Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules.

Low-volatile organic compounds (LVOCs) drive key atmospheric processes, such as new particle formation (NPF) and growth. Machine learning tools can accelerate studies of these phenomena, but extensive and versatile LVOC datasets relevant for the atmospheric research community are lacking. We present the GeckoQ dataset with atomic structures of 31,637 atmospherically relevant molecules resulting from the oxidation of α-pinene, toluene and decane. For each molecule, we performed comprehensive conformer sampling with the COSMOconf program and calculated thermodynamic properties with density functional theory (DFT) using the Conductor-like Screening Model (COSMO). Our dataset contains the geometries of the 7 Mio. conformers we found and their corresponding structural and thermodynamic properties, including saturation vapor pressures (pSat), chemical potentials and free energies. The pSat were compared to values calculated with the group contribution method SIMPOL. To validate the dataset, we explored the relationship between structural and thermodynamic properties, and then demonstrated a first machine-learning application with Gaussian process regression.

Updates to the DScribe library: New descriptors and derivatives.

We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.

Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization.

Finding low-energy conformers of organic molecules is a complex problem due to the flexibilities of the molecules and the high dimensionality of the search space. When such molecules are on nanoclusters, the search complexity is exacerbated by constraints imposed by the presence of the cluster and other surrounding molecules. To address this challenge, we modified our previously developed active learning molecular conformer search method based on Bayesian optimization and density functional theory. Especially, we have developed and tested strategies to avoid steric clashes between a molecule and a cluster. In this work, we chose a cysteine molecule on a well-studied gold-thiolate cluster as a model system to test and demonstrate our method. We found that cysteine conformers in a cluster inherit the hydrogen bond types from isolated conformers. However, the energy rankings and spacings between the conformers are reordered.

Molecular Conformer Search with Low-Energy Latent Space.

Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with 5-9 searching dimensions. Our results agree with previous studies.

Protective Coating Interfaces for Perovskite Solar Cell Materials: A First-Principles Study.

The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the potential inorganic protective coating materials ZnO, SrZrO3, and ZrO2 for the CsPbI3 perovskite. The optimal interface registries are identified with Bayesian optimization. We then use semilocal density functional theory (DFT) to determine the atomic structure at the interfaces of each coating material with the clean CsI-terminated surface and three reconstructed surface models with added PbI2 and CsI complexes. For the final structures, we explore the level alignment at the interface with hybrid DFT calculations. Our analysis of the level alignment at the coating-substrate interfaces reveals no detrimental mid-gap states but rather substrate-dependent valence and conduction band offsets. While ZnO and SrZrO3 act as insulators on CsPbI3, ZrO2 might be suitable as an electron transport layer with the right interface engineering.

Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models.

Abstract: Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and pK a) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by ML approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization, and other functions inherent to OTA. Impact statement: Tannic acid is a versatile bio-derived material employed in coatings, surface modifiers, and emulsion and growth stabilizers, which also imparts mild anti-viral health benefits. Our recent work on the crystallization of oxidized tannic acid (OTA) colloids opens the route toward further valuable applications, but here the functional properties tend to depend strongly on particle morphology. In this study, we eschew trial-and-error morphology exploration of OTA particles in favor of a data-driven approach. We digitalized the experimental observations and input them into a Gaussian process regression algorithm to generate morphology surrogate models. These help us to visualize particle morphology in the design space of material processing conditions, and thus determine how to selectively engineer one-dimensional or three-dimensional particles with targeted functionalities. We extend this approach to visualize other experimental outcomes, including particle yield and particle surface-to-volume ratio, which are useful for the design of products based on OTA particles. Our findings demonstrate the use of data-efficient surrogate models for general materials engineering purposes and facilitate the development of next-generation OTA-based applications. Supplementary information: The online version contains supplementary material available at 10.1557/s43577-021-00183-4.

Efficient Amino Acid Conformer Search with Bayesian Optimization.

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization.

Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules.

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62 k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62 k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the G0W0 approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))).

Chemical diversity in molecular orbital energy predictions with kernel ridge regression.

Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules set, amino acid and dipeptide conformers, and organic crystal-forming molecules extracted from the Cambridge Structural Database. We focus on the prediction of highest occupied molecular orbital (HOMO) energies, computed at the density-functional level of theory. Two different representations that encode the molecular structure are compared: the Coulomb matrix (CM) and the many-body tensor representation (MBTR). We find that KRR performance depends significantly on the chemistry of the underlying dataset and that the MBTR is superior to the CM, predicting HOMO energies with a mean absolute error as low as 0.09 eV. To demonstrate the power of our machine learning method, we apply our model to structures of 10k previously unseen molecules. We gain instant energy predictions that allow us to identify interesting molecules for future applications.

First Principles Calculations on the Stoichiometric and Defective (101) Anatase Surface and Upon Hydrogen and H2Pc Adsorption: The Influence of Electronic Exchange and Correlation and of Basis Set Approximations.

Anatase TiO2 provides photoactivity with high chemical stability at a reasonable cost. Different methods have been used to enhance its photocatalytic activity by creating band gap states through the introduction of oxygen vacancies, hydrogen impurities, or the adorption of phthalocyanines, which are usually employed as organic dyes in dye-sensitized solar cells. Predicting how these interactions affect the electronic structure of anatase requires an efficient and robust theory. In order to document the efficiency and accuracy of commonly used approaches we have considered two widely used implementations of density functional theory (DFT), namely the all-electron linear combination of atomic orbitals (AE-LCAO) and the pseudo-potential plane waves (PP-PW) approaches, to calculate the properties of the stoichiometric and defective anatase TiO2 (101) surface. Hybrid functionals, and in particular HSE, lead to a computed band gap in agreement with that measured by using UV adsorption spectroscopy. When using PBE+U, the gap is underestimated by 20 % but the computed position of defect induced gap states relative to the conduction band minimum (CBM) are found to be in good agreement with those calculated using hybrid functionals. These results allow us to conclude that hybrid functionals based on the use of AE-LCAO provide an efficient and robust approach for predicting trends in the band gap and the position of gap states in large model systems. We extend this analysis to surface adsorption and use the AE-LCAO approach with the hybrid functional HSED3 to study the adsorption of the phthalocyanine H2Pc on anatase (101). Our results suggest that H2Pc prefers to be adsorbed on the surface Ti5c rows of anatase (101), in agreement with that seen in recent STM experiments on rutile (110).

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.

Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications.

Pentacene/TiO2 Anatase Hybrid Interface Study by Scanning Probe Microscopy and First Principles Calculations.

The understanding and control of the buried interface between functional materials in optoelectronic devices is key to improving device performance. We combined atomic resolution scanning probe microscopy with first-principles calculations to characterize the technologically relevant organic/inorganic interface structure between pentacene molecules and the TiO2 anatase (101) surface. A multipass atomic force microscopy imaging technique overcomes the technical challenge of imaging simultaneously the corrugated anatase substrate, molecular adsorbates, monolayers, and bilayers at the same level of detail. Submolecular resolution images revealed the orientation of the adsorbates with respect to the substrate and allowed direct insights into interface formation. Pentacene molecules were found to physisorb parallel to the anatase substrate in the first contact layer, passivating the surface and promoting bulk-like growth in further organic layers. While molecular electronic states were not significantly hybridized by the substrate, simulations predicted localized pathways for molecule-surface charge injection. The localized states were associated with the molecular lowest unoccupied molecular orbital inside the oxide conduction band, pointing to efficient transfer of photo-induced electron charge carriers across this interface in prospective photovoltaic devices. In uncovering the atomic arrangement and favorable electronic properties of the pentacene/anatase interface, our findings testify to the maturity and analytic power of our methodology in further studies of organic/inorganic interfaces.

Submolecular Imaging by Noncontact Atomic Force Microscopy with an Oxygen Atom Rigidly Connected to a Metallic Probe.

In scanning probe microscopy, the imaging characteristics in the various interaction channels crucially depend on the chemical termination of the probe tip. Here we analyze the contrast signatures of an oxygen-terminated copper tip with a tetrahedral configuration of the covalently bound terminal O atom. Supported by first-principles calculations we show how this tip termination can be identified by contrast analysis in noncontact atomic force and scanning tunneling microscopy (NC-AFM, STM) on a partially oxidized Cu(110) surface. After controlled tip functionalization by soft indentations of only a few angstroms in an oxide nanodomain, we demonstrate that this tip allows imaging an organic molecule adsorbed on Cu(110) by constant-height NC-AFM in the repulsive force regime, revealing its internal bond structure. In established tip functionalization approaches where, for example, CO or Xe is deliberately picked up from a surface, these probe particles are only weakly bound to the metallic tip, leading to lateral deflections during scanning. Therefore, the contrast mechanism is subject to image distortions, artifacts, and related controversies. In contrast, our simulations for the O-terminated Cu tip show that lateral deflections of the terminating O atom are negligible. This allows a detailed discussion of the fundamental imaging mechanisms in high-resolution NC-AFM experiments. With its structural rigidity, its chemically passivated state, and a high electron density at the apex, we identify the main characteristics of the O-terminated Cu tip, making it a highly attractive complementary probe for the characterization of organic nanostructures on surfaces.

Atomic species identification at the (101) anatase surface by simultaneous scanning tunnelling and atomic force microscopy.

Anatase is a pivotal material in devices for energy-harvesting applications and catalysis. Methods for the accurate characterization of this reducible oxide at the atomic scale are critical in the exploration of outstanding properties for technological developments. Here we combine atomic force microscopy (AFM) and scanning tunnelling microscopy (STM), supported by first-principles calculations, for the simultaneous imaging and unambiguous identification of atomic species at the (101) anatase surface. We demonstrate that dynamic AFM-STM operation allows atomic resolution imaging within the material's band gap. Based on key distinguishing features extracted from calculations and experiments, we identify candidates for the most common surface defects. Our results pave the way for the understanding of surface processes, like adsorption of metal dopants and photoactive molecules, that are fundamental for the catalytic and photovoltaic applications of anatase, and demonstrate the potential of dynamic AFM-STM for the characterization of wide band gap materials.

Correlations among Stress Parameters, Meat and Carcass Quality Parameters in Pigs.

Relationships among different stress parameters (lairage time and blood level of lactate and cortisol), meat quality parameters (initial and ultimate pH value, temperature, drip loss, sensory and instrumental colour, marbling) and carcass quality parameters (degree of rigor mortis and skin damages, hot carcass weight, carcass fat thickness, meatiness) were determined in pigs (n = 100) using Pearson correlations. After longer lairage, blood lactate (p<0.05) and degree of injuries (p<0.001) increased, meat became darker (p<0.001), while drip loss decreased (p<0.05). Higher lactate was associated with lower initial pH value (p<0.01), higher temperature (p<0.001) and skin blemishes score (p<0.05) and more developed rigor mortis (p<0.05), suggesting that lactate could be a predictor of both meat quality and the level of preslaughter stress. Cortisol affected carcass quality, so higher levels of cortisol were associated with increased hot carcass weight, carcass fat thickness on the back and at the sacrum and marbling, but also with decreased meatiness. The most important meat quality parameters (pH and temperature after 60 minutes) deteriorated when blood lactate concentration was above 12 mmol/L.

Understanding scanning tunneling microscopy contrast mechanisms on metal oxides: a case study.

A comprehensive analysis of contrast formation mechanisms in scanning tunneling microscopy (STM) experiments on a metal oxide surface is presented with the oxygen-induced (2√2×√2)R45° missing row reconstruction of the Cu(100) surface as a model system. Density functional theory and electronic transport calculations were combined to simulate the STM imaging behavior of pure and oxygen-contaminated metal tips with structurally and chemically different apexes while systematically varying bias voltage and tip-sample distance. The resulting multiparameter database of computed images was used to conduct an extensive comparison with experimental data. Excellent agreement was attained for a large number of cases, suggesting that the assumed model tips reproduce most of the commonly encountered contrast-determining effects. Specifically, we find that depending on the bias voltage polarity, copper-terminated tips allow selective imaging of two structurally distinct surface Cu sites, while oxygen-terminated tips show complex contrasts with pronounced asymmetry and tip-sample distance dependence. Considering the structural and chemical stability of the tips reveals that the copper-terminated apexes tend to react with surface oxygen at small tip-sample distances. In contrast, oxygen-terminated tips are considerably more stable, allowing exclusive surface oxygen imaging at small tip-sample distances. Our results provide a conclusive understanding of fundamental STM imaging mechanisms, thereby providing guidelines for experimentalists to achieve chemically selective imaging by properly selecting imaging parameters.

Density-functional theory study of gramicidin A ion channel geometry and electronic properties.

Understanding the mechanisms underlying ion channel function from the atomic-scale requires accurate ab initio modelling as well as careful experiments. Here, we present a density functional theory (DFT) study of the ion channel gramicidin A (gA), whose inner pore conducts only monovalent cations and whose conductance has been shown to depend on the side chains of the amino acids in the channel. We investigate the ground state geometry and electronic properties of the channel in vacuum, focusing on their dependence on the side chains of the amino acids. We find that the side chains affect the ground state geometry, while the electrostatic potential of the pore is independent of the side chains. This study is also in preparation for a full, linear scaling DFT study of gA in a lipid bilayer with surrounding water. We demonstrate that linear scaling DFT methods can accurately model the system with reasonable computational cost. Linear scaling DFT allows ab initio calculations with 10,000-100,000 atoms and beyond, and will be an important new tool for biomolecular simulations.