jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Habib Asseiss Neto | Wanessa Luciene Fonseca Tavares | Daniela Cotta Ribeiro | Ronnie Alves | L.M. Fonseca | Sérgio Campos
Source: BioData Mining (article, 2019-07-08)
Field: Chemistry — Analytical Chemistry

On the utilization of deep and ensemble learning to detect milk adulteration The proposed methodology uses machine learning techniques on milk spectral data. It is able to predict common adulterations that occur in the dairy industry. Both deep and ensemble tree learners were evaluated considering binary and multiclass classifications and the results were compared. The proposed neural network architecture is able to outperform the composition recognition made by the FTIR equipment and by commonly used methods in the dairy industry. Computer science ; Ensemble learning ; Artificial intelligence ; Data science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Kaushalya Madhawa | Katushiko Ishiguro | Kosuke Nakago | Motoki Abe
Source: arXiv (Cornell University) (preprint, 2019-05-28)
Field: Materials Science — Materials Chemistry

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows. We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties. Invertible matrix ; Molecular graph ; Adjacency matrix ; Graph ; Adjacency list ; Computer science ; Mathematics ; Theoretical computer science ; Algorithm ; Pure mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Ravsaheb H. Rathod | Suraj R. Chaudhari | Amod S. Patil | Atul A. Shirkhedkar
Source: Future Journal of Pharmaceutical Sciences (article, 2019-10-31)
Field: Chemistry — Spectroscopy

Ultra-high performance liquid chromatography-MS/MS (UHPLC-MS/MS) in practice: analysis of drugs and pharmaceutical formulations Abstract Background UHPLC-MS/MS is connected in various research facilities for the qualitative and quantitative investigation of a pharmaceutical substance, pharmaceutical items, and biological specimen. Main body The commence review article is an endeavor to offer pervasive awareness around assorted aspects and details about the UHPLC-MS/MS and related techniques with the aim on practice to an estimation of medicinal active agents in the last 10 years. The article also focused on isolation, separation, and characterization of present impurity in drug and biological samples. Conclusion Review article compiles a general overview of medicinally important drugs and their analysis with UHPLC-MS/MS. It gives fundamental thought regarding applications of UHPLC-MS/MS for the study on safety limit. The summary of developed UHPLC-MS/MS methods gives a contribution to the future trend and limitations in this area of research. Qualitative analysis ; Isolation (microbiology) ; Chromatography ; Biochemical engineering ; Computer science ; Chemistry ; Engineering ; Qualitative research ; Biology ; Bioinformatics Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety
Authors: Tao Pan | Wu Zhi | Alex C.K. Yip
Source: Catalysts (review, 2019-03-17)
Field: Chemistry — Inorganic Chemistry

Advances in the Green Synthesis of Microporous and Hierarchical Zeolites: A Short Review Hierarchical zeolites have been extensively studied due to their enhancement of intra-crystalline diffusion, which leads to the improved catalytic activity and resistance to coking-deactivation. Traditional synthesis strategies of hierarchical zeolites via post-treatment or directing synthesis with the aid of mesoporous template are often characterized by high energy consumption and substantial use of expensive and environmentally unfriendly organic templates. In the recent decade, new green synthesis protocols have been developed for the effective synthesis of conventional and hierarchical zeolites. In this review, the latest advancements on the green synthesis of hierarchical zeolites are summarized and discussed in detail. Microporous material ; Mesoporous material ; Nanotechnology ; Template ; Materials science ; Catalysis ; Biochemical engineering ; Chemistry ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Prashant Bhimrao Koli | Kailas Haribhau Kapadnis | U. G. Deshpande
Source: Journal of nanostructure in chemistry (article, 2019-04-05)
Field: Materials Science — Materials Chemistry

Nanocrystalline-modified nickel ferrite films: an effective sensor for industrial and environmental gas pollutant detection The inverse spinel nanocrystalline pure and doped nickel ferrite particles were synthesized by co-precipitation method. The thick films of both pure and doped nickel ferrite nanoparticles were prepared by coating the material on glass substrate via screen printing method. The XRD was used to confirm cubic, crystalline nickel ferrites. The scanning electron microscopy confirms nanosized, cubic nanoparticles of nickel ferrite, and their morphology was investigated. The energy-dispersive spectroscopy was used to comprise elemental composition for pure and doped ferrites. Transmission electron microscopy was attributed for investigation of surface morphology, crystal structure identification of nickel ferrites. The FT-IR was used to find the vibrational frequencies, symmetric, asymmetric stretching and bending modes of metal oxide linkage. The thick films of nickel ferrite were employed for sensing phenomenon of gases such as LPG, NO2, CH3-OH, C2H5-OH, NH3 and petrol vapours. The pure nickel ferrite showed excellent results for ammonia and nitrogen dioxide gases up to 90.42 and 86.42, respectively. Manganese- and cobalt-doped ferrites were excellent for ammonia and petrol vapours. Modified nickel ferrite effect of dopants cobalt and manganese was investigated. Pure and doped ferrites showed excellent response and recovery for ammonia, NO2, petrol vapours and LPG gases. The graphical abstract presenting the synthesis process of pure and modified nickel ferrite prepared by co-precipitation method. The prepared material ferrite thick films were exclusively utilized for the detection of some pollutant gases. Materials science ; Nickel ; Nanocrystalline material ; Ferrite (magnet) ; Chemical engineering ; Spinel ; Nanoparticle ; Zinc ferrite ; Scanning electron microscope ; Dopant ; Metallurgy ; Analytical Chemistry (journal) ; Doping ; Composite material ; Nanotechnology ; Chemistry ; Organic chemistry Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Copper-based nanomaterials and applications
Authors: Matthew Witman | Sanliang Ling | David M. Grant | Gavin S. Walker | Sapan Agarwal | Vitalie Stavila | Mark D. Allendorf
Source: The Journal of Physical Chemistry Letters (article, 2019-12-07)
Field: Materials Science — Materials Chemistry

Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H<sub>2</sub> pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure-property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated. Intermetallic ; Hydride ; Hydrogen storage ; Stability (learning theory) ; Materials science ; Alloy ; Thermodynamics ; Property (philosophy) ; Chemistry ; Computer science ; Artificial intelligence ; Metal ; Machine learning ; Physics ; Metallurgy Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Juan Ponce | Arturo Aquino | José Manuel Andújar
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks The automation of classification and grading of horticultural products attending to different features comprises a major challenge in food industry. Thus, focused on the olive sector, which boasts of a huge range of cultivars, it is proposed a methodology for olive-fruit variety classification, approaching it as an image classification problem. To that purpose, 2,800 fruits belonging to seven different olive varieties were photographed. After processing these initial captures by means of image processing techniques, the resulting set of images of individual fruits were used to train, and continuedly to externally validate, the implementations of six different Convolutional Neural Networks architectures. This, in order to compute the classifiers with which perform the variety categorization of the fruits. Remarkable hit rates were obtained after testing the classifiers on the corresponding external validation sets. Thus, it was yielded a top accuracy of 95.91% when using the Inception-ResnetV2 architecture. The results suggest that the proposed methodology, once integrated into industrial conveyor belts, promises to be an advanced solution to postharvest olive-fruit processing and classification. Convolutional neural network ; Categorization ; Computer science ; Contextual image classification ; Image processing ; Artificial intelligence ; Automation ; Artificial neural network ; Pattern recognition (psychology) ; Variety (cybernetics) ; Postharvest ; Machine learning ; Image (mathematics) ; Engineering ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Sergio Manzetti | Jean‐Christophe P. Gabriel
Source: International nano letters. (article, 2019-01-02)
Field: Materials Science — Materials Chemistry

Methods for dispersing carbon nanotubes for nanotechnology applications: liquid nanocrystals, suspensions, polyelectrolytes, colloids and organization control Carbon nanotubes (CNTs) are a central part of advanced nanomaterials and are used in state-of-the-art technologies, based on their high tensile strength, excellent thermal transfer properties, low-band gaps and optimal chemical and physical stability. Carbon nanotubes are also intriguing given their unique -electron-rich structures, which opens a variety of possibilities for modifications and alterations of their chemical and electronic properties. In this review, a comprehensive survey of the methods of solubilization of carbon nanotubes is presented, forming the methodological foundation for synthesis and manufacturing of modified nanomaterials. The methods presented herein show that solubilized carbon nanotubes have a great potential in being applied as reactants and components for advanced solar cell technologies, nanochemical compounds in electronics and as parts in thermal transfer management. An example lies in the preservation of the aromatic chemistry in CNTs and ligation of functional groups to their surfaces, which confers CNTs with an optimal potential as tunable Schottky contacts, or as parts in nanotransistors and nano-resistances. Future nanoelectronic circuits and structures can therefore depend more and more on how carbon nanotubes are modified and functionalized, and for this, solubilization is often a critical part of their fabrication process. This review is important, is in conjecture with the latest developments in synthesis and modification of CNTs, and provides the know-how for developing new CNT-based state-of-the-art technologies, particularly with emphasis on computing, catalysis, environmental remediation as well as microelectronics. Carbon nanotube ; Nanotechnology ; Materials science ; Nanomaterials ; Applications of nanotechnology ; Nanoelectronics ; Microelectronics Carbon Nanotubes in Composites ; Graphene research and applications ; Supercapacitor Materials and Fabrication
Authors: Cameron Webb | Swapnil Khadke | S. Schmidt | Carla B. Roces | Neil S. Forbes | Gillian Berrie | Yvonne Perrie
Source: Pharmaceutics (article, 2019-12-04)
Field: Materials Science — Biomaterials

The Impact of Solvent Selection: Strategies to Guide the Manufacturing of Liposomes Using Microfluidics The aim of this work was to assess the impact of solvent selection on the microfluidic production of liposomes. To achieve this, liposomes were manufactured using small-scale and bench-scale microfluidics systems using three aqueous miscible solvents (methanol, ethanol or isopropanol, alone or in combination). Liposomes composed of different lipid compositions were manufactured using these different solvents and characterised to investigate the influence of solvents on liposome attributes. Our studies demonstrate that solvent selection is a key consideration during the microfluidics manufacturing process, not only when considering lipid solubility but also with regard to the resultant liposome critical quality attributes. In general, reducing the polarity of the solvent (from methanol to isopropanol) increased the liposome particle size without impacting liposome short-term stability or release characteristics. Furthermore, solvent combinations such as methanol/isopropanol mixtures can be used to modify solvent polarity and the resultant liposome particle size. However, the impact of solvent choice on the liposome product is also influenced by the liposome formulation; liposomes containing charged lipids tended to show more sensitivity to solvent selection and formulations containing increased concentrations of cholesterol or pegylated-lipids were less influenced by the choice of solvent. Indeed, incorporation of 14 wt% or more of pegylated-lipid was shown to negate the impact of solvent selection. Liposome ; Solvent ; Microfluidics ; Solubility ; Chemistry ; Methanol ; Chromatography ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry Nanoparticle-Based Drug Delivery ; Microfluidic and Capillary Electrophoresis Applications ; Microfluidic and Bio-sensing Technologies
Authors: Aditya Nandy | Jiazhou Zhu | Jon Paul Janet | Chenru Duan | Rachel B. Getman | Heather J. Kulik
Source: ACS Catalysis (article, 2019-07-23)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network (ANN) ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of nonlocal, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty-aware evolutionary optimization using the ANN to explore a > 37 000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counterintuitive oxo formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions. Keywords: metal-oxo species; machine learning; density functional theory; spin-state-dependent reactivity; transition metal catalysis Density functional theory ; Chemistry ; Catalysis ; Metal ; Transition metal ; Ligand (biochemistry) ; Computational chemistry ; Spin states ; Oxidation state ; Chemical physics ; Inorganic chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: X. L. Lei | Andrew J. Medford
Source: Physical Review Materials (article, 2019-06-12)
Field: Materials Science — Materials Chemistry

Design and analysis of machine learning exchange-correlation functionals via rotationally invariant convolutional descriptors In this work, the authors explore a framework for developing machine-learned exchange-correlation (XC) functionals. The approach consists of two parts: a rotationally invariant model space constructed from convolutional kernels, and a neural network to predict the XC energy density. The results show that increasing the number of features in the model space leads to systematically improvable approximations to the B3LYP XC energy for 21 small molecule systems. The XC formation energy is predicted to chemical accuracy based on orbital free descriptors with a range of $&lt;$0.2 Angstrom, suggesting this approach provides an efficient route toward semi-local approximations of hybrid functionals. Invariant (physics) ; Convolutional neural network ; Materials science ; Range (aeronautics) ; Density functional theory ; Correlation ; Molecule ; Space (punctuation) ; Statistical physics ; Physics ; Artificial intelligence ; Computer science ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Rocío Ríos‐Reina | Raquel M. Callejón | Francesco Savorani | José Manuel Amigo | Marina Cocchi
Source: Talanta (article, 2019-01-30)
Field: Chemistry — Analytical Chemistry

Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars Wine ; Chemistry ; Sensor fusion ; Fusion ; Artificial intelligence ; Quality (philosophy) ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Computer science ; Food science ; Chromatography Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Horticultural and Viticultural Research
Authors: Marat Khafizov | Janne Pakarinen | Lingfeng He | David H. Hurley
Source: Journal of the American Ceramic Society (article, 2019-05-30)
Field: Materials Science — Materials Chemistry

Impact of irradiation induced dislocation loops on thermal conductivity in ceramics Abstract Experimental work aimed at understanding the role of dislocation loops in limiting phonon mediated thermal transport in ceramics is presented. Faulted dislocation loops, having diameters of a few nanometers, were introduced by irradiating a polycrystalline cerium dioxide sample with 1.6 MeV protons at 700°C. XRD analysis indicated that irradiated samples retained their crystalline structure and exhibit very little lattice expansion suggesting a low concentration of point defects. Further microstructure characterization using transmission electron microscopy revealed that interstitial type faulted dislocation loops were primarily created as expected for these irradiation conditions. Thermal conductivity of the damaged layer was measured using a modulated thermoreflectance approach. Analysis of the experimental data using the classical Klemens‐Callaway approach reveals that the conductivity reduction is primarily due to dislocation loops, while point defects and voids play only a minor role. These results provide experimental confirmation that faulted loops offer a unique arrangement for displaced atoms that leads to an unusually large reduction of thermal conductivity. Materials science ; Dislocation ; Crystallographic defect ; Thermal conductivity ; Crystallite ; Ceramic ; Irradiation ; Microstructure ; Condensed matter physics ; Transmission electron microscopy ; Conductivity ; Phonon ; Composite material ; Nanotechnology ; Chemistry ; Metallurgy ; Physics Nuclear materials and radiation effects ; Nuclear Materials and Properties ; High-pressure geophysics and materials
Authors: Yufeng Huang | Jun Kang | William A. Goddard | Lin‐Wang Wang
Source: Physical review. B./Physical review. B (article, 2019-02-06)
Field: Materials Science — Materials Chemistry

Density functional theory based neural network force fields from energy decompositions In order to develop force fields (FF) for molecular dynamics simulations that retain the accuracy of ab initio density functional theory (DFT), we developed a machine learning protocol based on an energy decomposition scheme that extracts atomic energies from DFT calculations. Our DFT to FF (DFT2FF) approach provides almost hundreds of times more data for the DFT energies, which dramatically improves accuracy with less DFT calculations. In addition, we use piecewise cosine basis functions to systematically construct symmetry invariant features into the neural network model. We illustrate this DFT2FF approach for amorphous silicon where only 800 DFT configurations are sufficient to achieve an accuracy of 1 meV/atom for energy and 0.1 eV/A for forces. We then use the resulting FF model to calculate the thermal conductivity of amorphous Si based on long molecular dynamics simulations. The dramatic speedup in training in our DFT2FF protocol allows the adoption of a simulation paradigm where an accurate and problem specific FF for a given physics phenomenon is trained on-the-spot through a quick DFT precalculation and FF training. Density functional theory ; Piecewise ; Molecular dynamics ; Ab initio ; Artificial neural network ; Computer science ; Statistical physics ; Physics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Chemistry ; Artificial intelligence ; Mathematical analysis Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Electron Microscopy Techniques and Applications