jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Osman Mamun | Kirsten T. Winther | Jacob R. Boes | Thomas Bligaard
Source: npj Computational Materials (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org. Bimetallic strip ; Alloy ; Catalysis ; Adsorption ; Bayesian probability ; Materials science ; Energy (signal processing) ; Chemical engineering ; Computer science ; Metallurgy ; Artificial intelligence ; Chemistry ; Statistics ; Mathematics ; Engineering ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Sunil G. Shelar | Vilas K. Mahajan | Sandip P. Patil | Gunvant H. Sonawane
Source: SN Applied Sciences (article, 2020-04-05)
Field: Materials Science — Materials Chemistry

Effect of doping parameters on photocatalytic degradation of methylene blue using Ag doped ZnO nanocatalyst Nanomaterial-based catalyst ; Photocatalysis ; Methylene blue ; Scanning electron microscope ; Dopant ; Materials science ; Doping ; Degradation (telecommunications) ; Chemical engineering ; Precipitation ; Nuclear chemistry ; Nanotechnology ; Catalysis ; Chemistry ; Nanoparticle ; Organic chemistry ; Composite material ; Optoelectronics ZnO doping and properties ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Valeria Lotito | Tomaso Zambelli
Source: Advances in Colloid and Interface Science (review, 2020-09-06)
Field: Materials Science — Materials Chemistry

Pattern detection in colloidal assembly: A mosaic of analysis techniques Characterization of the morphology, identification of patterns and quantification of order encountered in colloidal assemblies is essential for several reasons. First of all, it is useful to compare different self-assembly methods and assess the influence of different process parameters on the final colloidal pattern. In addition, casting light on the structures formed by colloidal particles can help to get better insight into colloidal interactions and understand phase transitions. Finally, the growing interest in colloidal assemblies in materials science for practical applications going from optoelectronics to biosensing imposes a thorough characterization of the morphology of colloidal assemblies because of the intimate relationship between morphology and physical properties (e.g. optical and mechanical) of a material. Several image analysis techniques developed to investigate images (acquired via scanning electron microscopy, digital video microscopy and other imaging methods) provide variegated and complementary information on the colloidal structures under scrutiny. However, understanding how to use such image analysis tools to get information on the characteristics of the colloidal assemblies may represent a non-trivial task, because it requires the combination of approaches drawn from diverse disciplines such as image processing, computational geometry and computational topology and their application to a primarily physico-chemical process. Moreover, the lack of a systematic description of such analysis tools makes it difficult to select the ones more suitable for the features of the colloidal assembly under examination. In this review we provide a methodical and extensive description of real-space image analysis tools by explaining their principles and their application to the investigation of two-dimensional colloidal assemblies with different morphological characteristics. Characterization (materials science) ; Nanotechnology ; Computer science ; Process (computing) ; Colloidal particle ; Colloid ; Materials science ; Engineering ; Chemical engineering Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Cultural Heritage Materials Analysis
Authors: Qi Ou | Qian Peng | Zhigang Shuai
Source: Nature Communications (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Computational screen-out strategy for electrically pumped organic laser materials Electrically pumped organic lasing is one of the most challenging issues in organic optoelectronics. We present a systematic theoretical investigation to screen out electrical pumping lasing molecules over a wide range of organic materials. With the electronic structure information obtained from time-dependent density functional theory, we calculate multiple photophysical parameters of a set of optical pumping organic laser molecules in our self-developed molecular material property prediction package (MOMAP) to judge whether the electrically pumped lasing conditions can be satisfied, namely, to avoid reabsorption from excitons and/or polarons, and the accumulation of triplet excitons. In addition, a large oscillator strength of S<sub>1</sub> and weak intermolecular π-π interaction are preferred. With these criteria, we are able to conclude that BP3T, BSBCz, and CzPVSBF compounds are promising candidates for electrically pumped lasing, and the proposed computational strategy could serve as a general protocol for molecular design of organic lasing materials. Lasing threshold ; Materials science ; Optoelectronics ; Laser ; Exciton ; Oscillator strength ; Density functional theory ; Intermolecular force ; Optical pumping ; Molecule ; Chemistry ; Optics ; Physics ; Computational chemistry ; Condensed matter physics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Perovskite Materials and Applications
Authors: Yun Zhang | Xiaojie Xu
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Relative cooling power modeling of lanthanum manganites using Gaussian process regression Efficient solid-state refrigeration techniques at room temperature have drawn increasing attention due to their potential for improving energy efficiency of refrigeration, air-conditioning, and temperature-control systems without using harmful gas in conventional gas compression techniques. Recent developments of increased magnetocaloric effects and relative cooling power (RCP) in ferromagnetic lanthanum manganites show promising results of further developments in magnetic refrigeration devices. By incorporating chemical substitutions, oxygen content modifications, and various synthesis methods, these manganites experience lattice distortions from perovskite cubic structures to orthorhombic structures. Lattice distortions, revealed by changes in lattice parameters, have significant influences on adiabatic temperature changes and isothermal magnetic entropy changes, and thus RCP. Empirical results and previous models through thermodynamics and first-principles have shown that changes in lattice parameters correlate with those in RCP, but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed to find statistical correlations and predict RCP based on lattice parameters among lanthanum manganites. This modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of RCP and understandings of magnetic phase transformations and magnetocaloric effects in lanthanum manganites. Magnetic refrigeration ; Orthorhombic crystal system ; Adiabatic process ; Refrigeration ; Isothermal process ; Thermodynamics ; Ferromagnetism ; Materials science ; Condensed matter physics ; Lattice (music) ; Gaussian ; Lanthanum ; Magnetization ; Statistical physics ; Chemistry ; Crystal structure ; Physics ; Magnetic field ; Computational chemistry Magnetic and transport properties of perovskites and related materials ; Advanced Thermoelectric Materials and Devices ; Optical properties and cooling technologies in crystalline materials
Authors: Khawla Seddiki | Philippe Saudemont | Fŕed́eric Precioso | Nina Ogrinc | Maxence Wisztorski | Michel Salzet | Isabelle Fournier | Arnaud Droit
Source: Nature Communications (article, 2020-11-05)
Field: Chemistry — Spectroscopy

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available. Computer science ; Transfer of learning ; Preprocessor ; Convolutional neural network ; Artificial intelligence ; Machine learning ; Raw data ; Data pre-processing ; Representation (politics) ; Pattern recognition (psychology) ; Deep learning ; Artificial neural network Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Isotope Analysis in Ecology
Authors: Joris Paret | Robert L. Jack | Daniele Coslovich
Source: Apollo (University of Cambridge) (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Assessing the structural heterogeneity of supercooled liquids through community inference. We present an information-theoretic approach inspired by distributional clustering to assess the structural heterogeneity of particulate systems. Our method identifies communities of particles that share a similar local structure by harvesting the information hidden in the spatial variation of two- or three-body static correlations. This corresponds to an unsupervised machine learning approach that infers communities solely from the particle positions and their species. We apply this method to three models of supercooled liquids and find that it detects subtle forms of local order, as demonstrated by a comparison with the statistics of Voronoi cells. Finally, we analyze the time-dependent correlation between structural communities and particle mobility and show that our method captures relevant information about glassy dynamics. Voronoi diagram ; Cluster analysis ; Supercooling ; Inference ; Computer science ; Statistical physics ; Artificial intelligence ; Data mining ; Machine learning ; Mathematics ; Geography ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Sensory Analysis and Statistical Methods
Authors: Jigyasa Nigam | Sergey N. Pozdnyakov | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2020-09-22)
Field: Materials Science — Materials Chemistry

Recursive evaluation and iterative contraction of <i>N</i>-body equivariant features Mapping an atomistic configuration to a symmetrized N-point correlation of a field associated with the atomic positions (e.g., an atomic density) has emerged as an elegant and effective solution to represent structures as the input of machine-learning algorithms. While it has become clear that low-order density correlations do not provide a complete representation of an atomic environment, the exponential increase in the number of possible N-body invariants makes it difficult to design a concise and effective representation. We discuss how to exploit recursion relations between equivariant features of different order (generalizations of N-body invariants that provide a complete representation of the symmetries of improper rotations) to compute high-order terms efficiently. In combination with the automatic selection of the most expressive combination of features at each order, this approach provides a conceptual and practical framework to generate systematically improvable, symmetry adapted representations for atomistic machine learning. Equivariant map ; Recursion (computer science) ; Homogeneous space ; Representation (politics) ; Computer science ; Theoretical computer science ; Mathematics ; Algorithm ; Algebra over a field ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Praveen Kumar Sharma | Andleeb Amin | Manjeet Kumar
Source: The Open Medicinal Chemistry Journal (article, 2020-09-14)
Field: Chemistry — Organic Chemistry

A Review: Medicinally Important Nitrogen Sulphur Containing Heterocycles Nitrogen sulphur containing heterocycles have specific properties due to which they can be used as a potential material in a different type of industries such as medicinal/pharmaceutical, paint, packing and textile, required for various chemical, physical operations and their use as products. Especially dyes, paint, agrochemicals, medicine, etc . make them more significant. In present days, Nitrogen-Sulfur heterocycles are repeatedly attracting the interest of chemists due to their exceptional bioactive behavior. The present study is a review of the work carried out by a chemist in the discovery of new, effective, medicinally important heterocyclic compounds. The present review basically focused on nitrogen-sulfur heterocycles of potential therapeutic interest, especially with thiazole, thiazine, pyrimidine, morpholine and piperazine heterosystems, benzothiazines, pyrazole-benzothiazines, morpholine-benzothiazines, piperazine-benzothiazines and pyrimidine-benzothiazoles, mainly due to their unique structural features, which enable them to exhibit a number of biological and pharmacological activities. Due to a novel mode of action, a broad spectrum of activity, lesser toxicity towards mammalian cells, and suitable profiles towards humans have triggered the use of Nitrogen Sulphur containing heterocycles in designing and synthesizing their derivatives with better properties. The overall objective of the review is to discuss the importance of novel biodynamic structurally diverse heterocycles of potential therapeutic interest: pyrimidine, morpholine, piperazine, pyrozole, benothiazoles, pyrimidobenzothiazoles, 4H-1,4-benzothiazines, pyrazolyl-benzothiazines, morpholinyl-benzothiazines and piperazinylbenzothiazines in order to have access to important commercial molecules for the search of better future. Morpholine ; Piperazine ; Benzothiazole ; Thiazole ; Chemistry ; Sulfur ; Pyrimidine ; Combinatorial chemistry ; Pyrazole ; Organic chemistry ; Stereochemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Siqi Yan | Xiaolong Zhu | Jianji Dong | Yunhong Ding | Sanshui Xiao
Source: Nanophotonics (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

2D materials integrated with metallic nanostructures: fundamentals and optoelectronic applications Abstract Due to their novel electronic and optical properties, atomically thin layered two‐dimensional (2D) materials are becoming promising to realize novel functional optoelectronic devices including photodetectors, modulators, and lasers. However, light–matter interactions in 2D materials are often weak because of the atomic‐scale thickness, thus limiting the performances of these devices. Metallic nanostructures supporting surface plasmon polaritons show strong ability to concentrate light within subwavelength region, opening thereby new avenues for strengthening the light–matter interactions and miniaturizing the devices. This review starts to present how to use metallic nanostructures to enhance light–matter interactions in 2D materials, mainly focusing on photoluminescence, Raman scattering, and nonlinearities of 2D materials. In addition, an overview of ultraconfined acoustic‐like plasmons in hybrid graphene–metal structures is given, discussing the nonlocal response and quantum mechanical features of the graphene plasmons and metals. Then, the review summarizes the latest development of 2D material–based optoelectronic devices integrated with plasmonic nanostructures. Both off‐chip and on‐chip devices including modulators and photodetectors are discussed. The potentials of hybrid 2D materials plasmonic optoelectronic devices are finally summarized, giving the future research directions for applications in optical interconnects and optical communications. Nanomaterials ; Materials science ; Nanotechnology ; Nanostructure ; Metal ; Optoelectronics ; Engineering physics ; Engineering ; Metallurgy 2D Materials and Applications ; Plasmonic and Surface Plasmon Research ; Nanowire Synthesis and Applications
Authors: Amy Verhagen | Antonios Kelarakis
Source: Nanomaterials (review, 2020-08-05)
Field: Materials Science — Materials Chemistry

Carbon Dots for Forensic Applications: A Critical Review Owing to their superior fluorescence performance, inexpensive synthesis and nontoxic nature, carbon dots (C-dots) are systematically explored in a variety of applications; in this review, we outline and critically discuss recent trends with respect to their potential exploitation in criminal investigation, forensic toxicology and anti-counterfeit interventions. Capitalising on their colour-tuneable behaviour (in the sense that they adopt different colours with respect to the incident radiation), C-dot-based compositions are ideal for the visual enhancement of latent fingerprints, affording improved contrast against multicoloured and patterned backgrounds. As highly sensitive and highly selective optical nanoprobes, C-dots show excellent analytical performance in detecting biological compounds, drugs, explosives, heavy metals and poisonous reactants. In addition, benefiting from their versatile structural and chemical composition, C-dots can be incorporated into ink and polymeric formulations capable of functioning as a new generation of cost-effective barcodes and security nanotags for object authentication and anti-counterfeit applications. Translating these encouraging research outcomes into real-life innovations with significant social and economic impact requires an open, multidisciplinary approach and a close synergy between materials scientists, biologists, forensic investigators and digital engineers. Counterfeit ; Nanotechnology ; Authentication (law) ; Computer science ; Materials science ; Computer security ; Political science Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Mercury impact and mitigation studies
Authors: Andreas Ankenbauer | Richard A Schäfer | Sandra C. Viegas | Vânia Pobre | Björn Voß | Cecília M. Arraiano | Ralf Takors
Source: Microbial Biotechnology (article, 2020-04-08)
Field: Materials Science — Biomaterials

<i>Pseudomonas putida</i> KT2440 is naturally endowed to withstand industrial‐scale stress conditions Pseudomonas putida is recognized as a very promising strain for industrial application due to its high redox capacity and frequently observed tolerance towards organic solvents. In this research, we studied the metabolic and transcriptional response of P. putida KT2440 exposed to large-scale heterogeneous mixing conditions in the form of repeated glucose shortage. Cellular responses were mimicked in an experimental setup comprising a stirred tank reactor and a connected plug flow reactor. We deciphered that a stringent response-like transcriptional regulation programme is frequently induced, which seems to be linked to the intracellular pool of 3-hydroxyalkanoates (3-HA) that are known to serve as precursors for polyhydroxyalkanoates (PHA). To be precise, P. putida is endowed with a survival strategy likely to access cellular PHA, amino acids and glycogen in few seconds under glucose starvation to obtain ATP from respiration, thereby replenishing the reduced ATP levels and the adenylate energy charge. Notably, cells only need 0.4% of glucose uptake to build those 3-HA-based energy buffers. Concomitantly, genes that are related to amino acid catabolism and β-oxidation are upregulated during the transient absence of glucose. Furthermore, we provide a detailed list of transcriptional short- and long-term responses that increase the cellular maintenance by about 17% under the industrial-like conditions tested. Pseudomonas putida ; Polyhydroxyalkanoates ; Catabolism ; Biochemistry ; Chemistry ; Cell biology ; Biology ; Metabolism ; Gene ; Bacteria biodegradable polymer synthesis and properties ; Microbial Metabolic Engineering and Bioproduction ; Enzyme Catalysis and Immobilization
Authors: José Julio Gutiérrez Moreno | Jiang Cao | Marco Fronzi | M. Hussein N. Assadi
Source: Materials for Renewable and Sustainable Energy (review, 2020-08-05)
Field: Materials Science — Materials Chemistry

A review of recent progress in thermoelectric materials through computational methods Abstract Reducing our overwhelming dependence on fossil fuels requires groundbreaking innovations in increasing our efficiency in energy consumption for current technologies and moving towards renewable energy sources. Thermoelectric materials can help in achieving both goals. Moreover, because of recent advances in high-performance computing, researchers more increasingly rely on computational methods in discovering new thermoelectric materials with economically feasible performance. In this article, significant thermoelectric materials discovered through these computational methods are systematically reviewed. Furthermore, the primary computational tools that aid the design of the next-generation thermoelectric materials are introduced and discussed. These techniques include various levels of density functional theory, electronic transport simulations, and phonon calculations. Thermoelectric effect ; Thermoelectric materials ; Renewable energy ; Computer science ; Engineering physics ; Systems engineering ; Engineering ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications