified two families of invariant shear planes during the martensitic transition, near the {10[Formula: see text]1} and {12[Formula: see text]2} set of planes, in agreement with the orientation of planar defect structures observed in quartz samples which experienced hypervelocity impacts. We calculated the reaction barrier using density functional theory and found that the barrier of 150 meV/atom is pressure invariant from ambient pressure up to 5 GPa, while the mean principal stress limiting the stability of strained quartz is [Formula: see text] 2 GPa. The model calculations quantitatively confirm that coesite can be formed in strained quartz at pressures significantly below the hydrostatic equilibrium transition pressure.

Perylenetetracarboxylic Diimide Composite Electrodes as Organic Cathode Materials for Rechargeable Sodium-Ion Batteries: A Joint Experimental and Theoretical Study

The organic semiconductor 3,4,9,10-perylenetetracarboxylic diimide (PTCDI), a widely used industrial pigment, has been identified as a diffusion-less Na-ion storage material, allowing for exceptionally fast charging/discharging rates. The elimination of diffusion effects in electrochemical measurements enables the assessment of interaction energies from simple cyclic voltammetry experiments through the theoretical work of Laviron and Tokuda. In this work, the two N-substituted perylenes, N,N'-dimethyl-3,4,9,10-perylenetetracarboxylic diimide (Me2PTCDI) and N,N'-diphenyl-3,4,9,10-perylenetetracarboxylic diimide (Ph2PTCDI), as well as the parent molecule 3,4,9,10-perylenetetracarboxylic diimide (H2PTCDI) are investigated as thin-film composite electrodes on carbon fibers for sodium-ion batteries. The composite electrodes are analyzed with Raman spectroscopy. Interaction parameters are extracted from cyclic voltammetry measurements. The stability and rate capability of the three PTCDI derivatives are examined through galvanostatic measurements in sodium-ion half-cell batteries and the influence of the interactions on those parameters is evaluated. In addition, self-consistent charge density function tight binding calculations of the different PTCDI systems interacting with graphite have been carried out. The results show that the binding motif displays notable deviations from an ideal ABA stacking, especially for the neutral state. In addition, data obtained for the electron-transfer integrals show that the difference in performance between different PTCDI thin-film batteries cannot be solely explained by the electron-transfer properties and other factors such as H-bonding have to be considered.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

Exciton Transfer Between Extended Electronic States in Conjugated Inter-Polyelectrolyte Complexes

Artificial light harvesting, a process that involves converting sunlight into chemical potential energy, is considered to be a promising part of the overall solution to address urgent global energy challenges. Conjugated polyelectrolyte complexes (CPECs) are particularly attractive for this purpose due to their extended electronic states, tunable assembly thermodynamics, and sensitivity to their local environment. Importantly, ionically assembled complexes of conjugated polyelectrolytes can act as efficient donor-acceptor pairs for electronic energy transfer (EET). However, to be of use in material applications, we must understand how modifying the chemical structure of the CPE backbone alters the EET rate beyond spectral overlap considerations. In this report we investigate the dependence of the EET efficiency and rate on the electronic structure and excitonic wave function of the CPE backbone. To do so, we synthesized a series of alternating copolymers where the electronic states are systematically altered by introducing comonomers with electron withdrawing and electron-rich character while keeping the linear ionic charge density nearly fixed. We find evidence that the excitonic coupling may be significantly affected by the exciton delocalization radius, in accordance with analytical models based on the line-dipole approximation and quantum chemistry calculations. Our results imply that care should be taken when selecting CPE components for optimal CPEC EET. These results have implications for using CPECs as key components in water-based light-harvesting materials, either as standalone assemblies or as adsorbates on nanoparticles and thin films.

A hybrid quantum-classical theory for predicting terahertz charge-transfer plasmons in metal nanoparticles on graphene

Metal nanoparticle (NP) complexes lying on a single-layer graphene surface are studied with a developed original hybrid quantum-classical theory using the Finite Element Method (FEM) that is computationally cheap. Our theory is based on the motivated assumption that the carrier charge density in the doped graphene does not vary significantly during the plasmon oscillations. Charge transfer plasmon (CTP) frequencies, eigenvectors, quality factors, energy loss in the NPs and in graphene, and the absorption power are aspects that are theoretically studied and numerically calculated. It is shown the CTP frequencies reside in the terahertz range and can be represented as a product of two factors: the Fermi level of graphene and the geometry of the NP complex. The energy losses in the NPs are predicted to be inversely dependent on the radius R of the nanoparticle, while the loss in graphene is proportional to R and the interparticle distance. The CTP quality factors are predicted to be in the range ∼10-100. The absorption power under CTP excitation is proportional to the scalar product of the CTP dipole moment and the external electromagnetic field. The developed theory makes it possible to simulate different properties of CTPs 3-4 orders of magnitude faster compared to the original FEM or the finite-difference time domain method, providing possibilities for predicting the plasmonic properties of very large systems for different applications.

Chirality and length-dependent electron transmission of fullerene-capped chiral carbon nanotubes sandwiched in gold electrodes

In order to develop high-performance CNT-based electronic and optoelectronic devices, it is crucial to establish the relationship between the electron transport properties of carbon nanotubes (CNTs) and their structures. In this work, we have investigated the transport properties of chiral (8, m) and (10, m) CNTs sandwiched between two gold electrodes by employing nonequilibrium Green's function (NEGF) combined with density functional theory (DFT). We demonstrate that with the change of chirality the transport property changes, as predicted by the (n - m) rule. The change of length is also considered. Our results show that the electrical conductance of (10, m) CNTs is larger than that of the (8, m) CNTs, due to larger diameter. Furthermore, we found that the (8, 1) chiral CNT does not follow the (n - m) rule in shorter length and it shows metallic behavior. The cohesive energy, wavefunctions of electronic states, and coupling energy calculation indicate that the devices considered in this study are stable. The transmission spectra, current vs. voltage curves, and transmission eigenchannels provide strong evidence for our findings. Among the (10, m) series, (10, 3) CNT would be the optimal choice for a semiconducting molecular junction device with a significant conductance of 20 μA at 0.8 bias voltage.

Energy Decomposition Scheme for Rectangular Graphene Flakes

We show-to our own surprise-that total electronic energies for a family of m × n rectangular graphene flakes can be very accurately represented by a simple function of the structural parameters m and n with errors not exceeding 1 kcal/mol. The energies of these flakes, usually referred to as multiple zigzag chains Z(m,n), are computed for m, n < 21 at their optimized geometries using the DFTB3 methodology. We have discovered that the structural parameters m and n (and their simple algebraic functions) provide a much better basis for the energy decomposition scheme than the various topological invariants usually used in this context. Most terms appearing in our energy decomposition scheme seem to have simple chemical interpretations. Our observation goes against the well-established knowledge stating that many-body energies are complicated functions of molecular parameters. Our observations might have far-reaching consequences for building accurate machine learning models.

Molecular Mechanism of Labelling Functional Cysteines by Heterocyclic Thiones

Heterocyclic thiones have recently been identified as reversible covalent warheads, consistent with their mild electrophilic nature. Little is known so far about their mechanism of action in labelling nucleophilic sidechains, especially cysteines. The vast number of tractable cysteines promotes a wide range of target proteins to examine; however, our focus was put on functional cysteines. We chose the main protease of SARS-CoV-2 harboring Cys145 at the active site that is a structurally characterized and clinically validated target of covalent inhibitors. We screened an in-house, cysteine-targeting covalent inhibitor library which resulted in several covalent fragment hits with benzoxazole, benzothiazole and benzimidazole cores. Thione derivatives and Michael acceptors were selected for further investigations with the objective of exploring the mechanism of inhibition of the thiones and using the thoroughly characterized Michael acceptors for benchmarking our studies. Classical and hybrid quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations were carried out that revealed a new mechanism of covalent cysteine labelling by thione derivatives, which was supported by QM and free energy calculations and by a wide range of experimental results. Our study shows that the molecular recognition step plays a crucial role in the overall binding of both sets of molecules.

Design, spectral characterization, quantum chemical investigation, biological activity of nano-sized transition metal complexes of tridentate 3-mercapto-4H-1,2,4-triazol-4-yl-aminomethylphenol Schiff base ligand

A tridentate Schiff base ligand, H2MTIP, was produced by condensing salicylaldehyde with 4-amino-4H-1,2,4-triazole-3-thiol. The ligand was then used to create nanosized complexes of Pt(II), Ni(II), Cu(II), and Pd(II). The complexes have the composition [Pt/Ni/Cu/or Pd(MTIP)(H2O)], this conclusion is supported by molar conductance, magnetic moments, elemental analyses, spectral analyses. In DFT analysis, the 6-31+ g(d,p) basis set was used to fully optimize the energy with respect to the shapes of Schiff base ligand and metal complexes. Pt(II), Ni(II), Cu(II), and Pd(II) complexes have been assigned square-planar geometries. At the same time, the intense diffraction peaks in X-ray diffractograms show their crystalline features with particle sizes in the nanoscale range. The binding interaction of calf thymus DNA with these metal complexes and their insulin-like activity was examined in vitro by inhibiting α-amylase. The study investigated the in-vitro activity of several complexes and identified Pt(II) complex as the one with the highest activity. The researchers then tested this complex for in-vivo antidiabetic activity in induced diabetic rats using the STZ model, and it significantly lowered blood glucose levels. The antioxidant activity and toxicity level of Pt(II) complex were also excellent, suggesting that it could be a good candidate for further research as a possible diabetes drug.Communicated by Ramaswamy H. Sarma.

Controlling Charge Carrier Dynamics in Porphyrin Nanorings by Optically Active Templates

Understanding the dynamics of photogenerated charge carriers is essential for enhancing the performance of solar and optoelectronic devices. Using atomistic quantum dynamics simulations, we demonstrate that a short π-conjugated optically active template can be used to control hot carrier relaxation, charge carrier separation, and carrier recombination in light-harvesting porphyrin nanorings. Relaxation of hot holes is slowed by 60% with an optically active template compared to that with an analogous optically inactive template. Both systems exhibit subpicosecond electron transfer from the photoactive core to the templates. Notably, charge recombination is suppressed 6-fold by the optically active template. The atomistic time-domain simulations rationalize these effects by the extent of electron and hole localization, modification of the density of states, participation of distinct vibrational motions, and changes in quantum coherence. Extension of the hot carrier lifetime and reduction of charge carrier recombination, without hampering charge separation, demonstrate a strategy for enhancing efficiencies of energy materials with optically active templates.

Probing Reactivity with External Forces: The Case of Nitroacetamides in Water

Many computational methods have been applied to interpret and predict changes in reactivity by slight modifications of a given molecular scaffold. We describe a novel and simple method based on approximate density-functional theory of valence electrons that can be applied within a large high-performance computational infrastructure to probe such changes using a statistical sample of molecular configurations, including the solvent. All the used computational tools are fully open-source. Following our previous application, we are able to explain the high acidity of C-H bond at α position in nitro compounds when the amide linkage an ammonium group is inserted into the α substituent.

Identifying pathways to metal-organic framework collapse during solvent activation with molecular simulations

Metal-organic framework (MOF) materials are a vast family of nanoporous solids with potential applications ranging from drug delivery to environmental remediation. Application of MOFs in these scenarios is hindered, however, by difficulties in MOF 'activation' after initial synthesis - removal of the synthesis solvent from the pores to make the pore space accessible - often leading to framework collapse if improperly performed. While experimental studies have correlated collapse to specific solvent properties and conditions, the mechanism of activation-collapse is currently unknown. Developing this understanding would enable researchers to create better activation protocols for MOFs, accelerating discovery and process intensification. To achieve this goal, we simulated solvent removal using grand-canonical Monte Carlo and free energy perturbation methods. By framing activation as a fluid desorption problem, we investigated activation processes in the isoreticular metal organic framework (IRMOF) family of MOFs for different solvents. We identified two pathways for solvent activation - the solvent either desorbs uniformly from each individual pore or forms coexisting phases during desorption. These mesophases in turn lead to large capillary stresses within the framework, corroborating experimental hypotheses for the cause of activation-collapse. Finally, we found that the activation energy of solvent removal increased with pore size and connectivity due to the increased stability of solvent mesophases, matching experimental findings. Using these simulations, it is possible to screen MOF activation procedures, enabling rapid identification of ideal solvents and conditions and thus enabling faster development of MOFs for practical applications.

Interactive molecular dynamics in virtual reality for modelling materials and catalysts

Interactive molecular dynamics simulation in virtual reality (iMD-VR) is emerging as a promising technique in molecular science. Here, we demonstrate its use in a range of fifteen applications in materials science and heterogeneous catalysis. In this work, the iMD-VR package Narupa is used with the MD package, DL_POLY [1]. We show how iMD-VR can be used to: (i) investigate the mechanism of lithium fast ion conduction by directing the formation of defects showing that vacancy transport is favoured over interstitialcy mechanisms, and (ii) guide a molecule through a zeolite pore to explore diffusion within zeolites, examining in detail the motion of methyl n-hexanoate in H-ZSM-5 zeolite and identifying bottlenecks restricting diffusion. iMD-VR allows users to manipulate these systems intuitively, to drive changes in them and observe the resulting changes in structure and dynamics. We make these simulations available, as a resource for both teaching and research. All simulation files, with videos, can be found online (https://doi.org/10.5281/zenodo.8252314) and are provided as open-source material.

Theoretical Examination of the Hydroxide Transport in Cobaltocenium-Containing Polyelectrolytes

Polymers incorporating cobaltocenium groups have received attention as promising components of anion-exchange membranes (AEMs), exhibiting a good balance of chemical stability and high ionic conductivity. In this work, we analyze the hydroxide diffusion in the presence of cobaltocenium cations in an aqueous environment based on the molecular dynamics of model systems confined in one dimension to mimic the AEM channels. In order to describe the proton hopping mechanism, the forces are obtained from the electronic structure computed at the density-functional tight-binding level. We find that the hydroxide diffusion depends on the channel size, modulation of the electrostatic interactions by the solvation shell, and its rearrangement ability. Hydroxide diffusion proceeds via both the vehicular and structural diffusion mechanisms with the latter playing a larger role at low diffusion coefficients. The highest diffusion coefficient is observed under moderate water densities (around half the density of liquid water) when there are enough water molecules to form the solvation shell, reducing the electrostatic interaction between ions, yet there is enough space for the water rearrangements during the proton hopping. The effects of cobaltocenium separation, orientation, chemical modifications, and the role of nuclear quantum effects are also discussed.

Theory and modeling of light-matter interactions in chemistry: current and future

Light-matter interaction not only plays an instrumental role in characterizing materials' properties via various spectroscopic techniques but also provides a general strategy to manipulate material properties via the design of novel nanostructures. This perspective summarizes recent theoretical advances in modeling light-matter interactions in chemistry, mainly focusing on plasmon and polariton chemistry. The former utilizes the highly localized photon, plasmonic hot electrons, and local heat to drive chemical reactions. In contrast, polariton chemistry modifies the potential energy curvatures of bare electronic systems, and hence their chemistry, via forming light-matter hybrid states, so-called polaritons. The perspective starts with the basic background of light-matter interactions, molecular quantum electrodynamics theory, and the challenges of modeling light-matter interactions in chemistry. Then, the recent advances in modeling plasmon and polariton chemistry are described, and future directions toward multiscale simulations of light-matter interaction-mediated chemistry are discussed.

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.

Velocity-Gauge Real-Time Time-Dependent Density Functional Tight-Binding for Large-Scale Condensed Matter Systems

We present a new velocity-gauge real-time, time-dependent density functional tight-binding (VG-rtTDDFTB) implementation in the open-source DFTB+ software package (https://dftbplus.org) for probing electronic excitations in large, condensed matter systems. Our VG-rtTDDFTB approach enables real-time electron dynamics simulations of large, periodic, condensed matter systems containing thousands of atoms with a favorable computational scaling as a function of system size. We provide computational details and benchmark calculations to demonstrate its accuracy and computational parallelizability on a variety of large material systems. As a representative example, we calculate laser-induced electron dynamics in a 512-atom amorphous silicon supercell to highlight the large periodic systems that can be examined with our implementation. Taken together, our VG-rtTDDFTB approach enables new electron dynamics simulations of complex systems that require large periodic supercells, such as crystal defects, complex surfaces, nanowires, and amorphous materials.

Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications

Two-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting in reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that varying the cross-linking concentration has a negligible effect on stability and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane technologies.

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).

Multifidelity Machine Learning for Molecular Excitation Energies

The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular aggregates is of key importance, requiring highly accurate excitation energies. To this end, machine learning techniques can be a very useful tool, though the cost of generating highly accurate training data sets still remains a severe challenge. To overcome this hurdle, this work proposes the use of multifidelity machine learning where very little training data from high accuracies is combined with cheaper and less accurate data to achieve the accuracy of the costlier level. In the present study, the approach is employed to predict vertical excitation energies to the first excited state for three molecules of increasing size, namely, benzene, naphthalene, and anthracene. The energies are trained and tested for conformations stemming from classical molecular dynamics and density functional based tight-binding simulations. It can be shown that the multifidelity machine learning model can achieve the same accuracy as a machine learning model built only on high-cost training data while expending a much lower computational effort to generate the data. The numerical gain observed in these benchmark test calculations was over a factor of 30 but certainly can be much higher for high-accuracy data.

Universal Pairwise Interatomic van der Waals Potentials Based on Quantum Drude Oscillators

Repulsive short-range and attractive long-range van der Waals (vdW) forces play an appreciable role in the behavior of extended molecular systems. When using empirical force fields, the most popular computational methods applied to such systems, vdW forces are typically described by Lennard-Jones-like potentials, which unfortunately have a limited predictive power. Here, we present a universal parameterization of a quantum-mechanical vdW potential, which requires only two free-atom properties─the static dipole polarizability α1 and the dipole-dipole C6 dispersion coefficient. This is achieved by deriving the functional form of the potential from the quantum Drude oscillator (QDO) model, employing scaling laws for the equilibrium distance and the binding energy, and applying the microscopic law of corresponding states. The vdW-QDO potential is shown to be accurate for vdW binding energy curves, as demonstrated by comparing to the ab initio binding curves of 21 noble-gas dimers. The functional form of the vdW-QDO potential has the correct asymptotic behavior at both zero and infinite distances. In addition, it is shown that the damped vdW-QDO potential can accurately describe vdW interactions in dimers consisting of group II elements. Finally, we demonstrate the applicability of the atom-in-molecule vdW-QDO model for predicting accurate dispersion energies for molecular systems. The present work makes an important step toward constructing universal vdW potentials, which could benefit (bio)molecular computational studies.