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

Probing the range of applicability of structure- and energy-adjusted QM/MM link bonds III: QM/MM MD simulations of solid-state systems at the example of layered carbon structures

The previously introduced workflow to achieve an energetically and structurally optimized description of frontier bonds in quantum mechanical/molecular mechanics (QM/MM)-type applications was extended into the regime of computational material sciences at the example of a layered carbon model systems. Optimized QM/MM link bond parameters at HSEsol/6-311G(d,p) and self-consistent density functional tight binding (SCC-DFTB) were derived for graphitic systems, enabling detailed investigation of specific structure motifs occurring in graphene-derived structures v i a quantum-chemical calculations. Exemplary molecular dynamics (MD) simulations in the isochoric-isothermic (NVT) ensemble were carried out to study the intercalation of lithium and the properties of the Stone-Thrower-Wales defect. The diffusivity of lithium as well as hydrogen and proton adsorption on a defective graphene surface served as additional example. The results of the QM/MM MD simulations provide detailed insight into the applicability of the employed link-bond strategy when studying intercalation and adsorption properties of graphitic materials.

AI-assisted models to predict chemotherapy drugs modified with C60 fullerene derivatives

Employing quantitative structure-activity relationship (QSAR)/ quantitative structure-property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug-fullerene complexes (i.e., drug-pristine C60 fullerene and drug-carboxyfullerene C60-COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson's hard-soft acid-base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug-fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.

Atomic-level structure of the amorphous drug atuliflapon via NMR crystallography

We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

Theoretical Investigation on the Reversible Photoswitch Mechanism of Benzylidene-Oxazolone System

The design and application of molecular photoswitches have attracted much attention. Herein, we performed a detailed computational study on the photoswitch benzylidene-oxazolone system based on static electronic structure calculations and on-the-fly excited-state dynamic simulations. For the Z and E isomer, we located six and four minimum energy conical intersections (MECIs) between the first excited state (S1) and the ground state (S0), respectively. Among them, the relaxation pathway driven by ring-puckering motion is the most competitive channel with the photoisomeization process, leading to the low photoisomerization quantum yield. In the dynamic simulations, about 88 % and 66 % trajectories decay from S1 to S0 for Z and E isomer, respectively, within the total simulation time of ~2 ps. The photoisomeization quantum yields obtained in our study (0.20 for Z→E and 0.12 for E→Z) agree well with the experimental measured values (0.25 and 0.11), even though the number of trajectories is limited to 50. Our study sheds light on the complexity of the benzylidene-oxazolone system 's deactivation process and the competitive mechanisms among different reaction channels, which provides theoretical guidance for further design and development of benzylidene-oxazolone based molecular photoswitches.

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Mechanism of activation and autophosphorylation of a histidine kinase

Histidine kinases (HK) are one of the main prokaryotic signaling systems. Two structurally conserved catalytic domains inside the HK enable autokinase, phosphotransfer, and phosphatase activities. Here, we focus on a detailed mechanistic understanding of the functional cycle of the WalK HK by a multi-scale simulation approach, consisting of classical as well as hybrid QM/MM molecular dynamics simulation. Strikingly, a conformational transition induced solely in DHp leads to the correct activated conformation in CA crucial for autophosphorylation. This finding explains how variable sensor domains induce the transition from inactive to active state. The subsequent autophosphorylation inside DHp proceeds via a penta-coordinated transition state to a protonated phosphohistidine intermediate. This intermediate is consequently deprotonated by a suitable nearby base. The reaction energetics are controlled by the final proton acceptor and presence of a magnesium cation. The slow rates of the process result from the high energy barrier of the conformational transition between inactive and active states. The phosphorylation step exhibits a lower barrier and down-the-hill energetics. Thus, our work suggests a detailed mechanistic model for HK autophosphorylation.

Dispersion-Driven Cooperativity in Alkyl Perylene Diimide Oligomers: Insights from Density Functional Theory

The cooperative mechanism is of paramount importance in the synthesis of supramolecular polymers with desired characteristics, including molecular mass, polydispersity, and morphology. It is primarily driven by the presence of intermolecular interactions, which encompass strong hydrogen bonding, metal-ligand interactions, and dipole-dipole interactions. In this study, we utilize density functional theory and energy decomposition analysis to investigate the cooperative behavior of perylene diimide (PDI) oligomers with alkyl chains at their imide positions, which lack the previously mentioned interactions. Our systematic examination reveals that dispersion interactions originating from the alkyl side-chain substituents play an important role in promoting cooperativity within these PDIs. This influence becomes even more pronounced for alkyl chain lengths beyond hexyl groups. The energy decomposition analysis reveals that the delicate balance between dispersion energy and Pauli repulsion energy is the key driver of cooperative behavior in PDIs. Additionally, we have developed a mathematical model capable of predicting the saturated binding energies for PDI oligomers of varying sizes and alkyl chain lengths. Overall, our findings emphasize the previously undervalued significance of dispersion forces in cooperative supramolecular polymerization, enhancing our overall understanding of the cooperative mechanism.

Kekulé Counts, Clar Numbers, and ZZ Polynomials for All Isomers of (5,6)-Fullerenes C52-C70

We report an extensive tabulation of several important topological invariants for all the isomers of carbon (5,6)-fullerenes Cn with n = 52-70. The topological invariants (including Kekulé count, Clar count, and Clar number) are computed and reported in the form of the corresponding Zhang-Zhang (ZZ) polynomials. The ZZ polynomials appear to be distinct for each isomer cage, providing a unique label that allows for differentiation between various isomers. Several chemical applications of the computed invariants are reported. The results suggest rather weak correlation between the Kekulé count, Clar count, Clar number invariants, and isomer stability, calling into doubt the predictive power of these topological invariants in discriminating the most stable isomer of a given fullerene. The only exception is the Clar count/Kekulé count ratio, which seems to be the most important diagnostic discovered from our analysis. Stronger correlations are detected between Pauling bond orders computed from Kekulé structures (or Clar covers) and the corresponding equilibrium bond lengths determined from the optimized DFTB geometries of all 30,579 isomers of C20-C70.

Designing building blocks of covalent organic frameworks through on-the-fly batch-based Bayesian optimization

In this work, we use a Bayesian optimization (BO) algorithm to sample the space of covalent organic framework (COF) components aimed at the design of COFs with a high hole conductivity. COFs are crystalline, often porous coordination polymers, where organic molecular units-called building blocks (BBs)-are connected by covalent bonds. Even though we limit ourselves here to a space of three-fold symmetric BBs forming two-dimensional COF sheets, their design space is still much too large to be sampled by traditional means through evaluating the properties of each element in this space from first principles. In order to ensure valid BBs, we use a molecular generation algorithm that, by construction, leads to rigid three-fold symmetric molecules. The BO approach then trains two distinct surrogate models for two conductivity properties, level alignment vs a reference electrode and reorganization free energy, which are combined in a fitness function as the objective that evaluates BBs' conductivities. These continuously improving surrogates allow the prediction of a material's properties at a low computational cost. It thus allows us to select promising candidates which, together with candidates that are very different from the molecules already sampled, form the updated training sets of the surrogate models. In the course of 20 such training steps, we find a number of promising candidates, some being only variations on already known motifs and others being completely novel. Finally, we subject the six best such candidates to a computational reverse synthesis analysis to gauge their real-world synthesizability.

Multipodal Au-C grafting of calix[4]arene molecules on gold nanorods

The interface robustness and spatial arrangement of functional molecules on metallic nanomaterials play a key part in the potential applications of functional nano-objects. The design of mechanically stable and electronically coupled attachments with the underlying metal is essential to bring specific desirable properties to the resulting hybrid materials. In this context, rigid multipodal platforms constitute a unique opportunity for the controllable grafting of functionality. Herein, we provide for the first time an in-depth description of the interface between gold nanorods and a chemically-grafted multipodal platform based on diazonium salts. Thanks to Raman and X-ray photoelectron spectroscopies and theoretical modeling, we deliver insights on the structural and electronic properties of the hybrid material. More importantly, it allows for the accurate assignment of Raman bands. The combination of experimental and theoretical results establishes the formation of four carbon-gold anchors for the calix[4]arene macrocycle leading to the exceptional stability of the functionalized nano-objects. Our results lay the foundations for the future design of robust and versatile platforms.

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD, and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows for deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.

The dynamic nature of electrostatic disorder in organic mixed ionic and electronic conductors

Charge dynamics in disordered media is described invariably assuming that the energy landscape for hopping site energy is stationary. Within the same framework, the correlation between low electronic disorder and high charge mobility is considered extremely robust, despite the emergence of materials with mixed ionic and electronic conductivity (OMIECs) that display high mobility coexisting with large disorder. We show in this work that the disorder of OMIEC polymers is highly dynamical, i.e. the on-site energy for charge transport fluctuates with a characteristic time comparable with that of electron transport. Under these conditions, the disorder of the "frozen" system is not relevant for the charge carrier, whose dynamics are instead controlled by the underlying dynamics of the material. Deep traps exist but have a finite lifetime. The combination of classical simulations and quantum chemical calculations on the nanosecond timescale seems ideal to disclose and characterise the phenomenon.

Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d-Block

First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all d-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory.

A first-principles alternative to empirical solvent parameters

The use of solvents is ubiquitous in chemistry. Empirical parameters, such as the Kamlet-Taft parameters and Gutmann donor/acceptor numbers, have long been used to predict and quantify the effects solvents have on chemical phenomena. Collectively however, such parameters are unsatisfactory, since each describes ultimately the same non-covalent solute-solvent and solute-solute interactions in completely disparate ways. Here we hypothesise that empirical solvent parameters are essentially proxy measures of the electrostatic terms that dominate solvent-solute interactions. On the basis of this hypothesis, we develop a new fundamental descriptor of these interactions, , and show that it is a self-consistent, probe-free, first principles alternative to established empirical solvent parameters.

Dynamic interchange between two protonation states is characteristic of active sites of cholinesterases

Cholinesterases are well-known and widely studied enzymes crucial to human health and involved in neurology, Alzheimer's, and lipid metabolism. The protonation pattern of active sites of cholinesterases influences all the chemical processes within, including reaction, covalent inhibition by nerve agents, and reactivation. Despite its significance, our comprehension of the fine structure of cholinesterases remains limited. In this study, we employed enhanced-sampling quantum-mechanical/molecular-mechanical calculations to show that cholinesterases predominantly operate as dynamic mixtures of two protonation states. The proton transfer between two non-catalytic glutamate residues follows the Grotthuss mechanism facilitated by a mediator water molecule. We show that this uncovered complexity of active sites presents a challenge for classical molecular dynamics simulations and calls for special treatment. The calculated proton transfer barrier of 1.65 kcal/mol initiates a discussion on the potential existence of two coupled low-barrier hydrogen bonds in the inhibited form of butyrylcholinesterase. These findings expand our understanding of structural features expressed by highly evolved enzymes and guide future advances in cholinesterase-related protein and drug design studies.

Impact of 2-hydroxypropyl-β-cyclodextrin inclusion complex formation on dopamine receptor-ligand interaction - A case study

The octanol-water distribution coefficient (logP), used as a measure of lipophilicity, plays a major role in the drug design and discovery processes. While average logP values remain unchanged in approved oral drugs since 1983, current medicinal chemistry trends towards increasingly lipophilic compounds that require adapted analytical workflows and drug delivery systems. Solubility enhancers like cyclodextrins (CDs), especially 2-hydroxypropyl-β-CD (2-HP-β-CD), have been studied in vitro and in vivo investigating their ADMET (adsorption, distribution, metabolism, excretion and toxicity)-related properties. However, data is scarce regarding the applicability of CD inclusion complexes (ICs) in vitro compared to pure compounds. In this study, dopamine receptor (DR) ligands were used as a case study, utilizing a combined in silico/in vitro workflow. Media-dependent solubility and IC stoichiometry were investigated using HPLC. NMR was used to observe IC formation-caused chemical shift deviations while in silico approaches utilizing basin hopping global minimization were used to propose putative IC binding modes. A cell-based in vitro homogeneous time-resolved fluorescence (HTRF) assay was used to quantify ligand binding affinity at the DR subtype 2 (D2R). While all ligands showed increased solubility using 2-HP-β-CD, they differed regarding IC stoichiometry and receptor binding affinity. This case study shows that IC-formation was ligand-dependent and sometimes altering in vitro binding. Therefore, IC complex formation can't be recommended as a general means of improving compound solubility for in vitro studies as they may alter ligand binding.

Host-Guest Interactions of Ruthenium(II) Arene Complexes with Cucurbit[7/8]uril

Cucurbit[n]urils (CB[n]s) have been recognized for their chemical and thermal stability, and their ability to bind many neutral and cationic guest molecules makes them excellent hosts in a range of supramolecular applications. In drug delivery, CB[n]s can enhance drug solubility, improve chemical and physical drug stability, and allow for triggered and controlled release. This study aimed to investigate the ability of CB[7] and CB[8] as molecular hosts to bind ruthenium(II) arene complexes that are current anticancer lead structures in the area of metallodrugs. Both, experimental and computational methods, led to insights into the binding preferences and geometries of [RuII(cym)Cl2]2 (1; cym = η6-p-cymene), [RuII(cym)(dmb)Cl2]) (2; cym = η6-p-cymene; dmb = 1,3-dimethylbenzimidazol-2-ylidene), and [RuII(cym)(pta)Cl2] (3, RAPTA-C; cym = η6-p-cymene; pta = 1,3,5-triaza-7-phospha-adamantane) with CB[7] and CB[8]. Competition experiments by mass spectrometry revealed clear preferences of 2 for CB[8] and 3 for CB[7]. Based on a comparison of the associated interaction energies from quantum chemical calculations as well as experimental data, 3@CB[7] clearly prefers a binding mode, where the pta ligand is located inside the cavity of the host, and the metal ion interacts with two of the carbonyl groups on the rim of CB[7]. In contrast, complex 2 binds in two different orientations with interaction energies similar to those of both CB[n]s, with the cym ligand being either inside or outside of the cavity. These findings suggest that ruthenium(II) arene complexes are able to form stable host-guest interactions with CB[n]s, which can be exploited as drug delivery vehicles in further metallodrug development to improve their chemical stability.

Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method

In this study, we investigate second- and third-harmonic generation processes in Au nanorod systems using the real-time time-dependent density functional tight binding method. Our study focuses on the computation of nonlinear signals based on the time dependent dipole response induced by linearly polarized laser pulses interacting with nanoparticles. We systematically explore the influence of various laser parameters, including pump intensity, duration, frequency, and polarization directions, on harmonic generation. We demonstrate all the results using Au nanorod dimer systems arranged in end-to-end configurations, and disrupting the spatial symmetry of regular single nanorod systems is crucial for second-harmonic generation processes. Furthermore, we study the impact of nanorod lengths, which lead to variable plasmon energies, on harmonic generation, and estimates of polarizabilities and hyper-polarizabilities are provided.

Nonadiabatic Exciton Dynamics and Energy Gradients in the Framework of FMO-LC-TDDFTB

We introduce a novel methodology for simulating the excited-state dynamics of extensive molecular aggregates in the framework of the long-range corrected time-dependent density-functional tight-binding fragment molecular orbital method (FMO-LC-TDDFTB) combined with the mean-field Ehrenfest method. The electronic structure of the system is described in a quasi-diabatic basis composed of locally excited and charge-transfer states of all fragments. In order to carry out nonadiabatic molecular dynamics simulations, we derive and implement the excited-state gradients of the locally excited and charge-transfer states. Subsequently, the accuracy of the analytical excited-state gradients is evaluated. The applicability to the simulation of exciton transport in organic semiconductors is illustrated on a large cluster of anthracene molecules. Additionally, nonadiabatic molecular dynamics simulations of a model system of benzothieno-benzothiophene molecules highlight the method's utility in studying charge-transfer dynamics in organic materials. Our new methodology will facilitate the investigation of excitonic transfer in extensive biological systems, nanomaterials, and other complex molecular systems consisting of thousands of atoms.

Efficient Parameterization of Density Functional Tight-Binding for 5f-Elements: A Th-O Case Study

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th-O system, which was subsequently applied to the study of ThO2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO2. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

Exploring fracture of H-BN and graphene by neural network force fields

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture often lead to catastrophic failure of materials and structures. Understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Atomistic simulations offer 'first-principles' tools to resolve the progressive microstructural changes at crack fronts and are widely used to explore the underlying processes of mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g. kinking, branching). Empirical force fields developed based on atomic-level structural descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, especially for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of bond-breaking and (re)formation events during fracture, which, unfortunately, have not been taken into account in either the state-of-the-art empirical or machine-learning force fields. Based on data generated by density functional theory calculations, we report a neural network-based force field for fracture (NN-F3) constructed by using the end-to-end symmetry preserving framework of deep potential-smooth edition (DeepPot-SE). The workflow combines pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F3is demonstrated by studying the rupture of hexagonal boron nitride (h-BN) and twisted bilayer graphene as model problems. The simulation results elucidate the roughening physics of fracture defined by the lattice asymmetry in h-BN, explaining recent experimental findings, and predict the interaction between cross-layer cracks in twisted graphene bilayers, which leads to a toughening effect.

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

HYPOTHESIS

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

SIMULATIONS

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

FINDINGS

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

Theory for nonlinear conductivity switching in semiconducting organic ferroelectrics

In this work, the ferroelectric and semiconducting properties of the organic semiconducting ferroelectric benzotrithiophene tricarboxamide (BTTTA), and especially their nonlinear coupling, are theoretically investigated. BTTTA is an exponent of a small class of semiconducting organic ferroelectrics for which experiments have established a surprising polarization direction dependence of the bulk conductivity at finite fields. First, molecular dynamics (MD) simulations are used to investigate the occurrence and, under the influence of an external electric field, the inversion of the macroscopic electric dipole that forms along the axis of supramolecular columns of BTTTA. The MD results are consistent with the experimentally observed ferroelectric behavior of the material. Building on the MD results, a QM/MM scheme is used to investigate the charge carrier mobility in the quasi-1D BTTTA stacks in the linear and non-linear regimes. Indeed, at finite electric fields, a clear resistance switching effect was observed in the form of a hole mobility that is a factor ∼2 larger for antiparallel orientations of the polarization and field than for a parallel orientation. This phenomenon can be understood as a microscopic ratchet that is based on the non-equilibrium interaction between the (oriented) dipoles and the (direction of the) charge transport.

Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment

This roadmap reviews the new, highly interdisciplinary research field studying the behavior of condensed matter systems exposed to radiation. The Review highlights several recent advances in the field and provides a roadmap for the development of the field over the next decade. Condensed matter systems exposed to radiation can be inorganic, organic, or biological, finite or infinite, composed of different molecular species or materials, exist in different phases, and operate under different thermodynamic conditions. Many of the key phenomena related to the behavior of irradiated systems are very similar and can be understood based on the same fundamental theoretical principles and computational approaches. The multiscale nature of such phenomena requires the quantitative description of the radiation-induced effects occurring at different spatial and temporal scales, ranging from the atomic to the macroscopic, and the interlinks between such descriptions. The multiscale nature of the effects and the similarity of their manifestation in systems of different origins necessarily bring together different disciplines, such as physics, chemistry, biology, materials science, nanoscience, and biomedical research, demonstrating the numerous interlinks and commonalities between them. This research field is highly relevant to many novel and emerging technologies and medical applications.

Chemical Bonding and Crystal Structure Schemes in Atomic/Molecular Layer Deposited Fe-Terephthalate Thin Films

Advanced deposition routes are vital for the growth of functional metal-organic thin films. The gas-phase atomic/molecular layer deposition (ALD/MLD) technique provides solvent-free and uniform nanoscale thin films with unprecedented thickness control and allows straightforward device integration. Most excitingly, the ALD/MLD technique can enable the in situ growth of novel crystalline metal-organic materials. An exquisite example is iron-terephthalate (Fe-BDC), which is one of the most appealing metal-organic framework (MOF) type materials and thus widely studied in bulk form owing to its attractive potential in photocatalysis, biomedicine, and beyond. Resolving the chemistry and structural features of new thin film materials requires an extended selection of characterization and modeling techniques. Here we demonstrate how the unique features of the ALD/MLD grown in situ crystalline Fe-BDC thin films, different from the bulk Fe-BDC MOFs, can be resolved through techniques such as synchrotron grazing-incidence X-ray diffraction (GIXRD), Mössbauer spectroscopy, and resonant inelastic X-ray scattering (RIXS) and crystal structure predictions. The investigations of the Fe-BDC thin films, containing both trivalent and divalent iron, converge toward a novel crystalline Fe(III)-BDC monoclinic phase with space group C2/c and an amorphous Fe(II)-BDC phase. Finally, we demonstrate the excellent thermal stability of our Fe-BDC thin films.

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; in another, DFTB+receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+ or enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields

We present software infrastructure for the design and testing of new quantum mechanical/molecular mechanical and machine-learning potential (QM/MM-ΔMLP) force fields for a wide range of applications. The software integrates Amber's molecular dynamics simulation capabilities with fast, approximate quantum models in the xtb package and machine-learning potential corrections in DeePMD-kit. The xtb package implements the recently developed density-functional tight-binding QM models with multipolar electrostatics and density-dependent dispersion (GFN2-xTB), and the interface with Amber enables their use in periodic boundary QM/MM simulations with linear-scaling QM/MM particle-mesh Ewald electrostatics. The accuracy of the semiempirical models is enhanced by including machine-learning correction potentials (ΔMLPs) enabled through an interface with the DeePMD-kit software. The goal of this paper is to present and validate the implementation of this software infrastructure in molecular dynamics and free energy simulations. The utility of the new infrastructure is demonstrated in proof-of-concept example applications. The software elements presented here are open source and freely available. Their interface provides a powerful enabling technology for the design of new QM/MM-ΔMLP models for studying a wide range of problems, including biomolecular reactivity and protein-ligand binding.

Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4'-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.

Computational analysis of R-X oxidative addition to Pd nanoparticles

Oxidative addition (OA) is a necessary step in mechanisms of widely used synthetic methodologies such as the Heck reaction, cross-coupling reactions, and the Buchwald-Hartwig amination. This study pioneers the exploration of OA of aryl halide to palladium nanoparticles (NPs), a process previously unaddressed in contrast to the activity of well-studied Pd(0) complexes. Employing DFT modeling and semi-empirical metadynamics simulations, the oxidative addition of phenyl bromide to Pd nanoparticles was investigated in detail. Energy profiles of oxidative addition to Pd NPs were analyzed and compared to those involving Pd(0) complexes forming under both ligand-stabilized (phosphines) and ligandless (amine base) conditions. Metadynamics simulations highlighted the edges of the (1 1 1) facets of Pd NPs as the key element of oxidative addition activity. We demonstrate that OA to Pd NPs is not only kinetically facile at ambient temperatures but also thermodynamically favorable. This finding accentuates the necessity of incorporating OA to Pd NPs in future investigations, thus providing a more realistic view of the involved catalytic mechanisms. These results enhance the understanding of aryl halide (cross-)coupling reactions, reinforcing the concept of a catalytic "cocktail". This concept posits dynamic interconversions between diverse active and inactive centers, collectively affecting the outcome of the reaction. High activity of Pd NPs in direct C-X activation paves the way for novel approaches in catalysis, potentially enhancing the field and offering new catalytic pathways to consider.

Unveiling a New 2D Semiconductor: Biphenylene-Based InN

The two-dimensional (2D) materials class earned a boost in 2021 with biphenylene synthesis, which is structurally formed by the fusion of four-, six-, and eight-membered carbon rings, usually named 4-6-8-biphenylene network (BPN). This research proposes a detailed study of electronic, structural, dynamic, and mechanical properties to demonstrate the potential of the novel biphenylene-like indium nitride (BPN-InN) via density functional theory and molecular dynamics simulations. The BPN-InN has a direct band gap energy transition of 2.02 eV, making it promising for optoelectronic applications. This structure exhibits maximum and minimum Young modulus of 22.716 and 22.063 N/m, Poisson ratio of 0.018 and -0.008, and Shear modulus of 11.448 and 10.860 N/m, respectively. To understand the BPN-InN behavior when subjected to mechanical deformations, biaxial and uniaxial strains in armchair and zigzag directions from -8 to 8% were applied, achieving a band gap energy modulation of 1.36 eV over tensile deformations. Our findings are expected to motivate both theorists and experimentalists to study and obtain these new 2D inorganic materials that exhibit promising semiconductor properties.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Obtaining Robust Density Functional Tight-Binding Parameters for Solids across the Periodic Table

The density functional tight-binding (DFTB) approach allows electronic structure-based simulations at length and time scales far beyond what is possible with first-principles methods. This is achieved by using minimal basis sets and empirical approximations. Unfortunately, the sparse availability of parameters across the periodic table is a significant barrier to the use of DFTB in many cases. We therefore propose a workflow that allows the robust and consistent parametrization of DFTB across the periodic table. Importantly, our approach requires no element-pairwise parameters so that the parameters can be used for all element combinations and are readily extendable. This is achieved by parametrizing all elements on a consistent set of artificial homoelemental crystals, spanning a wide range of coordination environments. The transferability of the resulting periodic table baseline parameters to multielement systems and unknown structures is explored and the model is extensively benchmarked against previous specialized and general DFTB parametrizations.

Interfacial Charge Transfer in Photoexcited QD-Molecule Composite of Tetrahedral CdSe Quantum Dot Coupled with Carbazole

Photoexcited charge transfer dynamics in CdSe quantum dots (QDs) coupled with carbazole were explored to model QD-molecule systems for light-harvesting applications. The absorption spectra of QDs with different sizes, i.e., Cd35Se20X30L30 (T1), Cd56Se35X42L42 (T2), and Cd84Se56X56L56 (T3) were simulated with quantum dynamical methods, which qualitatively match the reported experimental spectra. The carbazole is attached with a 3-amino group at the apex position of T1 (namely T1-3A-Cz), establishing proper electronic communication between T1 and carbazole. The spectra of T1-3A-Cz is 0.22 eV red-shifted compared to T1. A time-dependent perturbation was applied in tune with the lowest energy peak (3.63 eV) of T1-3A-Cz to investigate the charge transfer dynamics, which revealed an ultrafast charge separation within the femtosecond time scale. The electronic structure showed a favorable energy alignment between T1 and carbazole in T1-3A-Cz. The LUMO of carbazole was situated below the conduction band of the QD, while the HOMO of carbazole mixed perfectly with the top of the valence band of the QD, developing the interfacial charge transfer states. These states promoted the photoexcited electron transfer directly from the CdSe core to carbazole. A rapid and enhanced charge separation occurred with the laser field strength increasing from 0.001 to 0.005 V/Å. However, T1 connected to the other positions of carbazole did not show charge separation effectively. The photoinduced charge transfer is negligible in the case of T2-carbazole systems due to poor electronic coupling, and it is not observed in T3-carbazole systems. So, the T1-3A-Cz model acts as a perfect donor-acceptor QD-molecule nanocomposite that can harvest photon energy efficiently. Further enhancement of charge transfer can be achieved by coupling more carbazoles to the T1 QD (e.g., T1-3A-Cz2) due to the extension of hole delocalization between T1 and the carbazoles.

Intra-strand phosphate-mediated pathways in microsolvated double-stranded DNA

We argue that dry DNA charge transport in molecular junctions, over distances of tens of nanometers, can take place via independent intra-strand pathways involving the phosphate groups. Such pathways explain recent single-molecule experiments that compare currents in intact and nicked 100 base-pair double-stranded DNA. We explore the conditions that favor independent intra-strand transport channels with the participation of the phosphate groups, as opposed to purely base-mediated transport involving the pi-stacked bases and inter-strand transitions. Our computations demonstrate how long-distance transport pathways in DNA are tuned by the degree of solvation, which affects the level of dynamic disorder in the pi-stacking, and the energies of phosphate-group molecular orbitals.

Thermal isomerization rates in retinal analogues using Ab-Initio molecular dynamics

For a detailed understanding of chemical processes in nature and industry, we need accurate models of chemical reactions in complex environments. While Eyring transition state theory is commonly used for modeling chemical reactions, it is most accurate for small molecules in the gas phase. A wide range of alternative rate theories exist that can better capture reactions involving complex molecules and environmental effects. However, they require that the chemical reaction is sampled by molecular dynamics simulations. This is a formidable challenge since the accessible simulation timescales are many orders of magnitude smaller than typical timescales of chemical reactions. To overcome these limitations, rare event methods involving enhanced molecular dynamics sampling are employed. In this work, thermal isomerization of retinal is studied using tight-binding density functional theory. Results from transition state theory are compared to those obtained from enhanced sampling. Rates obtained from dynamical reweighting using infrequent metadynamics simulations were in close agreement with those from transition state theory. Meanwhile, rates obtained from application of Kramers' rate equation to a sampled free energy profile along a torsional dihedral reaction coordinate were found to be up to three orders of magnitude higher. This discrepancy raises concerns about applying rate methods to one-dimensional reaction coordinates in chemical reactions.

Effective nitrogen doping of TiO2 polymorphs at mild temperatures for visible-light-responsive hydrogen evolution

Herein, a mild-temperature nitrogen doping route with the urea-derived gaseous species as the active doping agent is proposed to realize visible-light-responsive photocatalytic hydrogen evolution both for the anatase and rutile TiO2. DFT simulations reveal that the cyanic acid (HOCN), derived from the decomposition of urea, plays a curial role in the effective doping of nitrogen in TiO2 at mild temperatures. Photocatalytic performance demonstrates that both the anatase and rutile TiO2 doped at mild temperatures exhibit the highest hydrogen evolution rates, although the ones prepared at high temperatures possess higher absorbance in the visible range. Steady-state and transient surface photovoltage characterizations of these doped TiO2 polymorphs prepared at different temperatures reveal that harsh conditions (high temperature reaction) typically result in the formation of intrinsic defects that are detrimental to the transport of the low-energy visible-light-induced electrons, while the mild-temperature nitrogen-doping could flatten the pristine upward band bending without triggering the formation of Ti3+, thus achieving enhanced visible-light-responsive hydrogen evolution rates. We anticipate that our findings will provide inspiring information for shrinking the gap between the visible-light-absorbance and the visible-light-responsiveness in the band engineering of wide-bandgap metal-oxide photocatalysts.

Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials

We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM-ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client-server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM-ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications.

SCINE-Software for chemical interaction networks

The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt, or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator are as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available as open source and free of charge.

Plugin-based interoperability and ecosystem management for the MolSSI Driver Interface Project

The MolSSI Driver Interface (MDI) Project is an effort to simplify and standardize the process of enabling tight interoperability between independently developed code bases and is supported by numerous software packages across the domain of chemical physics. It enables a wide variety of use cases, including quantum mechanics/molecular mechanics, advanced sampling, path integral molecular dynamics, machine learning, ab initio molecular dynamics, etc. We describe two major developments within the MDI Project that provide novel solutions to key interoperability challenges. The first of these is the development of the MDI Plugin System, which allows MDI-supporting libraries to be used as highly modular plugins, with MDI enforcing a standardized application programming interface across plugins. Codes can use these plugins without linking against them during their build process, and end-users can select which plugin(s) they wish to use at runtime. The MDI Plugin System features a sophisticated callback system that allows codes to interact with plugins on a highly granular level and represents a significant advancement toward increased modularity among scientific codes. The second major development is MDI Mechanic, an ecosystem management tool that utilizes Docker containerization to simplify the process of developing, validating, maintaining, and deploying MDI-supporting codes. Additionally, MDI Mechanic provides a framework for launching MDI simulations in which each interoperating code is executed within a separate computational environment. This eliminates the need to compile multiple production codes within a single computational environment, reducing opportunities for dependency conflicts and lowering the barrier to entry for users of MDI-enabled codes.

Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials

Rare tautomeric forms of nucleobases can lead to Watson-Crick-like (WC-like) mispairs in DNA, but the process of proton transfer is fast and difficult to detect experimentally. NMR studies show evidence for the existence of short-time WC-like guanine-thymine (G-T) mispairs; however, the mechanism of proton transfer and the degree to which nuclear quantum effects play a role are unclear. We use a B-DNA helix exhibiting a wGT mispair as a model system to study tautomerization reactions. We perform ab initio (PBE0/6-31G*) quantum mechanical/molecular mechanical (QM/MM) simulations to examine the free energy surface for tautomerization. We demonstrate that while the ab initio QM/MM simulations are accurate, considerable sampling is required to achieve high precision in the free energy barriers. To address this problem, we develop a QM/MM machine learning potential correction (QM/MM-ΔMLP) that is able to improve the computational efficiency, greatly extend the accessible time scales of the simulations, and enable practical application of path integral molecular dynamics to examine nuclear quantum effects. We find that the inclusion of nuclear quantum effects has only a modest effect on the mechanistic pathway but leads to a considerable lowering of the free energy barrier for the GT*⇌G*T equilibrium. Our results enable a rationalization of observed experimental data and the prediction of populations of rare tautomeric forms of nucleobases and rates of their interconversion in B-DNA.

Reinforcement learning for in silico determination of adsorbate-substrate structures

Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q-learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2-amino-acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host-guest interactions between phenylboronic acid and β-cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)-(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q-matrix or Q-table for better decision making (policy) on next actions. Finally, we have developed a transfer-learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels.

Recent advances in computational and experimental protein-ligand affinity determination techniques

INTRODUCTION

Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved.

AREAS COVERED

In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design.

EXPERT OPINION

The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.

Porous isoreticular non-metal organic frameworks

Metal-organic frameworks (MOFs) are useful synthetic materials that are built by the programmed assembly of metal nodes and organic linkers1. The success of MOFs results from the isoreticular principle2, which allows families of structurally analogous frameworks to be built in a predictable way. This relies on directional coordinate covalent bonding to define the framework geometry. However, isoreticular strategies do not translate to other common crystalline solids, such as organic salts3-5, in which the intermolecular ionic bonding is less directional. Here we show that chemical knowledge can be combined with computational crystal-structure prediction6 (CSP) to design porous organic ammonium halide salts that contain no metals. The nodes in these salt frameworks are tightly packed ionic clusters that direct the materials to crystallize in specific ways, as demonstrated by the presence of well-defined spikes of low-energy, low-density isoreticular structures on the predicted lattice energy landscapes7,8. These energy landscapes allow us to select combinations of cations and anions that will form thermodynamically stable, porous salt frameworks with channel sizes, functionalities and geometries that can be predicted a priori. Some of these porous salts adsorb molecular guests such as iodine in quantities that exceed those of most MOFs, and this could be useful for applications such as radio-iodine capture9-12. More generally, the synthesis of these salts is scalable, involving simple acid-base neutralization, and the strategy makes it possible to create a family of non-metal organic frameworks that combine high ionic charge density with permanent porosity.

Protein Effects on the Excitation Energies and Exciton Dynamics of the CP24 Antenna Complex

In this study, the site energy fluctuations, energy transfer dynamics, and some spectroscopic properties of the minor light-harvesting complex CP24 in a membrane environment were determined. For this purpose, a 3 μs-long classical molecular dynamics simulation was performed for the CP24 complex. Furthermore, using the density functional tight binding/molecular mechanics molecular dynamics (DFTB/MM MD) approach, we performed excited state calculations for the chlorophyll a and chlorophyll b molecules in the complex starting from five different positions of the MD trajectory. During the extended simulations, we observed variations in the site energies of the different sets as a result of the fluctuating protein environment. In particular, a water coordination to Chl-b 608 occurred only after about 1 μs in the simulations, demonstrating dynamic changes in the environment of this pigment. From the classical and the DFTB/MM MD simulations, spectral densities and the (time-dependent) Hamiltonian of the complex were determined. Based on these results, three independent strongly coupled chlorophyll clusters were revealed within the complex. In addition, absorption and fluorescence spectra were determined together with the exciton relaxation dynamics, which reasonably well agrees with experimental time scales.

In Search of the Best Low-Cost Methods for Efficient Screening of Conformers

Locating the lowest energy conformer is crucial for the accurate computation of equilibrium properties of molecular systems. This paper examines the performance of efficient low-cost methods in terms of the alignment and relative energies of their energy minima against the benchmark revDSD-PBEP86-D4/def2-TZVPP//MP2/cc-pVTZ potential energy surface. The low-cost methods considered include GFN-FF, GFN2-xTB, DFTB3, HF-3c, B97-3c, PBEh-3c, and r2SCAN-3c composite methods against a diverse test set of 20 compounds including alkanes, perfluoroalkyl molecules, peptides, open-shell radicals, and Zn(II) complexes of varying sizes. The "3c" composite methods are generally more accurate, but are at least 2-3 orders of magnitude more expensive than tight-binding methods which have energy minima that align well with the benchmark potential energy surface. The findings of this paper were further exploited to introduce a simple strategy involving Grimme's CENSO energy-sorting algorithm that resulted in up to an order of magnitude reduction in computational time for locating the lowest energy conformer on the revDSD-PBEP86-D4/def2-TZVPP//MP2/cc-pVTZ surface.

Physical Prior Mean Function-Driven Gaussian Processes Search for Minimum-Energy Reaction Paths with a Climbing-Image Nudged Elastic Band: A General Method for Gas-Phase, Interfacial, and Bulk-Phase Reactions

The climbing-image nudged elastic band (CI-NEB) method serves as an indispensable tool for computational chemists, offering insight into minimum-energy reaction paths (MEPs) by delineating both transition states (TSs) and intermediate nonstationary structures along reaction coordinates. However, executing CI-NEB calculations for reactions with extensive reaction coordinate spans necessitates a large number of images to ensure a reliable convergence of the MEPs and TS structures, presenting a computationally demanding optimization challenge, even with mildly costly electronic-structure methods. In this study, we advocate for the utilization of physically inspired prior mean function-based Gaussian processes (GPs) to expedite MEP exploration and TS optimization via the CI-NEB method. By incorporating reliable prior physical approximations into potential energy surface (PES) modeling, we demonstrate enhanced efficiency in multidimensional CI-NEB optimization with surrogate-based optimizers. Our physically informed GP approach not only outperforms traditional nonsurrogate-based optimizers in optimization efficiency but also on-the-fly learns the reaction path valley during optimization, culminating in significant advancements. The surrogate PES derived from our optimization exhibits high accuracy compared to true PES references, aligning with our emphasis on leveraging reliable physical priors for robust and efficient posterior mean learning in GPs. Through a systematic benchmark study encompassing various reaction pathways, including gas-phase, bulk-phase, and interfacial/surface reactions, our physical GPs consistently demonstrate superior efficiency and reliability. For instance, they outperform the popular fast inertial relaxation engine optimizer by approximately a factor of 10, showcasing their versatility and efficacy in exploring reaction mechanisms and surface reaction PESs.

Rotational Dynamics of Water near Osmolytes by Molecular Dynamics Simulations

The behavior of water molecules around organic molecules has attracted considerable attention as a crucial factor influencing the properties and functions of soft matter and biomolecules. Recently, it has been suggested that the change in protein stability upon the addition of small organic molecules (osmolytes) is dominated by the change in the water dynamics caused by the osmolyte, where the dynamics of not only the directly interacting water molecules but also the long-range hydration layer affect the protein stability. However, the relation between the long-range structure of hydration water in various solutions and the water dynamics remains unclear at the molecular level. We performed density-functional tight-binding molecular dynamics simulations to elucidate the varying rotational dynamics of water molecules in 15 osmolyte solutions. A positive correlation was observed between the rotational relaxation time and our proposed normalized parameter obtained by dividing the number of hydrogen bonds between water molecules by the number of nearest-neighbor water molecules. For the 15 osmolyte solutions, an increase or a decrease in the value of the normalized parameter for the second hydration shell tended to result in water molecules with slow and fast rotational dynamics, respectively, thus illustrating the importance of the second hydration shell for the rotational dynamics of water molecules. Our simulation results are anticipated to advance the current understanding of water dynamics around organic molecules and the long-range structure of water molecules.

The impact of alcohol and ammonium fluoride on pressure-induced amorphization of cubic structure I clathrate hydrates

We have investigated pressure-induced amorphization (PIA) of an alcohol clathrate hydrate (CH) of cubic structure type I (sI) in the presence of NH4F utilizing dilatometry and x-ray powder diffraction. PIA occurs at 0.98 GPa at 77 K, which is at a much lower pressure than for other CHs of the same structure type. The amorphized CH also shows remarkable resistance against crystallization upon decompression. While amorphized sI CHs could not be recovered previously at all, this is possible in the present case. By contrast to other CHs, the recovery of the amorphized CHs to ambient pressure does not even require a high-pressure annealing step, where recovery without any loss of amorphicity is possible at 120 K and below. Furthermore, PIA is accessible upon compression at unusually high temperatures of up to 140 K, where it reaches the highest degree of amorphicity. Molecular dynamics simulations confirm that polar alcoholic guests, as opposed to non-polar guests, induce cage deformation at lower pressure. The substitution of NH4F into the host-lattice stabilizes the collapsed state more than the crystalline state, thereby enhancing the collapse kinetics and lowering the pressure of collapse.

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.