jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin-Woong Lee | Woon Bae Park | Jin Hee Lee | Satendra Pal Singh | Kee‐Sun Sohn
Source: Nature Communications (article, 2020-01-03)
Field: Materials Science — Materials Chemistry

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification. Convolutional neural network ; Phase (matter) ; Materials science ; Mixing (physics) ; Deep learning ; Computer science ; Diffraction ; Powder diffraction ; Identification (biology) ; Chemical engineering ; Artificial intelligence ; Crystallography ; Chemistry ; Physics ; Optics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear materials and radiation effects
Authors: Leili Mohammadi | Abbas Rahdar | Edris Bazrafshan | Hamid Dahmardeh | Md. Abu Bin Hasan Susan | George Z. Kyzas
Source: Processes (review, 2020-04-10)
Field: Chemistry — Analytical Chemistry

Petroleum Hydrocarbon Removal from Wastewaters: A Review Oil pollutants, due to their toxicity, mutagenicity, and carcinogenicity, are considered a serious threat to human health and the environment. Petroleum hydrocarbons compounds, for instance, benzene, toluene, ethylbenzene, xylene, are among the natural compounds of crude oil and petrol and are often found in surface and underground water as a result of industrial activities, especially the handling of petrochemicals, reservoir leakage or inappropriate waste disposal processes. Methods based on the conventional wastewater treatment processes are not able to effectively eliminate oil compounds, and the high concentrations of these pollutants, as well as active sludge, may affect the activities and normal efficiency of the refinery. The methods of removal should not involve the production of harmful secondary pollutants in addition to wastewater at the level allowed for discharge into the environment. The output of sewage filtration by coagulation and dissolved air flotation (DAF) flocculation can be transferred to a biological reactor for further purification. Advanced coagulation methods such as electrocoagulation and flocculation are more advanced than conventional physical and chemical methods, but the major disadvantages are the production of large quantities of dangerous sludge that is unrecoverable and often repelled. Physical separation methods can be used to isolate large quantities of petroleum compounds, and, in some cases, these compounds can be recycled with a number of processes. The great disadvantage of these methods is the high demand for energy and the high number of blockages and clogging of a number of tools and equipment used in this process. Third-party refinement can further meet the objective of water reuse using methods such as nano-filtration, reverse osmosis, and advanced oxidation. Adsorption is an emergency technology that can be applied using minerals and excellent materials using low-cost materials and adsorbents. By combining the adsorption process with one of the advanced methods, in addition to lower sludge production, the process cost can also be reduced. Environmental science ; Wastewater ; Pollutant ; Waste management ; Oil refinery ; Petrochemical ; Petroleum ; BTEX ; Hazardous waste ; Petroleum product ; Sewage ; Chemistry ; Xylene ; Environmental engineering ; Toluene ; Engineering Petroleum Processing and Analysis ; Oil Spill Detection and Mitigation ; Microbial bioremediation and biosurfactants
Authors: Brooke E. Husic | Nicholas E. Charron | Dominik Lemm | Jiang Wang | Adrià Pérez | Maciej Majewski | Andreas Krämer | Yaoyi Chen | Simon Olsson | Gianni de Fabritiis | Frank Noé | Cecilia Clementi
Source: The Journal of Chemical Physics (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Coarse graining molecular dynamics with graph neural networks Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems. Granularity ; Subnetwork ; Molecular dynamics ; Artificial neural network ; Force field (fiction) ; Limit (mathematics) ; Graph Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: Sanjeevamuthu Suganthi | Shanmugam Vignesh | Jeyaperumal Kalyana Sundar | V. Raj
Source: Applied Water Science (article, 2020-03-30)
Field: Materials Science — Biomaterials

Fabrication of PVA polymer films with improved antibacterial activity by fine-tuning via organic acids for food packaging applications Abstract In this study, organic acids were used as cross-linker with polyvinyl alcohol (PVA) films prepared by a solution-casting method for food packing applications. The effect of incorporating three different organic acids, i.e., malic acid (MA), tartaric acid (TA), and lactic acid (LA), on the physicochemical and biological properties of PVA was explored in detail. The crystalline phase, optical absorption, and functional groups were examined via UV–Vis and Fourier transform infrared spectroscopy. Thermal, microstructural, and surface investigations were conducted by thermogravimetric analysis and scanning electron microscopy, and the antibacterial activity was evaluated. The surface topography and roughness were found to have a strong effect on the bactericidal properties of the films, as confirmed by atomic force microscopy. Among the considered films, PVA/LA exhibited the highest bacterial inhibition, which was largely due to its capacity to modify the local pH and alter the permeability of the microbial layer by disrupting bacteria–substrate interaction. In general, the composite film was found to have attractive properties and can be considered as a food packaging material with low environmental impact based on polyvinyl alcohol. Polyvinyl alcohol ; Thermogravimetric analysis ; Materials science ; Food packaging ; Chemical engineering ; Polymer ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Composite number ; Tartaric acid ; Organic chemistry ; Composite material ; Chemistry ; Citric acid Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Antimicrobial agents and applications
Authors: Dirk Poelman | David Van der Heggen | Jiaren Du | Ewoud Cosaert | Philippe F. Smet
Source: Journal of Applied Physics (article, 2020-12-24)
Field: Materials Science — Materials Chemistry

Persistent phosphors for the future: Fit for the right application When the bright green-emitting SrAl2O4:Eu,Dy persistent phosphor was described in the literature in 1996, this presented a real breakthrough in performance, both in terms of initial brightness and afterglow duration. Since then, many new persistent phosphors, with emission spanning from the ultraviolet to the near infrared, have been developed. Very few materials, however, reach a similar afterglow time and intensity as SrAl2O4:Eu,Dy, which is still considered the benchmark phosphor. The present paper discusses the reasons for this—seemingly—fundamental limitation and gives directions for further improvements. An overview is given of the preparation methods of persistent phosphors and their properties. Much attention is paid to the correct evaluation of a persistent phosphor in absolute units rather than vague terms or definitions. State of the art persistent phosphors are currently used extensively in emergency signage, indicators, and toys. Many more applications could be possible by tuning the range of trap depths used for energy storage. Very shallow traps could be used for temperature monitoring in, for example, cryopreservation. Deeper traps are useful for x-ray imaging and dosimetry. Next to these applications, a critical evaluation is made of the possibilities of persistent phosphors for applications such as solar energy storage and photocatalysis. Phosphor ; Afterglow ; Persistent luminescence ; Phosphorescence ; Brightness ; Environmental science ; Computer science ; Materials science ; Nuclear engineering ; Engineering physics ; Optoelectronics ; Optics ; Physics ; Engineering ; Astrophysics ; Gamma-ray burst Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques ; Ammonia Synthesis and Nitrogen Reduction
Authors: George S. Fanourgakis | Konstantinos Gkagkas | Emmanuel Tylianakis | George E. Froudakis
Source: Journal of the American Chemical Society (article, 2020-02-04)
Field: Chemistry — Inorganic Chemistry

A Universal Machine Learning Algorithm for Large-Scale Screening of Materials Application of machine learning (ML) methods for the determination of the gas adsorption capacities of nanomaterials, such as metal-organic frameworks (MOF), has been extensively investigated over the past few years as a computationally efficient alternative to time-consuming and computationally demanding molecular simulations. Depending on the thermodynamic conditions and the adsorbed gas, ML has been found to provide very accurate results. In this work, we go one step further and we introduce chemical intuition in our descriptors by using the "type" of the atoms in the structure, instead of the previously used building blocks, to account for the chemical character of the MOF. ML predictions for the methane and carbon dioxide adsorption capacities of several tens of thousands of hypothetical MOFs are evaluated at various thermodynamic conditions using the random forest algorithm. For all cases examined, the use of atom types instead of building blocks leads to significantly more accurate predictions, while the number of MOFs needed for the training of the ML algorithm in order to achieve a specified accuracy can be reduced by an order of magnitude. More importantly, since practically there are an unlimited number of building blocks that materials can be made of but a limited number of atom types, the proposed approach is more general and can be considered as universal. The universality and transferability was proved by predicting the adsorption properties of a completely different family of materials after the training of the ML algorithm in MOFs. Transferability ; Universality (dynamical systems) ; Intuition ; Adsorption ; Chemistry ; Algorithm ; Methane ; Atom (system on chip) ; Computer science ; Statistical physics ; Machine learning ; Physical chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Koki Kitai | Jiang Guo | Shenghong Ju | Shu Tanaka | Koji Tsuda | Junichiro Shiomi | Ryo Tamura
Source: Physical Review Research (article, 2020-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing metamaterials with quantum annealing and factorization machines The authors provide an algorithm to incorporate quantum annealing into automated materials discovery. Their scheme can be used to design complex structures of wavelength selective radiators showing much better agreement with the thermal atmospheric transparency window. Quantum annealing ; Metamaterial ; Simulated annealing ; Annealing (glass) ; Quantum ; Factorization ; Wavelength ; Computer science ; Transparency (behavior) ; Thermal ; Optoelectronics ; Materials science ; Optics ; Physics ; Quantum computer ; Algorithm ; Quantum mechanics ; Composite material Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Photonic Crystals and Applications
Authors: Wilhelm Kirchgässner | Oliver Wallscheid | Joachim Böcker
Source: IEEE Transactions on Power Electronics (article, 2020-12-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Estimating Electric Motor Temperatures With Deep Residual Machine Learning Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented. Computer science ; Bayesian optimization ; Residual ; Estimator ; Stator ; Artificial intelligence ; Interpretability ; Machine learning ; Hyperparameter ; Convolutional neural network ; Algorithm ; Engineering ; Mathematics ; Mechanical engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Machine Fault Diagnosis Techniques
Authors: Karol Biernacki | Mateusz Daśko | Olga Ciupak | Konrad Kubiński | Janusz Rachoń | Sebastian Demkowicz
Source: Pharmaceuticals (review, 2020-05-29)
Field: Chemistry — Organic Chemistry

Novel 1,2,4-Oxadiazole Derivatives in Drug Discovery Five-membered 1,2,4-oxadiazole heterocyclic ring has received considerable attentionbecause of its unique bioisosteric properties and an unusually wide spectrum of biological activities.Thus, it is a perfect framework for the novel drug development. After a century since the1,2,4-oxadiazole have been discovered, the uncommon potential attracted medicinal chemists'attention, leading to the discovery of a few presently accessible drugs containing 1,2,4-oxadiazoleunit. It is worth noting that the interest in a 1,2,4-oxadiazoles' biological application has been doubledin the last fifteen years. Herein, after a concise historical introduction, we present a comprehensiveoverview of the recent achievements in the synthesis of 1,2,4-oxadiazole-based compounds and themajor advances in their biological applications in the period of the last five years as well as briefremarks on prospects for further development. Oxadiazole ; Drug discovery ; Broad spectrum ; Computer science ; Nanotechnology ; Combinatorial chemistry ; Chemistry ; Bioinformatics ; Biology ; Materials science ; Organic chemistry Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Kjell Jorner | Tore Brinck | Per‐Ola Norrby | David Buttar
Source: Chemical Science (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal mol<sup>-1</sup> for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100-150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints. Reactivity (psychology) ; Nucleophile ; Computer science ; Nucleophilic substitution ; Machine learning ; Artificial intelligence ; Chemistry ; Computational chemistry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Timothy Sheils | Stephen L. Mathias | Keith J. Kelleher | Vishal B. Siramshetty | Ðắc-Trung Nguyễn | Cristian Bologa | Lars Juhl Jensen | D. Vidović | Amar Koleti | Stephan C. Schürer | Anna Waller | Jeremy J. Yang | Jayme Holmes | Giovanni Bocci | Noel Southall | Poorva Dharkar | Ewy A. Mathé | Anton Simeonov | Tudor I. Oprea
Source: Nucleic Acids Research (article, 2020-10-14)
Field: Chemistry — Spectroscopy

TCRD and Pharos 2021: mining the human proteome for disease biology In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome. Biology ; Proteome ; Computational biology ; Human disease ; Bioinformatics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Rare Diseases ; vaccines and immunoinformatics approaches
Authors: O. I. Koifman | Tatyana A. Ageeva | I. P. Beletskaya | Alexei D. Averin | Alexei A. Yakushev | Larisa G. Tomilova | Tatiana V. Dubinina | А. Yu. Tsivadze | Yulia G. Gorbunova | Alexander G. Martynov | Dmitri V. Konarev | Salavat S. Khasanov | Rimma N. Lyubovskaya | Т. Н. Ломова | Viktor V. Korolev | Eduard I. Zenkevich | Thomas Blaudeck | Christian von Borczyskowski | Dietrich R. T. Zahn | А. Ф. Миронов | Natal’ya A. Bragina | Аrtem V. Еzhov | Kseniya A. Zhdanova | Pavel A. Stuzhin | Georgy L. Pakhomov | Nataliya V. Rusakova | Nikolay Semenishyn | Sergey S. Smola | V. I. Parfenyuk | А. С. Вашурин | Sergei V. Makarov | Ilia А. Dereven’kov | Н. Ж. Мамардашвили | Tigran S. Kurtikyan | Garik G. Martirosyan | V. A. Burmistrov | V. V. Aleksandriiskii | Igor V. Novikov | Dmitriy A. Pritmov | Mikhail A. Grin | Nikita Suvorov | Anatoliy A. Tsigankov | Alexey Yu. Fedorov | Natalia S. Kuzmina | Alexander V. Nyuchev | Vasilii F. Otvagin | А. В. Кустов | D. V. Belykh | Д. Б. Березин | Anna B. Solovieva | Peter Timashev | Е. Р. Милаева | Yulia A. Gracheva | М. А. Додохова | А. В. Сафроненко | D.B. Shpakovsky | S. А. Syrbu | Yury A. Gubarev | A. N. Kiselev | Mikhail О. Koifman | N. Sh. Lebedevа | Elena S. Yurina
Source: Macroheterocycles (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Macroheterocyclic Compounds - a Key Building Block in New Functional Materials and Molecular Devices The review discusses the latest advances in the directed synthesis and application of macroheterocyclic compounds in science, engineering and technology, viz. as catalysts for various processes in photo-and electrocatalysis, optical chemosensors for metal cations, selective receptors of organic compounds, inductors and selectors, in nonlinear optics, organic electronics, as magnets, photosensitizers for PDT of a number of oncological diseases and for antimicrobial PDT, etc. Key (lock) ; Block (permutation group theory) ; Materials science ; Computer science ; Nanotechnology ; Computer security ; Mathematics Inorganic and Organometallic Chemistry