Bayesian inference of atomistic structure in functional materials

Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device. Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations, however, finding the most favourable configurations remains computationally prohibitive. We propose a 'building block'-based Bayesian Optimisation Structure Search (BOSS) approach for addressing extended organic/inorganic interface problems and demonstrate its feasibility in a molecular surface adsorption study. In BOSS, a Bayesian model identifies material energy landscapes in an accelerated fashion from atomistic configurations sampled during active learning. This allowed us to identify several most favourable molecular adsorption configurations for C-60 on the (101) surface of TiO2 anatase and clarify the key molecule-surface interactions governing structural assembly. Inferred structures were in good agreement with detailed experimental images of this surface adsorbate, demonstrating good predictive power of BOSS and opening the route towards large-scale surface adsorption studies of molecular aggregates and films.Peer reviewe

Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111)

Organic charge transfer complexes (CTCs) can be grown as thin films on intercalated graphene (Gr). Deciphering their precise film morphologies requires global ab initio structure search, where configurational sampling is computationally intractable unless we reconsider the model for the complex substrate. In this study, we employ charged freestanding Gr to approximate an intercalated Gr/O/Ir(111) substrate, without altering the adsoption properties of deposited molecules. We compare different methods of charging Gr and select the most appropriate substitute model for Gr/O/Ir(111) that maintains the adsorption properties of fluorinated tetracyanoquinodimethane (F4TCNQ) and tetrathiafulvalene (TTF), prototypical electron acceptor/donor molecules in CTCs. Next, we apply our model in the Bayesian optimization structure search method and density-functional theory to identify the stable structures of F4TCNQ and TTF on supported Gr. We find that both molecules physisorb to Gr in various configurations. The narrow range of adsorption energies indicates that the molecules may diffuse easily on the surface and molecule-molecule interactions likely have a central role in film formation. Our study shows that complex intercalated substrates may be approximated with charged freestanding Gr, which can facilitate exhaustive structure search of CTCs

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization

Machine learning methods usually depend on internal parameters-so called hyperparameters-that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or computationally demanding brute-force parameter searches. We here assess three different hyperparameter selection methods: grid search, random search and an efficient automated optimization technique based on Bayesian optimization (BO). We apply these methods to a machine learning problem based on kernel ridge regression in computational chemistry. Two different descriptors are employed to represent the atomic structure of organic molecules, one of which introduces its own set of hyperparameters to the method. We identify optimal hyperparameter configurations and infer entire prediction error landscapes in hyperparameter space that serve as visual guides for the hyperparameter performance. We further demonstrate that for an increasing number of hyperparameters, BO and random search become significantly more efficient in computational time than an exhaustive grid search, while delivering an equivalent or even better accuracy

EFFECTS OF SUPPLEMENTATION WITH VITAMIN E ON GENTAMYCIN-INDUCED ACUTE RENAL FAILURE IN RATS

A frequent administration of gentamicin in clinical practice has shown its bactericidal activity, and besides being vestibulotoxic it is highly nephrotoxic, which can further result in acute renal insufficiency. The study analyzed 24 Wistar rats, divided into three equal groups. GM group received gentamicin (100 mg/kg), GME group received vitamin E (100 mg/kg) and the same dose of gentamicin as GM rats, while the third group served as the control group and received saline (1 ml/24h) for 8 days. Pathohistological examination of the kidney tissues from GM group rats showed areas of coagulation-type necrosis in a large number of proximal tubules, while their glomeruli were considerably enlarged compared both to control and GME group rats. In GME rats, changes in glomeruli were less visible, while areas of coagulation-type necrosis were not found.  Biochemical analysis showed significantly higher values of blood urea and creatinine in GM group rats in comparison to C group and GME group (p<0.001). The concentrations of potassium in blood serum was significantly lower in GM group compared to control group (p<0.01), whereas the concentration of sodium was lower, however, without statistical significance. The concentrations of AOPP for GM group were significantly higher when compared to C group (p<0.001), whereas the values for GME group of rats were statistically significantly lower than AOPP recorded for GM group (p<0.001). Our experimental study has shown that gentamicin-induced nephrotoxicity can be significantly reduced by simultaneous administration of vitamin E.Key words: Gentamicin, vitamin E, nephrotoxicity, Wistar rat

HERBICIDE PHOSPHINOTHRICIN CAUSES DIRECT STIMULATION HORMESIS

Herbicide phosphinothricin (PPT) inhibits glutamine synthetase (GS), a key enzyme in nitrogen assimilation, thus causing ammonia accumulation, glutamine depletion and eventually plant death. However, the growth response of Lotus corniculatus L. plants immersed in solutions with a broad range of PPT concentrations is biphasic, with pronounced stimulating effect on biomass production at concentrations ≤ 50 μM and growth inhibition at higher concentrations. The growth stimulation at low PPT concentrations is a result of activation of chloroplastic isoform GS2, while the growth suppression is caused by inhibition of both cytosolic GS1 and GS2 at higher PPT concentrations. Since the results are obtained in cell-free system (e.g. protein extracts), to which the principles of homeostasis are not applicable, this PPT effect is an unambiguous example of direct stimulation hormesis. A detailed molecular mechanism of concentration-dependent interaction of both PPT and a related GS inhibitor, methionine sulfoximine, with GS holoen- zymes is proposed. The mechanism is in concurrence with all experimental and literature data

Predicting gas-particle partitioning coefficients of atmospheric molecules with machine learning

The formation, properties, and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas-particle partitioning coefficients of the participating organic vapours. Since these coefficients are often difficult to measure and to compute, we developed a machine learning model to predict them given molecular structure as input. Our data-driven approach is based on the dataset by Wang et al. (2017), who computed the partitioning coefficients and saturation vapour pressures of 3414 atmospheric oxidation products from the Master Chemical Mechanism using the COSMOtherm programme. We trained a kernel ridge regression (KRR) machine learning model on the saturation vapour pressure (P-sat) and on two equilibrium partitioning coefficients: between a water-insoluble organic matter phase and the gas phase (K-WIOM/G) and between an infinitely dilute solution with pure water and the gas phase (K-W/G). For the input representation of the atomic structure of each organic molecule to the machine, we tested different descriptors. We find that the many-body tensor representation (MBTR) works best for our application, but the topological fingerprint (TopFP) approach is almost as good and computationally cheaper to evaluate. Our best machine learning model (KRR with a Gaussian kernel + MBTR) predicts P-sat and K-WIOM/G to within 0.3 logarithmic units and K-W/G to within 0.4 logarithmic units of the original COSMOtherm calculations. This is equal to or better than the typical accuracy of COSMOtherm predictions compared to experimental data (where available). We then applied our machine learning model to a dataset of 35 383 molecules that we generated based on a carbon-10 backbone functionalized with zero to six carboxyl, carbonyl, or hydroxyl groups to evaluate its performance for polyfunctional compounds with potentially low P-sat. The resulting saturation vapour pressure and partitioning coefficient distributions were physico-chemically reasonable, for example, in terms of the average effects of the addition of single functional groups. The volatility predictions for the most highly oxidized compounds were in qualitative agreement with experimentally inferred volatilities of, for example, alpha-pinene oxidation products with as yet unknown structures but similar elemental compositions

Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In this fresh approach, cross-disciplinary tools are integrated for a robust and automated identification of 3D adsorbate configurations. Bayesian optimization is employed with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow fingerprinting adsorbate structures that appear in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, three matching AFM image contrasts are found, which allow correlating experimental image features to distinct cases of molecular adsorption

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

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

Traditional use of plants in Kuršumlija

Plants as a source of active phytochemistry are the basis of nutrition. However, man uses them in traditional medicine and veterinary medicine, but also as raw materials in many branches of industry (textile, construction…). The need and role of traditional medicines in the health care system havе been growing in recent decades. The aim of the research is to determine the list of plants that are traditionally used by the local population on the territory of the municipality of Kuršumlija, and the way of their application in folk medicine, veterinary medicine, customs. Data on knowledge of plants and their use were collected through interviews in the period from May to September 2020. A total of 49 people were interviewed (37.5% men and 62.5% women), aged between 21 and 81, mostly from urban areas (79.2% of respondents are from the city and 20.8% from rural areas). Respondents have different education: 6.3% have a primary school, 58.3% have a secondary school and 35.4% have higher education. During the research, it was stated that the largest number of plant species used for therapeutic purposes belongs to families: Lamiaceae (20.5%), Asteraceae (12.8%), Rosaceae (5.1%) and the most commonly used species are: Mentha piperita (47.9% of respondents), Matricaria chamomilla (37.5% of respondents); Urtica dioica (31.2% of respondents); Hypericum perforatum (27% of respondents), Salvia officinalis (22.9% of respondents); Achillea millefolium (14.5% of respondents); Ocimum basilicum (12.5% ​​of respondents). For therapeutic purposes, teas (infusion, decoction), tinctures, and oils are prepared for oral use, and for external use, compresses and ointments. The largest number of respondents reported the use of herbal medicines for the treatment of gastrointestinal and respiratory organs. In the customs related to religious holidays, the largest number of respondents use oak.Publishe