Tunable Macrocyclic Polyparaphenylene Nanolassos via Copper-Free Click Chemistry.

Deriving diverse compound libraries from a single substrate in high yields remains to be a challenge in cycloparaphenylene chemistry. In here, a strategy for the late-stage functionalization of shape-persistent alkyne-containing cycloparaphenylene has been explored using readily available azides. The copper-free [3+2]azide-alkyne cycloaddition provided high yields (>90 %) in a single reaction step. Systematic variation of the azides from electron-rich to -deficient shines light on how peripheral substitution influences the characteristics of the resulting adducts. We find that among the most affected properties are the molecular shape, the oxidation potential, excited state features, and affinities towards different fullerenes. Joint experimental and theoretical results are presented including calculations with the state-of-the-art, artificial intelligence-enhanced quantum mechanical method 1 (AIQM1).

Photo-Driven Aerobic Methane Nitration.

Conversion of methane to liquid oxygenates is challenging but of great value. Here, we report the oxidation of methane (CH4) to methanol (CH3OH) assisted by nitrogen dioxide (NO2) as a photo-mediator and using molecular oxygen (O2) as the terminal oxidant. Similar photoreactions are widely studied in atmospheric chemistry but were not previously used in preparative methane conversion. We used visible light to excite NO2 generated by heating aluminum nitrate Al(NO3)3 and drove its reaction with methane and O2 to produce methyl nitrate (CH3ONO2), which is then hydrolyzed to CH3OH. Nitric acid (HNO3) and nitrate (NO3-) were produced and recycled back to Al(NO3)3, completing a chemical loop. HCl can catalyze this photochemical process via relay hydrogen atom-transfer reactions, with up to 17% methane conversion and 78% CH3ONO2 selectivity. This simple photochemical system provides new opportunities in selective methane transformation.

Energy-conserving molecular dynamics is not energy conserving.

Molecular dynamics (MD) is a widely-used tool for simulating molecular and materials properties. It is common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots of effort is put into ensuring that molecular dynamics simulations are energy conserving. The emergence of machine learning (ML) potentials for MD leads to a growing realization that monitoring conservation of energy during simulations is of low utility because the dynamics is often unphysically dissociative. Other ML methods for MD are not based on a potential and provide only forces or trajectories which are reasonable but not necessarily energy-conserving. Here we propose to clearly distinguish between the simulation-energy and true-energy conservation and highlight that the simulations should focus on decreasing the degree of true-energy non-conservation. We introduce very simple, new criteria for evaluating the quality of molecular dynamics by estimating the degree of true-energy non-conservation and we demonstrate their practical utility on an example of infrared spectra simulations. These criteria are more important and intuitive than simply evaluating the quality of the ML potential energies and forces as is commonly done and can be applied universally, e.g., even for trajectories with unknown or discontinuous potential energy. Such an approach introduces new standards for evaluating MD by focusing on the true-energy conservation and can help in developing more accurate methods for simulating molecular and materials properties.

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights.

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow.

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

A Spherically Shielded Triphenylamine and Its Persistent Radical Cation.

This work reports the design and synthesis of a sterically protected triphenylamine scaffold which undergoes one-electron oxidation to form an amine-centered radical cation of remarkable stability. Several structural adjustments were made to tame the inherent reactivity of the radical cation. First, the parent propeller-shaped triphenylamine was planarized with sterically demanding bridging units and, second, protecting groups were deployed to block the reactive positions. The efficiently shielded triphenylamine core can be reversibly oxidized at moderate potentials (+0.38 V, vs. Fc/Fc+ in CH2 Cl2 ). Spectroelectrochemistry and chemical oxidation studies were employed to monitor the evolution of characteristic photophysical features. To obtain a better understanding of the impact of one-electron oxidation on structural and electronic properties, joint experimental and computational studies were conducted, including X-ray structural analysis, electron paramagnetic resonance (EPR), and density functional theory (DFT) calculations. The sterically shielded radical cation combines various desirable attributes: A characteristic and unobstructed absorption in the visible region, high stability which enables storage for weeks without spectroscopically traceable degradation, and a reliable oxidation/re-reduction process due to effective screening of the planarized triphenylamine core from its environment.

Machine Learning for Absorption Cross Sections.

We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.

Hierarchical machine learning of potential energy surfaces.

We present hierarchical machine learning (hML) of highly accurate potential energy surfaces (PESs). Our scheme is based on adding predictions of multiple Δ-machine learning models trained on energies and energy corrections calculated with a hierarchy of quantum chemical methods. Our (semi-)automatic procedure determines the optimal training set size and composition of each constituent machine learning model, simultaneously minimizing the computational effort necessary to achieve the required accuracy of the hML PES. Machine learning models are built using kernel ridge regression, and training points are selected with structure-based sampling. As an illustrative example, hML is applied to a high-level ab initio CH3Cl PES and is shown to significantly reduce the computational cost of generating the PES by a factor of 100 while retaining similar levels of accuracy (errors of ∼1 cm-1).

The Impact of Aggregation on the Photophysics of Spiro-Bridged Heterotriangulenes.

We report on the impact of the central heteroatom on structural, electronic, and spectroscopic properties of a series of spirofluorene-bridged heterotriangulenes and provide a detailed study on their aggregates. The in-depth analysis of their molecular structure by NMR spectroscopy and X-ray crystallography was further complemented by density functional theory calculations. With the aid of extensive photophysical analysis the complex fluorescence spectra were deconvoluted showing contributions from the peripheral fluorenes and the heteroaromatic cores. Beyond the molecular scale, we examined the aggregation behavior of these heterotriangulenes in THF/H2 O mixtures and analyzed the aggregates by static and dynamic light scattering. The excited-state interactions within the aggregates were found to be similar to those found in the solid state. A plethora of morphologies and superstructures were observed by scanning electron microscopy of drop-casted dispersions.

MLatom: A program package for quantum chemical research assisted by machine learning.

MLatom is a program package designed for computationally efficient simulations of atomistic systems with machine-learning (ML) algorithms. It can be used out-of-the-box as a stand-alone program with a user-friendly online manual. The use of MLatom does not require extensive knowledge of machine learning, programming, or scripting. The user need only prepare input files and choose appropriate options. The program implements kernel ridge regression and supports Gaussian, Laplacian, and Matérn kernels. It can use arbitrary, user-provided input vectors and can convert molecular geometries into input vectors corresponding to several types of built-in molecular descriptors. MLatom saves and re-uses trained ML models as needed, in addition to estimating the generalization error of ML setups. Various sampling procedures are supported and the gradients of output properties can be calculated. The core part of MLatom is written in Fortran, uses standard libraries for linear algebra, and is optimized for shared-memory parallel computations. © 2019 Wiley Periodicals, Inc.

Big data analysis of ab Initio molecular integrals in the neglect of diatomic differential overlap approximation.

Most modern semiempirical quantum-chemical (SQC) methods are based on the neglect of diatomic differential overlap (NDDO) approximation to ab initio molecular integrals. Here, we check the validity of this approximation by computing all relevant integrals for 32 typical organic molecules using Gaussian-type orbitals and various basis sets (from valence-only minimal to all-electron triple-ζ basis sets) covering in total more than 15.6 million one-electron (1-e) and 10.3 billion two-electron (2-e) integrals. The integrals are calculated in the nonorthogonal atomic basis and then transformed by symmetric orthogonalization to the Löwdin basis. In the case of the 1-e integrals, we find strong orthogonalization effects that need to be included in SQC models, for example, by strategies such as those adopted in the available OMx methods. For the valence-only minimal basis, we confirm that the 2-e Coulomb integrals in the Löwdin basis are quantitatively close to their counterparts in the atomic basis and that the 2-e exchange integrals can be safely neglected in line with the NDDO approximation. For larger all-electron basis sets, there are strong multishell orthogonalization effects that lead to more irregular patterns in the transformed 2-e integrals and thus cast doubt on the validity of the NDDO approximation for extended basis sets. Focusing on the valence-only minimal basis, we find that some of the NDDO-neglected integrals are reduced but remain sizable after the transformation to the Löwdin basis; this is true for the two-center 2-e hybrid integrals, the three-center 1-e nuclear attraction integrals, and the corresponding three-center 2-e hybrid integrals. We consider a scheme with a valence-only minimal basis that includes such terms as a possible strategy to go beyond the NDDO integral approximation in attempts to improve SQC methods. © 2018 Wiley Periodicals, Inc.

Organic Electron Acceptors Comprising a Dicyanomethylene-Bridged Acridophosphine Scaffold: The Impact of the Heteroatom.

Stable two-electron acceptors comprising a dicyanomethylene-bridged acridophosphine scaffold were synthesized and their reversible reduction potentials were efficiently tuned through derivatization of the phosphorus center. X-ray crystallographic analysis combined with NMR, UV/Vis, IR spectroscopic, and electrochemical studies, supported by theoretical calculations, revealed the crucial role of the phosphorus atom for the unique redox, structural, and photophysical properties of these compounds. The results identify the potential of these electron deficient scaffolds for the development of functional n-type materials and redox active chromophores upon further functionalization.

Dithiafulvenyl-Extended N-Heterotriangulenes and Their Interaction with C60 : Cooperative Fluorescence.

We describe the synthesis as well as the electronic and photophysical characterization of novel N-heterotriangulene derivatives decorated with methoxycarbonyl- and methyl-sulfanyl-substituted dithiafulvenyl moieties. The association of these electron-rich compounds with fullerene C60 as electron acceptor was investigated by means of photophysical, voltammetric, and mass spectrometric methods and rationalized by DFT calculations. Importantly, light-induced interactions between the dithiafulvene-substituted N-heterotriangulene bearing methoxycarbonyl substituents with C60 leads to cooperative fluorescence. Quantitative Job plot analyses by means of fluorescence spectroscopy and voltammetry confirm a 1:1 association with binding constants in the order of 104 m-1 . Supportive results for the supramolecular assembly of both N-heterotriangulenes with C60 were obtained by ESI mass spectrometric investigations in the gas phase.

On the feasibility of reactions through the fullerene wall: a theoretical study of NHx@C60.

We propose a new approach to the synthesis of AHx@fullerene structures via reactions through the fullerene wall. To investigate the feasibility of the approach, the step-by-step hydrogenation of the template endofullerene N@C60 up to NH4@C60 has been studied using DFT and MP2 calculations. Protonation of the endohedral guest through the fullerene wall is competitive with escape of the guest, whereas reaction with a hydrogen atom is less favorable. Each protonation step is highly exothermic, so that less active acids can also protonate the guest with less accumulation of energy. The final product, NH4@C60 is a novel concentric ion pair NH4+@C60˙- in which the charge-centers of the two ions coincide.

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.

We present an efficient approach for generating highly accurate molecular potential energy surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine learning (ML). We introduce structure-based sampling to automatically assign nuclear configurations from a pre-defined grid to the training and prediction sets, respectively. Accurate high-level ab initio energies are required only for the points in the training set, while the energies for the remaining points are provided by the ML model with negligible computational cost. The proposed sampling procedure is shown to be superior to random sampling and also eliminates the need for training several ML models. Self-correcting machine learning has been implemented such that each additional layer corrects errors from the previous layer. The performance of our approach is demonstrated in a case study on a published high-level ab initio PES of methyl chloride with 44 819 points. The ML model is trained on sets of different sizes and then used to predict the energies for tens of thousands of nuclear configurations within seconds. The resulting datasets are utilized in variational calculations of the vibrational energy levels of CH3Cl. By using both structure-based sampling and self-correction, the size of the training set can be kept small (e.g., 10% of the points) without any significant loss of accuracy. In ab initio rovibrational spectroscopy, it is thus possible to reduce the number of computationally costly electronic structure calculations through structure-based sampling and self-correcting KRR-based machine learning by up to 90%.

Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge.

One of the central aspects of biomolecular recognition is the hydrophobic effect, which is experimentally evaluated by measuring the distribution coefficients of compounds between polar and apolar phases. We use our predictions of the distribution coefficients between water and cyclohexane from the SAMPL5 challenge to estimate the hydrophobicity of different explicit solvent simulation techniques. Based on molecular dynamics trajectories with the CHARMM General Force Field, we compare pure molecular mechanics (MM) with quantum-mechanical (QM) calculations based on QM/MM schemes that treat the solvent at the MM level. We perform QM/MM with both density functional theory (BLYP) and semi-empirical methods (OM1, OM2, OM3, PM3). The calculations also serve to test the sensitivity of partition coefficients to solute polarizability as well as the interplay of the quantum-mechanical region with the fixed-charge molecular mechanics environment. Our results indicate that QM/MM with both BLYP and OM2 outperforms pure MM. However, this observation is limited to a subset of cases where convergence of the free energy can be achieved.

Multiply bonded metal(II) acetate (rhodium, ruthenium, and molybdenum) complexes with the trans-1,2-bis(N-methylimidazol-2-yl)ethylene ligand.

The synthesis and structural characterization of new coordination polymers with the N,N-donor ligand trans-1,2-bis(N-methylimidazol-2-yl)ethylene (trans-bie) are reported. It was found that the acetate-bridged paddlewheel metal(II) complexes [M2(O2CCH3)4(trans-bie)]n with M = Rh, Ru, Mo, and Cr are linked by the trans-bie ligand to give a one-dimensional alternating chain. The metal-metal multiple bonds were analyzed with density functional theory and CASSCF/CASPT2 calculations (bond orders: Rh, 0.8; Ru, 1.7; Mo, 3.3).

AI in computational chemistry through the lens of a decade-long journey.

This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.