jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jerzy Stangierski | Dorota Weiss | Anna Kaczmarek
Source: European Food Research and Technology (article, 2019-09-27)
Field: Chemistry — Analytical Chemistry

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese Abstract The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R 2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R 2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C. Artificial neural network ; Principal component analysis ; Linear regression ; Statistics ; Mathematics ; Regression ; Coefficient of determination ; Regression analysis ; Predictive modelling ; Data set ; Test set ; Set (abstract data type) ; Mean squared error ; Correlation coefficient ; Principal component regression ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Computer science Spectroscopy and Chemometric Analyses ; Probiotics and Fermented Foods ; Meat and Animal Product Quality
Authors: Hongtao Xu | Fei Ma | Nan Wang | Wei Hou | Huan Xiong | Fengping Lu | Jie Li | Shuyue Wang | Peixiang Ma | Guang Yang | Richard A. Lerner
Source: Advanced Science (article, 2019-09-30)
Field: Chemistry — Organic Chemistry

DNA‐Encoded Libraries: Aryl Fluorosulfonates as Versatile Electrophiles Enabling Facile On‐DNA Suzuki, Sonogashira, and Buchwald Reactions Using (hetero)aryl fluorosulfonates as versatile electrophiles, facile on-DNA cross-coupling reactions of Suzuki, Sonogashira, and Buchwald are reported here. Notably, all of these reactions show excellent functional group tolerance, mild reaction conditions (relative low temperature and open to air), rich heterocyclic coupling partners, and more importantly, DNA-compatibility. Thus, these new reactions based on efficient formation of C(sp<sup>2</sup>)-C(sp<sup>2</sup>), C(sp<sup>2</sup>)-C(sp), and C(sp<sup>2</sup>)-N bonds are highly amenable to synthesis of DNA-encoded libraries with great molecular diversity. Sonogashira coupling ; Aryl ; Electrophile ; Suzuki reaction ; Combinatorial chemistry ; Chemistry ; DNA ; Functional group ; Palladium ; Catalysis ; Organic chemistry ; Biochemistry Catalytic Cross-Coupling Reactions ; Chemical Synthesis and Analysis ; Carbohydrate Chemistry and Synthesis
Authors: Zihang Qiu | Leiyang Lv | Jianbin Li | Chenchen Li | Chen-Chen Li | Chao-Jun Li
Source: Chemical Science (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Direct conversion of phenols into primary anilines with hydrazine catalyzed by palladium Primary anilines are essential building blocks to synthesize various pharmaceuticals, agrochemicals, pigments, electronic materials, and others. To date, the syntheses of primary anilines mostly rely on the reduction of nitroarenes or the transition-metal-catalyzed Ullmann, Buchwald-Hartwig and Chan-Lam cross-coupling reactions with ammonia, in which non-renewable petroleum-based chemicals are typically used as feedstocks <i>via</i> multiple step syntheses. A long-standing scientific challenge is to synthesize various primary anilines directly from renewable sources. Herein, we report a general method to directly convert a broad range of phenols into the corresponding primary anilines with the cheap and widely available hydrazine as both amine and hydride sources with simple Pd/C as the catalyst. Hydrazine (antidepressant) ; Primary (astronomy) ; Palladium ; Catalysis ; Phenols ; Chemistry ; Organic chemistry ; Combinatorial chemistry ; Chromatography Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions
Authors: Anders S. Christensen | Felix A. Faber | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2019-02-12)
Field: Materials Science — Materials Chemistry

Operators in quantum machine learning: Response properties in chemical space The role of response operators is well established in quantum mechanics. We investigate their use for universal quantum machine learning models of response properties in molecules. After introducing a theoretical basis, we present and discuss numerical evidence based on measuring the potential energy's response with respect to atomic displacement and to electric fields. Prediction errors for corresponding properties, atomic forces, and dipole moments improve in a systematic fashion with training set size and reach high accuracy for small training sets. Prediction of normal modes and infrared-spectra of some small molecules demonstrates the usefulness of this approach for chemistry. Quantum ; Set (abstract data type) ; Displacement (psychology) ; Dipole ; Space (punctuation) ; Training set ; Quantum chemical ; Quantum process Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Physical and Chemical Molecular Interactions
Authors: Lixue Cheng | Matthew Welborn | Anders S. Christensen | Thomas F. Miller
Source: The Journal of Chemical Physics (article, 2019-04-04)
Field: Materials Science — Materials Chemistry

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules We address the degree to which machine learning (ML) can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based machine learning (MOB-ML) method is applied to several test systems. Strikingly, for the second-order Møller-Plessett perturbation theory, coupled cluster with singles and doubles (CCSD), and CCSD with perturbative triples levels of theory, it is shown that the thermally accessible (350 K) potential energy surface for a single water molecule can be described to within 1 mhartree using a model that is trained from only a single reference calculation at a randomized geometry. To explore the breadth of chemical diversity that can be described, MOB-ML is also applied to a new dataset of thermalized (350 K) geometries of 7211 organic models with up to seven heavy atoms. In comparison with the previously reported Δ-ML method, MOB-ML is shown to reach chemical accuracy with threefold fewer training geometries. Finally, a transferability test in which models trained for seven-heavy-atom systems are used to predict energies for thirteen-heavy-atom systems reveals that MOB-ML reaches chemical accuracy with 36-fold fewer training calculations than Δ-ML (140 vs 5000 training calculations). Transferability ; Organic molecules ; Training set ; Correlation ; Cluster (spacecraft) ; Perturbation (astronomy) ; Molecule ; Feature selection Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Andrea Martini | С. А. Гуда | Alexander A. Guda | Grigory Smolentsev | Alexander Algasov | Oleg Usoltsev | Mikhail A. Soldatov | Aram L. Bugaev | Yury V. Rusalev | Carlo Lamberti | А. В. Солдатов
Source: Computer Physics Communications (article, 2019-11-23)
Field: Materials Science — Materials Chemistry

PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms XANES ; Algorithm ; Maxima and minima ; Interpolation (computer graphics) ; Computer science ; Software ; Spectral line ; Artificial neural network ; Machine learning ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Philipp Schlee | Servann Hérou | Rhodri Jervis | Paul R. Shearing | Dan J. L. Brett | D. A. Baker | Omid Hosseinaei | Per Tomani | M. Mangir Murshed | Yaomin Li | María José Mostazo‐López | Diego Cazorla‐Amorós | Ana Jorge Sobrido | Maria‐Magdalena Titirici
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Free-standing supercapacitors from Kraft lignin nanofibers with remarkable volumetric energy density We have discovered a very simple method to address the challenge associated with the low volumetric energy density of free-standing carbon nanofiber electrodes for supercapacitors by electrospinning Kraft lignin in the presence of an oxidizing salt (NaNO<sub>3</sub>) and subsequent carbonization in a reducing atmosphere. The presence of the oxidative salt decreases the diameter of the resulting carbon nanofibers doubling their packing density from 0.51 to 1.03 mg cm<sup>-2</sup> and hence doubling the volumetric energy density. At the same time, the oxidative NaNO<sub>3</sub> salt eletrospun and carbonized together with lignin dissolved in NaOH acts as a template to increase the microporosity, thus contributing to a good gravimetric energy density. By simply adjusting the process parameters (amount of oxidizing/reducing agent), the gravimetric and volumetric energy density of the resulting lignin free-standing carbon nanofiber electrodes can be carefully tailored to fit specific power to energy demands. The areal capacitance increased from 147 mF cm<sup>-2</sup> in the absence of NaNO<sub>3</sub> to 350 mF cm<sup>-2</sup> with NaNO<sub>3</sub> translating into a volumetric energy density increase from 949 μW h cm<sup>-3</sup> without NaNO<sub>3</sub> to 2245 μW h cm<sup>-3</sup> with NaNO<sub>3</sub>. Meanwhile, the gravimetric capacitance also increased from 151 F g<sup>-1</sup> without to 192 F g<sup>-1</sup> with NaNO<sub>3</sub>. Supercapacitor ; Lignin ; Kraft paper ; Nanofiber ; Energy density ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Electrochemistry ; Physics ; Electrode ; Engineering physics ; Engineering Supercapacitor Materials and Fabrication ; Lignin and Wood Chemistry ; Electrospun Nanofibers in Biomedical Applications
Authors: Osman Altay | Turan Gürgenç | Mustafa Ulaş | Cihan Özel
Source: Friction (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms In this study, experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with (wt.%) 50FeCrC-20FeW-30FeB and 70FeCrC-30FeB powder mixtures by plasma transfer arc welding were determined. The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory. The linear regression (LR), support vector machine (SVM), and Gaussian process regression (GPR) algorithms are used for predicting wear quantities. A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms. Support vector machine ; Kriging ; Coating ; Alloy ; Algorithm ; Materials science ; Process (computing) ; Gaussian process ; Welding ; Linear regression ; Metallurgy ; Gaussian ; Computer science ; Composite material ; Machine learning ; Physics Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Lubricants and Their Additives
Authors: Luca Münzfeld | Christoph Schoo | Sebastian Bestgen | Eufemio Moreno Pineda | Ralf Köppe | Mario Ruben | Peter W. Roesky
Source: Nature Communications (article, 2019-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis, structures and magnetic properties of [(η9-C9H9)Ln(η8-C8H8)] super sandwich complexes Sandwich complexes are an indispensable part of organometallic chemistry, which is becoming increasingly important in the field of lanthanide-based single molecule magnets. Herein, a fundamental class of pure sandwich complexes, [(η<sup>9</sup>-C<sub>9</sub>H<sub>9</sub>)Ln(η<sup>8</sup>-C<sub>8</sub>H<sub>8</sub>)] (Ln=Nd, Sm, Dy, Er), is reported. These neutral and sandwiched lanthanide compounds exclusively contain fully π-coordinated coplanar eight and nine membered CH rings. The magnetic properties of these compounds are investigated, leading to the observation of slow relaxation of the magnetization, including open hysteresis loops up to 10 K for the Er(III) analogue. Fast relaxation of the magnetization is likewise observed near zero field, a highly important characteristic for quantum information processing schemes. Our synthetic strategy is straightforward and utilizes the reaction of [(η<sup>8</sup>-C<sub>8</sub>H<sub>8</sub>)LnI(thf)<sub>n</sub>] complexes with [K(C<sub>9</sub>H<sub>9</sub>)]. Although all compounds are fully characterized, structural details of the title compounds can also be deduced by Raman spectroscopy only. Lanthanide ; Magnetization ; Relaxation (psychology) ; Raman spectroscopy ; Single-molecule magnet ; Molecule ; Crystallography ; Chemistry ; Hysteresis ; Magnetic relaxation ; Materials science ; Magnetic hysteresis ; Nuclear magnetic resonance spectroscopy ; Nanotechnology ; Magnetic field ; Stereochemistry ; Condensed matter physics ; Physics ; Ion ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Advanced NMR Techniques and Applications
Authors: Yang Yu | Theodore Tyrikos‐Ergas | Yuntao Zhu | Giulio Fittolani | Vittorio Bordoni | Ankush Singhal | Richard J. Fair | Andrea Grafmüller | Peter H. Seeberger | Martina Delbianco
Source: Angewandte Chemie International Edition (review, 2019-07-30)
Field: Materials Science — Biomaterials

Systematic Hydrogen‐Bond Manipulations To Establish Polysaccharide Structure–Property Correlations A dense hydrogen-bond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structure-function correlations. We have prepared well-defined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogen-bond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structure-property correlations will guide the synthesis of novel, tailor-made carbohydrate materials. Hydrogen bond ; Crystallinity ; Chemistry ; Polysaccharide ; Solubility ; Cellulose ; Derivatization ; Computational chemistry ; Molecule ; Crystallography ; Organic chemistry Advanced Cellulose Research Studies ; Polysaccharides Composition and Applications ; biodegradable polymer synthesis and properties
Authors: Gregory H. Teichert | Anirudh Raju Natarajan | Anton Van der Ven | Krishna Garikipati
Source: Computer Methods in Applied Mechanics and Engineering (article, 2019-05-21)
Field: Materials Science — Materials Chemistry

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions Bridging (networking) ; Integrable system ; Artificial neural network ; Fidelity ; Representation (politics) ; Energy (signal processing) ; Binary number ; Experimental data ; Artificial intelligence ; Computer science ; Statistical physics ; Algorithm ; Physics ; Mathematics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Min‐Hsuan Lee
Source: Advanced Energy Materials (article, 2019-06-03)
Field: Materials Science — Materials Chemistry

Insights from Machine Learning Techniques for Predicting the Efficiency of Fullerene Derivatives‐Based Ternary Organic Solar Cells at Ternary Blend Design Abstract Ternary organic solar cells (OSCs) have progressed significantly in recent years due to the sufficient photon harvesting of the blend photoactive layer including three absorption‐complementary materials. With the rapid development of highly efficient ternary OSCs in photovoltaics, the precise energy‐level alignment of the three active components within ternary OSC devices should be taken into account. The machine‐learning technique is a computational method that can effectively learn from previous historical data to build predictive models. In this study, a dataset of 124 fullerene derivatives‐based ternary OSCs is manually constructed from a diverse range of literature along with their frontier molecular orbital theory levels, and device structures. Different machine‐learning algorithms are trained based on these electronic parameters to predict photovoltaic efficiency. Thus, the best predictive capability is provided by using the Random Forest approach beyond other machine‐learning algorithms in the dataset. Furthermore, the Random Forest algorithm yields valuable insights into the crucial role of lowest unoccupied molecular orbital energy levels of organic donors in the performance of ternary OSCs. The outcome of this study demonstrates a smart strategy for extracting underlying complex correlations in fullerene derivatives‐based ternary OSCs, thereby accelerating the development of ternary OSCs and related research fields. Ternary operation ; Organic solar cell ; Fullerene ; Materials science ; Photovoltaic system ; Random forest ; Photovoltaics ; Artificial intelligence ; Machine learning ; Binary number ; Computer science ; Topology (electrical circuits) ; Physics ; Mathematics ; Electrical engineering ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advancements in Semiconductor Devices and Circuit Design
Authors: Fatima Nasser | Julia K. Constantinou | Iseult Lynch
Source: PROTEOMICS (review, 2019-11-21)
Field: Materials Science — Materials Chemistry

Nanomaterials in the Environment Acquire an “Eco‐Corona” Impacting their Toxicity to <i>Daphnia Magna</i>—a Call for Updating Toxicity Testing Policies Nanomaterials (NMs) are particles with at least one dimension between 1 and 100 nm and a large surface area to volume ratio, providing them with exceptional qualities that are exploited in a variety of industrial fields. Deposition of NMs into environmental waters during or after use leads to the adsorption of an ecological (eco-) corona, whereby a layer of natural biomolecules coats the NM changing its stability, identity and ultimately toxicity. The eco-corona is not currently incorporated into ecotoxicity tests, although it has been shown to alter the interactions of NMs with organisms such as Daphnia magna (D. magna). Here, the literature on environmental biomolecule interactions with NMs is synthesized and a framework for understanding the eco-corona composition and its role in modulating NMs ecotoxicity is presented, utilizing D. magna as a model. The importance of including biomolecules as part of the current international efforts to update the standard testing protocols for NMs, is highlighted. Facilitating the formation of an eco-corona prior to NMs ecotoxicity testing will ensure that signaling pathways perturbed by the NMs are real rather than being associated with the damage arising from reactive NM surfaces "acquiring" a corona by pulling biomolecules from the organism's surface. Daphnia magna ; Ecotoxicity ; Environmental chemistry ; Organism ; Biomolecule ; Nanotechnology ; Toxicity ; Chemistry ; Biochemical engineering ; Environmental science ; Materials science ; Biology ; Engineering Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Membrane Separation Technologies
Authors: Siripak Songsaeng | Patchanita Thamyongkit | Sirilux Poompradub
Source: Journal of Advanced Research (article, 2019-05-31)
Field: Materials Science — Surfaces, Coatings and Films

Natural rubber/reduced-graphene oxide composite materials: Morphological and oil adsorption properties for treatment of oil spills A green sorbent material was fabricated through the simple addition of reduced graphene oxide (rGO) to natural rubber (NR) latex. The effect of rGO content in the NR foam on petroleum oil adsorption was investigated. The addition of rGO in NR increased the petroleum oil adsorption capacity of the resulting NR/rGO (NRG) composite foam (12-21 g g<sup>-1</sup>) with respect to those of the pure NR foam (8-15 g g<sup>-1</sup>) and a commercial sorbent (6-7 g g<sup>-1</sup>). The adsorption capacity was optimal for 0.5 phr rGO (NRG-0.5). Further, the environmental conditions (temperature and waves) affected the oil adsorption capacity of the sorbent materials. The adsorption kinetics of the sorbent materials for crude AXL oil was best described with pseudo-second-order kinetics. The interparticle diffusion model revealed three steps whereas the adsorption isotherms approximated the Langmuir isotherms. Moreover, the oil adsorption mechanisms of the NR and NRG sorbent materials were compared to that of a commercial sorbent. The high elasticity of the NRG-0.5 composite foam improved not only the oil adsorption capacity but also the reusability of the sorbent material. The presence of rGO increased the strength of the NRG-0.5 compared to that of pure NR, which resulted in a high-performance and reusable material with an oil removal efficiency higher than 70% after 30 uses. Sorbent ; Adsorption ; Chemical engineering ; Graphene ; Materials science ; Natural rubber ; Oxide ; Composite number ; Langmuir adsorption model ; Composite material ; Chemistry ; Organic chemistry ; Nanotechnology ; Metallurgy Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics ; Graphene research and applications