On the brink of self-hydration: the water heptadecamer.

For pure, neutral, isolated molecular clusters, (H2O)17 marks the transition from structures with all water molecules on the cluster surface to water self-hydration, i.e., cluster structures around one central water molecule. Getting this right with water model potentials turns out to be challenging. Even the best water potentials currently available, which reproduce collective properties very well, still deliver contradicting results for (H2O)17, when different low-energy isomers from global structure optimizations are examined. Interestingly, ab initio quantum chemistry also struggles with the only seemingly simple question if (H2O)17 is all-surface or water-centered. Hence, although the long history of water potential development may be entering its final phase, it is not quite finished yet.

Comparison of Implicit and Explicit Solvent Approaches in Ab Initio Evaluation of Thermochemistry in Solution: Application in Studying Boron Isotope Fractionation in Water.

Evaluation of thermochemistry in solution plays a key role in numerous fields. For this task, the solvent effects are commonly included in theoretical computations based on either implicit or explicit solvent approaches. In the present study, we evaluate and compare the performance of some of the most widely applied methods based on these two approaches. For studying the solvent effect on thermochemistry with an explicit solvent, we demonstrate that partial normal mode analysis with frozen geometry of solvent molecules for multiple solute-solvent configurations can yield quite accurate and reliable results for a drastically reduced computational cost. As a case study, we consider the evaluation of the equilibrium constant for the boron isotope exchange between boric acid and borate (k3-4) in pure and saline water which is of high geochemical importance. Employing three different rigorous and high-precision theoretical approaches, we provide a reliable estimation of k3-4 which is a value within 1.028 to 1.030 for both pure and saline water which is in excellent agreement with experimental data.

Dependence of Vaporization Enthalpy on Molecular Surfaces and Temperature: Thermodynamically Effective Molecular Surfaces.

Approximation of molecular surfaces is of central importance in numerous scientific fields. In this study we theoretically derive a physical model to relate phase-change thermodynamics to molecular surfaces. The model allows accurately predicting vaporization enthalpy of compounds for a wide temperature range without requiring any empirical parameter. Through the new model, we conceptualize thermodynamically effective molecular surfaces and show that they, although only marginally different than van der Waals surfaces, substantially improve predictability of multiple thermodynamic quantities.

Graph-based Automated Macro-Molecule Assembly.

We present a general molecular framework assembly algorithm that takes a largely arbitrary molecular fragment database and a user-supplied target template graph as input. Automatic assembly of molecular fragments from the database, following a prescribed, user-supplied set of connection rules, then turns the template graph into an actual, chemically reasonable molecular framework. Assembly capabilities of our algorithm are tested by producing several abstract, closed-loop shapes. To indicate a few of many possible application areas we demonstrate a host-guest complex and a road toward catalysis. Postassembly substituent exchange can be used to produce electric fields of desired values at desired points inside the framework or at its surface as a stepping stone toward rationally designed, artificial heterogeneous catalysts.

Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning.

Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of non-covalent interaction energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets.

Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model.

Theoretical estimation of solvation free energy by continuum solvation models, as a standard approach in computational chemistry, is extensively applied by a broad range of scientific disciplines. Nevertheless, the current widely accepted solvation models are either inaccurate in reproducing experimentally determined solvation free energies or require a number of macroscopic observables which are not always readily available. In the present study, we develop and introduce the Machine-Learning Polarizable Continuum solvation Model (ML-PCM) for a substantial improvement of the predictability of solvation free energy. The performance and reliability of the developed models are validated through a rigorous and demanding validation procedure. The ML-PCM models developed in the present study improve the accuracy of widely accepted continuum solvation models by almost one order of magnitude with almost no additional computational costs. A freely available software is developed and provided for a straightforward implementation of the new approach.

Globally optimal catalytic fields for a Diels-Alder reaction.

In a previous paper [M. Dittner and B. Hartke, J. Chem. Theory Comput. 14, 3547 (2018)], we introduced a preliminary version of our GOCAT (globally optimal catalyst) concept in which electrostatic catalysts are designed for arbitrary reactions by global optimization of distributed point charges that surround the reaction. In this first version, a pre-defined reaction path was kept fixed. This unrealistic assumption allowed for only small catalytic effects. In the present work, we extend our GOCAT framework by a sophisticated and robust on-the-fly reaction path optimization, plus further concomitant algorithm adaptions. This allows smaller and larger excursions from a pre-defined reaction path under the influence of the GOCAT point-charge surrounding, all the way to drastic mechanistic changes. In contrast to the restricted first GOCAT version, this new version is able to address real-life catalysis. We demonstrate this by applying it to the electrostatic catalysis of a prototypical Diels-Alder reaction. Without using any prior information, this procedure re-discovers theoretically and experimentally established features of electrostatic catalysis of this very reaction, including a field-dependent transition from the synchronous, concerted textbook mechanism to a zwitterionic two-step mechanism, and diastereomeric discrimination by suitable electric field components.

Exploring self-organization of molecular tether molecules on a gold surface by global structure optimization.

We employ nondeterministic global cluster structure optimization, based on the evolutionary algorithms paradigm, to model the self-assembly of complex molecules on a surface. As a real-life application example directly related to many recent experiments, we use this approach for the assembly of triazatriangulene "platform" molecules on the Au(111) surface. Without additional restrictions like spatial discretizations, coarse-graining or precalculated adsorption poses, and despite the proof-of-principle character of this study, we achieve satisfactory qualitative agreement with several experimental observations and can provide answers to questions that experiments on these species had left open so far. © 2019 Wiley Periodicals, Inc.

Axitinib: A Photoswitchable Approved Tyrosine Kinase Inhibitor.

The goal of photopharmacology is to develop photoswitchable enzyme modulators as tunable (pro-)drugs that can be spatially and temporally controlled by light. In this context, the tyrosine kinase inhibitor axitinib, which contains a photosensitive stilbene-like moiety that allows for E/Z isomerization, is of interest. Axitinib is an approved drug that targets the vascular endothelial growth factor receptor 2 (VEGFR2) and is licensed for second-line therapy of renal cell carcinoma. The photoinduced E/Z isomerization of axitinib has been investigated to explore if its inhibitory effect can be turned "on" and "off", as triggered by light. Under controlled light conditions, (Z)-axitinib is 43 times less active than that of the E isomer in an VEGFR2 assay. Furthermore, it was proven that kinase activity in human umbilical vein cells (HUVECs) was decreased by (E)-axitinib, but only weakly affected by (Z)-axitinib. By irradiating (Z)-axitinib in vitro with UV light (λ=385 nm), it is possible to switch it almost quantitatively into the E isomer and to completely restore the biological activity of (E)-axitinib. However, switching the biological activity off from (E)- to (Z)-axitinib was not possible in aqueous solution due to a competing irreversible [2+2]-photocycloaddition, which yielded a biologically inactive axitinib dimer.

Globally Optimal Catalytic Fields - Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration.

The search for, and understanding of, good catalysts for chemical reactions is a central issue for chemists. Here, we present first steps toward developing a general computational framework to better support this task. This framework combines efficient, unbiased global optimization techniques with an abstract representation of the catalytic environment, to shrink the search space. To analyze the resulting catalytic embeddings, we employ dimensionality reduction and clustering techniques. This not only provides an inverse design approach to new catalytic embeddings but also illuminates the actual interactions behind catalytic effects. All this is illustrated here with a strictly electrostatic model for the environment and with two versions of a selected example reaction. We close with detailed discussions of future improvements of our framework.

Resonance dynamics of DCO (X̃ 2A') simulated with the dynamically pruned discrete variable representation (DP-DVR).

Selected resonance states of the deuterated formyl radical in the electronic ground state X̃ 2A' are computed using our recently introduced dynamically pruned discrete variable representation [H. R. Larsson, B. Hartke, and D. J. Tannor, J. Chem. Phys. 145, 204108 (2016)]. Their decay and asymptotic distributions are analyzed and, for selected resonances, compared to experimental results obtained by a combination of stimulated emission pumping and velocity-map imaging of the product D atoms. The theoretical results show good agreement with the experimental kinetic energy distributions. The intramolecular vibrational energy redistribution is analyzed and compared with previous results from an effective polyad Hamiltonian. Specifically, we analyzed the part of the wavefunction that remains in the interaction region during the decay. The results from the polyad Hamiltonian could mainly be confirmed. The C=O stretch quantum number is typically conserved, while the D-C=O bend quantum number decreases. Differences are due to strong anharmonic coupling such that all resonances have major contributions from several zero-order states. For some of the resonances, the coupling is so strong that no further zero-order states appear during the dynamics in the interaction region, even after propagating for 300 ps.

Cluster structures influenced by interaction with a surface.

Clusters on surfaces are vitally important for nanotechnological applications. Clearly, cluster-surface interactions heavily influence the preferred cluster structures, compared to clusters in vacuum. Nevertheless, systematic explorations and an in-depth understanding of these interactions and how they determine the cluster structures are still lacking. Here we present an extension of our well-established non-deterministic global optimization package OGOLEM from isolated clusters to clusters on surfaces. Applying this approach to intentionally simple Lennard-Jones test systems, we produce a first systematic exploration that relates changes in cluster-surface interactions to resulting changes in adsorbed cluster structures.

Simulations of optically switchable molecular machines for particle transport.

A promising application for design and deployment of molecular machines is nanoscale transport, driven by artificial cilia. In this contribution, we present several further steps toward this goal, beyond our first-generation artificial cilium (Raeker et al., J. Phys. Chem. A 2012, 116, 11241). Promising new azobenzene-derivatives were tested for use as cilium motors. Using a QM/MM partitioning in on-the-fly photodynamics, excited-state surface-hopping trajectories were calculated for each isomerization direction and each motor version. The methods used were reparametrized semiempirical quantum chemistry together with floating-occupation configuration interaction as the QM part and the OPLSAA-L forcefield as MM part. In addition, we simulated actual particle transport by a single cilium attached to a model surface, with varying attachment strengths and modes, and with transport targets ranging from single atoms to multi-molecule arrangements. Our results provide valuable design guidelines for cilia-driven nanoscale transport and emphasize the need to carefully select the whole setup (not just the cilium itself, but also its surface attachment and the dynamic cilium-target interaction) to achieve true transport. © 2018 Wiley Periodicals, Inc.

Ultrafast dynamics of UV-excited trans- and cis-ferulic acid in aqueous solutions.

The ultrafast UV-induced processes of the neutral, anionic and dianionic forms of trans- and cis-ferulic acid (FA) in aqueous solution were studied by static and femtosecond time-resolved emission and absorption spectroscopy combined with quantum chemical calculations. In all cases, initial excitation populates the first 1ππ* state. For the dianionic cis-isomer cFA2-, electronic deactivation takes place with a time constant of only 1.4 ps, whereas in all other cases, excited-state deactivation happens more than ten times slower, on a time scale of ≈20 ps. The data suggest sequential de-excitation pathways, where initial sub-picosecond solvent rearrangement and structural changes are followed by internal conversion to an intermediate excited electronic state from which deactivation to the ground state proceeds. Considering the time scales, barrierless excited-state pathways are suggested only in the case of cFA2-, where the observed formation of the isomerisation photoproduct tFA2- provides clear evidence for a cis ⇄ trans isomerisation coordinate. In the other cases, pathways with an excited-state energy barrier, presumably along the same coordinate, are likely, given the longer excited-state lifetimes.

Full-Dimensional Excited-State Intramolecular Proton Transfer Dynamics of Salicylic Acid.

Salicylic acid (SAc) and its excited-state intramolecular proton transfer (ESIPT) capabilities have been studied both experimentally and theoretically by static calculations. However, to our knowledge, no radiationless pathway has been proposed so far. Instead, excited-state deactivation was only investigated via fluorescence. Therefore, we will present full-dimensional photodynamics of SAc using the floating-occupation configuration-interaction (FOCI) treatment with single and paired double excitations based on the semiempirical RM1 Hamiltonian. To further clarify mechanistic details, the potential energy surface (PES) is scanned along the proton transfer coordinates in one and two dimensions. The time-evolution of relevant degrees of freedom (DOF), quantum yields and isomer populations are evaluated from 200 surface-hopping trajectories. It was found that a deactivation pathway from the excited state to the ground state is indeed accessible through a conical intersection, via rotation of the carboxyl group. Together with the ESIPT process, this rotation can also interchange the protons of the two (formal) OH groups, which makes the overall dynamics still more complex. Our full-dimensional photodynamics study provides a comprehensive overview of all these entangled steps.

Cheap but accurate calculation of chemical reaction rate constants from ab initio data, via system-specific, black-box force fields.

Building on the recently published quantum-mechanically derived force field (QMDFF) and its empirical valence bond extension, EVB-QMDFF, it is now possible to generate a reliable potential energy surface for any given elementary reaction step in an essentially black box manner. This requires a limited and pre-defined set of reference data near the reaction path and generates an accurate approximation of the reference potential energy surface, on and off the reaction path. This intermediate representation can be used to generate reaction rate data, with far better accuracy and reliability than with traditional approaches based on transition state theory (TST) or variational extensions thereof (VTST), even if those include sophisticated tunneling corrections. However, the additional expense at the reference level remains very modest. We demonstrate all this for three arbitrarily chosen example reactions.

Error-Safe, Portable, and Efficient Evolutionary Algorithms Implementation with High Scalability.

We present an efficient massively parallel implementation of genetic algorithms for chemical and materials science problems, solely based on Java virtual machine (JVM) technologies and standard networking protocols. The lack of complicated dependencies allows for a highly portable solution exploiting strongly heterogeneous components within a single computational context. At runtime, our implementation is almost completely immune to hardware failure, and additional computational resources can be added or subtracted dynamically, if needed. With extensive testing, we show that despite all these benefits, parallel scalability is excellent.

reaxFF Reactive Force Field for Disulfide Mechanochemistry, Fitted to Multireference ab Initio Data.

Mechanochemistry, in particular in the form of single-molecule atomic force microscopy experiments, is difficult to model theoretically, for two reasons: Covalent bond breaking is not captured accurately by single-determinant, single-reference quantum chemistry methods, and experimental times of milliseconds or longer are hard to simulate with any approach. Reactive force fields have the potential to alleviate both problems, as demonstrated in this work: Using nondeterministic global parameter optimization by evolutionary algorithms, we have fitted a reaxFF force field to high-level multireference ab initio data for disulfides. The resulting force field can be used to reliably model large, multifunctional mechanochemistry units with disulfide bonds as designed breaking points. Explorative calculations show that a significant part of the time scale gap between AFM experiments and dynamical simulations can be bridged with this approach.

Reactive force fields made simple.

Generating a reactive force field for a given chemical reaction is turned from a many-months project for experts into a task of a few hours for a non-specialist, by joining the newly developed quantum-mechanically derived force field (QMDFF) and Warshel's time-tested empirical valence bond (EVB) idea. Three first example applications demonstrate that this works not just for simple atom exchange but also for more complicated reactions.

Efficient global optimization of reactive force-field parameters.

Reactive force fields make low-cost simulations of chemical reactions possible. However, optimizing them for a given chemical system is difficult and time-consuming. We present a high-performance implementation of global force-field parameter optimization, which delivers parameter sets of the same quality with much less effort and in far less time than before, and also offers excellent parallel scaling. We demonstrate these features with example applications targeting the ReaxFF force field. © 2015 Wiley Periodicals, Inc.