jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anna Kryshchyshyn‐Dylevych
Source: Medical and Clinical Chemistry (article, 2021-02-12)
Field: Chemistry — Organic Chemistry

QSAR-АНАЛІЗ БІБЛІОТЕК 4-ТІАЗОЛІДИНОН-СПОРІДНЕНИХ ГЕТЕРОЦИКЛІВ ДЛЯ ПРОГНОЗУВАННЯ ПРОТИТРИПАНОСОМНИХ ВЛАСТИВОСТЕЙ НОВИХ ПОХІДНИХ Вступ. Похідні тіазолідинону та споріднених гетероциклів є джерелом нових протипаразитарних агентів, у тому числі молекул із протитрипаносомними властивостями. В актуальних наукових джерелах знайдено ряд досліджень про кількісний взаємозв’язок структура – протитрипаносомна активність, що включає різні підходи комп’ютерної хімії. Більшість досліджень належить до так званих мультитаргетних, коли до вибірки включають результати інших видів протипаразитарних активностей. Розробка нових QSAR-моделей похідних тіазолідинону з протитрипаносомними властивостями дозволить окреслити напрямки спрямованого дизайну нових протипаразитарних агентів на основі циклів тіазолу та тіазолідинону.&#x0D; Мета дослідження – встановити кількісний взаємозв’язок структура – протитрипаносомна активність у межах бібліотек тіазолідинонів та споріднених гетероциклів.&#x0D; Методи дослідження. Побудову математичних моделей на основі QSAR-аналізу здійснювали за допомогою онлайн-платформи Online Chemical Database.&#x0D; Результати й обговорення. Аналіз кількісного взаємозв’язку структура – протитрипаносомна активність проводили із застосуванням математичної моделі асоціативних нейронних мереж (ASNN: Associative Neural Networks) та методу регресії Random Forest (RFR: Random Forest regression) на основі вибірок, що включали похідні ізотіокумарин-3-карбонових кислот, тіопіранотіазолів і 4-тіазолідинон-імідазотіадіазолів із встановленою трипаноцидною активністю щодо Trypanosoma brucei brucei та Trypanosoma brucei gambiense. Кращу прогнозувальну здатність для групи ізотіокумарин-3-карбонових кислот і тіопірано[2,3-d][1,3]тіазол-2-онів обчислено за допомогою алгоритму Random Forest. Модель, обчислена на основі алгоритму Random Forest для групи імідазотіадіазолів, володіє найвищою прогнозувальною здатністю зі значенням R2=0,96.&#x0D; Висновок. На основі методів асоціативних нейронних мереж та регресії Random Forest розроблено прогностичні моделі для прогнозування протипаразитарної активності диверсифікованих похідних ­4-тіазолідинонів і подальшого фокусування напрямків оптимізації нових біологічно активних молекул із трипаноцидними властивостями. Random forest ; Quantitative structure–activity relationship ; Trypanosoma brucei ; Biology ; Chemistry ; Stereochemistry ; Artificial intelligence ; Computer science ; Biochemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Petr Grigorev | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica | James R. Kermode | Thomas D. Swinburne
Source: arXiv (Cornell University) (preprint, 2021-11-22)
Field: Materials Science — Materials Chemistry

Synergistic coupling in ab initio-machine learning simulations of dislocations Ab initio simulations of dislocations are essential to build quantitative models of material strength, but the required system sizes are often at or beyond the limit of existing methods. Many important structures are thus missing in the training or validation of interatomic potentials, whilst studies of dislocation-defect interactions must mitigate the effect of strong periodic image interactions along the line direction. We show how these restrictions can be lifted through the use of linear machine learning potentials in hybrid simulations, where only a subset of atoms are governed by ab initio forces. The linear form is exploited in a constrained retraining procedure, qualitatively expanding the range of training structures for learning and giving precise matching of dislocation core structures, such that lines can cross the quantum/classical boundary. We apply our method to fully three dimensional studies of impurity segregation to edge and screw dislocations in tungsten. Our retrained potentials give systematically improved accuracy to QM/ML reference data and the three dimensional geometry allows for long-range relaxations that qualitatively change impurity-induced core reconstructions compared to simulations using short periodic supercells. More generally, the ability to treat arbitrary sub-regions of large scale simulations with ab initio accuracy opens a vast range of previously inaccessible extended defects to quantitative investigation. Ab initio ; Dislocation ; Statistical physics ; Matching (statistics) ; Ab initio quantum chemistry methods ; Range (aeronautics) ; Materials science ; Physics ; Computer science ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Surface and Thin Film Phenomena
Source: Journal of Physics Conference Series (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Preface Abstract 10 th International Conference on Physics and Its Applications (ICOPIA) 26 August 2020, Surakarta, Indonesia International Conference on Physics and Its Applications (ICOPIA) is a biannual conference, started in 2001. This year of 2020, ICOPIA is the tenth one. This conference is mainly supported by Department of Physics, Universitas Sebelas Maret, Indonesia. ICOPIA is a forum for presenting and discussing current research in physics and its applications. Varied studies about material physics, optics, geophysics, instrumentation, magnetics and theoretical physics have been presented in the 10 th ICOPIA. All the papers were presented orally. The keynote presentations by qualified speakers have been delivered in this conference. The keynote presentation included Rutile-Phased TiO2 Nanostructured Towards Electronic Devices Application, Deep Learning for Biomedical Application, Control of nanoparticle-size in cobalt ferrite system and Improvement of Functional Oxide Properties for Applications. The keynotes speakers were from Université du Littoral Côte d’Opale, France, University of Technology Sydney, Australia, Universiti Tun Hussein Onn Malaysia and Universitas Sebelas Maret, Indonesia. This proceeding contains the papers presented in ICOPIA 2020. The papers are divided into sections: Acoustic, Computational Physics, Geophysics, Instrumentation, Material Physics, Optics, Theoretical Physics, and other topics related to Physics. This structure is made so that readers are easier to find an article in this proceeding. We would like to thank to all the participants attending this conference and also to the committees for their contribution to this high-level conference and its overall success. We also would like to thank to the reviewers for their positive contribution to maintain the quality of the articles presented in this conference. Nuryani, Ph.D General Chair of ICOPIA2020 List of titles General Chair, Organizer, Advisory Board, Organizing Committee are available in this Pdf. Library science ; Presentation (obstetrics) ; Instrumentation (computer programming) ; Engineering physics ; Engineering ; Physics ; Computer science ; Medicine Magnetic Properties and Synthesis of Ferrites ; Chemical and Physical Properties of Materials ; Multiferroics and related materials
Authors: Alexander Tropsha | Stanley Y. Shaw | Ralph Weissleder | Denis Fourches | Russell J. Mumper | Dongqiuye Pu | Carlos Tassa
Source: Figshare (article, 2021-06-29)
Field: Materials Science — Materials Chemistry

Quantitative Nanostructure−Activity Relationship Modeling Evaluation of biological effects, both desired and undesired, caused by Manufactured NanoParticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as Quantitative Structure – Activity Relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach Quantitative Nanostructure-Activity Relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (PNAS, 2008, 105, pp 7387–7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol., 2005, 23, pp 1418–1423). We have generated QNAR models using machine learning approaches such as Support Vector Machine (SVM)-based classification and k Nearest Neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and R2 of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials towards better and safer products. Nanostructure ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Hannah Fino | Brooks H. Pate | C. D. West | Sean A. Peebles | Rebecca A. Peebles
Source: Proceedings of the 2021 International Symposium on Molecular Spectroscopy (article, 2021-06-25)
Field: Chemistry — Analytical Chemistry

IMPLEMENTING EXTENDED CROSS CORRELATION AS A TOOL FOR SEMI-AUTOMATED MICROWAVE SPECTROSCOPIC ANALYSIS OF WEAKLY BOUND CLUSTER SPECTRA Rapid recording of molecular spectra of complex mixtures has facilitated acquisition of high sensitivity data sets. In studies of weakly bound clusters, these spectra may contain a wide range of cluster sizes and abundances, leading to intensities spanning many orders of magnitude between the strongest and weakest spectra present. The line densities and wide dynamic ranges of these high sensitivity spectra often lead to bottlenecks at the spectroscopic assignment and analysis stages of an experiment. Recent developments in artificial intelligence and machine learning have the potential to lead to fast, automated approaches to the spectroscopic assignment process. Here, we present an implementation of Jacobson, \\emph{et al}’s extended cross correlation (XCC) approach\\footnote{M.P. Jacobson, S.M. Coy, R.W. Field, Extended Cross Correlation: A Technique for Spectroscopic Pattern Recognition, \\emph{J. Chem. Phys.} 107 (1997) 8349–8356.} to separating transitions arising from different species. While not leading to fully automated spectroscopic assignment on its own, XCC provides simplified data sets that can be more easily analyzed by visual pattern recognition or combined with other automated approaches. Initial XCC results will be compared with our previous implementation of principal component analysis-based examination of microwave spectra of complex mixtures of fluorinated ethylene/\\chem{CO_2} clusters. In addition to simplifying raw datasets, these semi-automated analyses may provide insights that help identify the species present in the mixture, as well as helping guide searches for spectra of new species. Cluster (spacecraft) ; Microwave ; Spectral line ; Correlation ; Physics ; Computer science ; Computational physics ; Mathematics ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Acoustic Wave Resonator Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sawyer D. Campbell | Douglas H. Werner | Pingjuan L. Werner
Source:  (article, 2021-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of broadband and reconfigurable meta-optics By exploiting the generalized form of Snell’s law, metasurfaces afford optical engineers a tremendous increase in degrees of design freedom compared to conventional optical components. These “meta-optics” can achieve unprecedented levels of performance through engineered wavelength-, angular-, and polarization-dependent responses which can be tailored by arranging subwavelength unit cells, or meta-atoms, in an intelligent way. Moreover, these metaatoms can be constructed from phase change materials which give the added flexibility of realizing reconfigurable metaoptics. Devices such as achromatic flat lenses and non-mechanical zoom lenses are becoming a reality through the advent of metasurface-augmented optical systems. However, achieving high-performance meta-optics relies heavily on proper meta-atom design. This challenge is best overcome through the use of advanced inverse-design tools and state-ofthe- art optimization algorithms. To this end, a number of successful meta-device inverse-design approaches have been demonstrated in the literature including those based on topology optimization, deep learning, and global optimization. While each has its pros and cons, multi-objective optimization strategies have proven quite successful do their ability to optimize problems with multiple competing objectives: a common occurrence in optical design. Moreover, multiobjective algorithms produce a Pareto Set of optimal solutions that designers can analyze in order to directly study the tradeoffs between the various design goals. In our presentation, we will introduce an efficient multi-objective optimization enabled design framework for the generation of broadband and multifunctional meta-atoms. Additionally, several meta-optic design examples will be presented, and future research directions discussed. Broadband ; Computer science ; Inverse ; Electronic engineering ; Optics ; Telecommunications ; Physics ; Engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Optimization
Authors: Jacob G. Lee | Chris J. Pickard | Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2021-11-25)
Field: Materials Science — Materials Chemistry

High-pressure phase behaviors of titanium dioxide revealed by a $\Delta$-learning potential Titanium dioxide has been extensively studied in the rutile or anatase phases, while its high-pressure phases are less well understood, despite that many are thought to have interesting optical, mechanical and electrochemical properties. First-principles methods such as density functional theory (DFT) are often used to compute the enthalpies of TiO$_2$ phases at 0~K, but they are expensive and thus impractical for long time-scale and large system-size simulations at finite temperatures. On the other hand, cheap empirical potentials fail to capture the relative stablities of the various polymorphs. To model the thermodynamic behaviors of ambient and high-pressure phases of TiO$_2$, we design an empirical model as a baseline, and then train a machine learning potential based on the difference between the DFT data and the empirical model. This so-called $\Delta$-learning potential contains long-range electrostatic interactions, and predicts the 0~K enthalpies of stable TiO$_2$ phases that are in good agreement with DFT. We construct a pressure-temperature phase diagram of TiO$_2$ in the range $0<P<70$~GPa and $100<T<1500$~K. We then simulate dynamic phase transition processes, by compressing anatase at different temperatures. At 300~K, we observe predominantly anatase-to-baddeleyite transformation at about 20~GPa, via a martensitic two-step mechanism with highly ordered and collective atomic motion. At 2000~K, anatase can transform into cotunnite around 45-55~GPa in a thermally-activated and probabilistic manner, accompanied by diffusive movement of oxygen atoms. The pressures computed for these transitions show good agreement with experiments. Our results shed light on how to synthesize and stabilize high-pressure TiO$_2$ phases, and our method is generally applicable to other functional materials with multiple polymorphs. Anatase ; Phase diagram ; Rutile ; Materials science ; Phase transition ; Titanium dioxide ; Phase (matter) ; Thermodynamics ; Density functional theory ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Metallurgy Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; High-pressure geophysics and materials
Authors: Fei Wang | Zhiyuan Bi | Lifeng Ding | Qingyuan Yang
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Large-Scale Computational Screening of Metal-Organic Frameworks for D &lt;sub&gt;2&lt;/sub&gt;/H &lt;sub&gt;2&lt;/sub&gt; Adsorption and Separation Adsorption ; Chemistry ; Metal-organic framework ; Scale (ratio) ; Inorganic chemistry ; Physical chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Covalent Organic Framework Applications
Authors: Valery Novoseletsky | Mikhail Lozhnikov | Grigoriy A. Armeev | Aleksandr Kudriavtsev | Alexey К. Shaytan | Georgiy Kobelkov | К. В. Шайтан
Source: International Journal of Biomedicine (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Abstract P-5: Neural Network Approaches to Classify 3D Protein Structures from the Data of X-ray Laser Radiation Diffraction from Single Particles Background: Protein structure determination using X-ray free-electron laser (XFEL) includes analysis and merging a large number of snapshot diffraction patterns. Convolutional neural networks are widely used to solve numerous computer vision problems, e.g. image classification, and can be used for diffraction pattern analysis. But the task of protein structure determination with the use of CNNs only is not yet solved. Methods: We simulated the diffraction patterns using the Condor software library and obtained more than 1000 diffraction patterns for each structure with simulation parameters resembling real ones. To classify diffraction patterns, we tried two approaches, which are widely known in the area of image classification: a classic VGG network and residual networks. Results: 1. Recognition of a protein class (GPCRs vs globins). Globins and GPCR-like proteins are typical α-helical proteins. Each of these protein families has a large number of representatives (including those with known structure) but we used only 8 structures from every family. 12,000 of diffraction patterns were used for training and 4,000 patterns for testing. Results indicate that all considered networks are able to recognize the protein family type with high accuracy. 2. Recognition of the number of protein molecules in the liposome. We considered the usage of lyposomes as carriers of membrane or globular proteins for sample delivery in XFEL experiments in order to improve the X-ray beam hit rate. Three sets of diffractograms for liposomes of various radius were calculated, including diffractograms for empty liposomes, liposomes loaded with 5 bacteriorhodopsin molecules, and liposomes loaded with 10 bacteriorhodopsin molecules. The training set consisted of 23625 diffraction patterns, and test set of 7875 patterns. We found that all networks used in our study were able to identify the number of protein molecules in liposomes independent of the liposome radius. Our findings make this approach rather promising for the usage of liposomes as protein carriers in XFEL experiments. Conclusion: Thus, the performed numerical experiments show that the use of neural network algorithms for the recognition of diffraction images from single macromolecular particles makes it possible to determine changes in the structure at the angstrom scale. Diffraction ; Crystallography ; Convolutional neural network ; Computer science ; Materials science ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Physics ; Chemistry ; Optics ; Biology Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Monika Eliasz-Kowalska | Agata Z. Wojtal
Source:  (article, 2021-03-12)
Field: Materials Science — Biomaterials

Limnological Characteristics and Diatoms in Lakes of North-Eastern Poland &amp;lt;sup&amp;gt;†&amp;lt;/sup&amp;gt; Determination of the relationships between environmental factors and diatom assemblages is usually made for several hundred lakes spread over a large area. However the analysis of several lakes located near Lake Wigry also gives interesting results. Lakes in Wigry National Park (Poland) with broad similarity of geological origin show clear limnological, physical, and chemical differences. We report analysis of how these dissimilarities influence diatom assemblages. Unsupervised machine learning using Hierarchical Cluster Analysis showed that the studied lakes can be divided into three groups: (1) disharmonic, (2) harmonious with greater human impact on the environment, and (3) harmonious with a more limited human impact. In the dysharmonic group, the most distinctive dominant species was Tabellaria flocculosa, accompanied by Eunotia mucophila, E. rhomboidea, Stauroforma exiguiformis, Nitzschia gracilis as most frequent codominants. Harmonic lakes were dominated by Achnanthidium minutissimum and Encyonopsis microcephala. Most frequent codominant species were Fragilaria subconstricta, Brachysira microcephala, Encyonopsis cesatii and Eunotia arcubus. Studied lakes were abound in rare species such as Gomphonema vibrio, Fragilaria subconstricta, Aneumastus balticus, Navicula praeterita, N. subalpina or Sellaphora bacillum. The three groups vary in dominance structures, as reference to the Dominance Index (DI) made clear (mean values being: (1) &minus;70.54%, (2) &minus;72%, and (3) &minus;54.58%, Generalized Linear Models with the categorical independent variable (group) showed significant differences between groups (for 1&ndash;3, 2&ndash;3) p value &lt; 0.05). Lakes influenced by anthropopressure and disharmonic ones had the strongest dominance structure. DI differences between the groups are consistent with the Species Pool Hypothesis (SPH), while studied differences can be said to result from natural geological dissimilarities, as well as disparate anthropogenic impacts. Fragilaria ; Dominance (genetics) ; Diatom ; Ecology ; Subtropics ; Nitzschia ; Geography ; Biology ; Phytoplankton Diatoms and Algae Research ; Aquatic Invertebrate Ecology and Behavior ; Environmental Science and Water Management