jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ludwig Holleis | B. S. Shivaram | Prasanna V. Balachandran
Source: Applied Physics Letters (article, 2019-06-03)
Field: Materials Science — Materials Chemistry

Machine learning guided design of single-molecule magnets for magnetocaloric applications We present a data-driven approach to predict entropy changes (ΔS) in small magnetic fields in single-molecule magnets (SMMs) relevant to their application as magnetocaloric refrigerants. We construct a database of SMMs with a representation scheme incorporating aspects related to dimensionality, structure, local coordination environment, ideal total spin of magnetic ions, ligand type, and linking chemistry. We train machine learning models for predicting the entropy change as a function of structure and chemistry and use the models to arrive at ΔS for hypothetical molecules. We also identify key descriptors that affect the entropy change, thus providing insights into designing tailored SMMs with improved magnetocaloric properties. Magnetic refrigeration ; Curse of dimensionality ; Magnet ; Entropy (arrow of time) ; Molecule ; Refrigerant ; Computer science ; Materials science ; Chemistry ; Thermodynamics ; Artificial intelligence ; Physics ; Magnetic field ; Mechanical engineering ; Engineering ; Magnetization ; Quantum mechanics Machine Learning in Materials Science ; Magnetism in coordination complexes ; Theoretical and Computational Physics
Authors: Chengqiang Lu | Qi Liu | Chao Wang | Zhenya Huang | Peize Lin | Lixin He
Source: arXiv (Cornell University) (preprint, 2019-06-25)
Field: Materials Science — Materials Chemistry

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction. Computer science ; Graph ; Theoretical computer science ; Quantum ; Property (philosophy) ; Perspective (graphical) ; Graph theory ; Molecule ; Quantum chemistry ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Akshay Iyer | Biswadip Dey | Arindam Dasgupta | Wei Chen | Amit K. Chakraborty
Source: arXiv (Cornell University) (preprint, 2019-10-04)
Field: Materials Science — Materials Chemistry

A Conditional Generative Model for Predicting Material Microstructures from Processing Methods Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications. Thus a critical task in material design is establishing the processing-structure relationship, which requires domain expertise and techniques that can model the high-dimensional material microstructure. This work proposes a deep learning based approach that models the processing-structure relationship as a conditional image synthesis problem. In particular, we develop an auxiliary classifier Wasserstein GAN with gradient penalty (ACWGAN-GP) to synthesize microstructures under a given processing condition. This approach is free of feature engineering, requires modest domain knowledge and is applicable to a wide range of material systems. We demonstrate this approach using the ultra high carbon steel (UHCS) database, where each microstructure is annotated with a label describing the cooling method it was subjected to. Our results show that ACWGAN-GP can synthesize high-quality multiphase microstructures for a given cooling method. Microstructure ; Computer science ; Feature (linguistics) ; Domain (mathematical analysis) ; Image processing ; Artificial intelligence ; Materials science ; Image (mathematics) ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Injection Molding Process and Properties
Authors: Rosa Vera | Raquel Araya | Carolina Garín | Sebastián Ossandón | Paula Rojas
Source: Materials and Corrosion (article, 2019-01-28)
Field: Materials Science — Materials Chemistry

Study on the effect of atmospheric corrosion on mechanical properties with impact test: Carbon steel and Galvanized steel The present work presents the behavior of carbon steel and galvanized steel against atmospheric corrosion after 3 years of exposure at seven locations around the region of Valparaiso, Chile. Results show a relation between corrosion rates and environmental and meteorological conditions, categorized as CX for the Quintero zone, and C3 and C2 in the remaining six zones. Corrosion rate behaviors and material toughness losses were modeled using power functions and neural networks, found to be a function of environmental exposure time. Losses were greater for carbon steel in coastal and industrial environments, reaching 70 to 80%. This effect was reduced in galvanized steel, not exceeding 15% over the same period of exposure. The relationship between corrosion rate and loss of toughness of both materials was modeled using neural networks. Galvanization ; Corrosion ; Carbon steel ; Materials science ; Metallurgy ; Toughness ; Environmental science ; Composite material ; Layer (electronics) Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Vahid Attari | Pejman Honarmandi | Thien Duong | Daniel Sauceda | Douglas Allaire | Raymundo Arróyave
Source: Acta Materialia (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model CALPHAD ; Monte Carlo method ; Propagation of uncertainty ; Field (mathematics) ; Inference ; Uncertainty quantification ; Phase (matter) ; Computer science ; Uncertainty analysis ; Markov chain Monte Carlo ; Statistical physics ; Multiscale modeling ; Materials science ; Machine learning ; Simulation ; Artificial intelligence ; Algorithm ; Phase diagram ; Physics ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High Temperature Alloys and Creep
Authors: Victor Naden Robinson | Hongxiang Zong | Graeme J. Ackland | Gavin Woolman | Andreas Hermann
Source: Proceedings of the National Academy of Sciences (article, 2019-04-11)
Field: Materials Science — Materials Chemistry

On the chain-melted phase of matter Various single elements form incommensurate crystal structures under pressure, where a zeolite-type "host" sublattice surrounds a "guest" sublattice comprising 1D chains of atoms. On "chain melting," diffraction peaks from the guest sublattice vanish, while those from the host remain. Diffusion of the guest atoms is expected to be confined to the channels in the host sublattice, which suggests 1D melting. Here, we present atomistic simulations of potassium to investigate this phenomenon and demonstrate that the chain-melted phase has no long-ranged order either along or between the chains. This 3D disorder provides the extensive entropy necessary to make the chain melt a true thermodynamic phase of matter, yet with the unique property that diffusion remains confined to 1D only. Calculations necessitated the development of an interatomic forcefield using machine learning, which we show fully reproduces potassium's phase diagram, including the chain-melted state and 14 known phase transitions. Phase diagram ; Chain (unit) ; Phase transition ; Chemical physics ; Phase (matter) ; Materials science ; Diffusion ; Crystallography ; Condensed matter physics ; Thermodynamics ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: M. Arshadi | Mahmood Karimi Abdolmaleki | Hamed Eskandarloo | Alireza Abbaspourrad
Source: Journal of Colloid and Interface Science (article, 2019-01-14)
Field: Materials Science — Polymers and Plastics

A supported dendrimer with terminal symmetric primary amine sites for adsorption of salicylic acid Dendrimer ; Amine gas treating ; Terminal (telecommunication) ; Primary (astronomy) ; Adsorption ; Chemistry ; Salicylic acid ; Polymer chemistry ; Organic chemistry ; Chemical engineering ; Stereochemistry ; Combinatorial chemistry ; Biochemistry ; Computer science ; Physics Dendrimers and Hyperbranched Polymers ; Graphene research and applications ; Covalent Organic Framework Applications
Authors: Aled D. Roberts | William Finnigan | Emmanuel Wolde-Michael | Paul P. Kelly | Jonny J. Blaker | Sam Hay | Rainer Breitling | Eriko Takano | Nigel S. Scrutton
Source: MRS Communications (article, 2019-04-24)
Field: Materials Science — Biomaterials

Synthetic biology for fibers, adhesives, and active camouflage materials in protection and aerospace Synthetic biology has huge potential to produce the next generation of advanced materials by accessing previously unreachable (bio)chemical space. In this prospective review, we take a snapshot of current activity in this rapidly developing area, focussing on prominent examples for high-performance applications such as those required for protective materials and the aerospace sector. The continued growth of this emerging field will be facilitated by the convergence of expertise from a range of diverse disciplines, including molecular biology, polymer chemistry, materials science and process engineering. This review highlights the most significant recent advances and address the cross-disciplinary challenges currently being faced. Aerospace ; Nanotechnology ; Materials science ; Synthetic biology ; Engineering ethics ; Biochemical engineering ; Systems engineering ; Biology ; Aerospace engineering ; Engineering ; Computational biology Silk-based biomaterials and applications ; Surface Modification and Superhydrophobicity ; Click Chemistry and Applications
Authors: Wérickson Fortunato de Carvalho Rocha | David A. Sheen
Source: Fuel (article, 2019-02-02)
Field: Chemistry — Analytical Chemistry

Determination of physicochemical properties of petroleum derivatives and biodiesel using GC/MS and chemometric methods with uncertainty estimation Partial least squares regression ; Support vector machine ; Chemometrics ; Robustness (evolution) ; Predictive modelling ; Gasoline ; Bootstrapping (finance) ; Biodiesel ; Computer science ; Biological system ; Chemistry ; Mathematics ; Machine learning ; Econometrics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Maxim Ziatdinov | Christopher T. Nelson | Rama K. Vasudevan | Deyang Chen | Sergei V. Kalinin
Source: Applied Physics Letters (article, 2019-07-29)
Field: Materials Science — Materials Chemistry

Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions Recent advances in scanning transmission electron microscopy (STEM) have enabled direct visualization of the atomic structure of ferroic materials, enabling the determination of atomic column positions with approximately picometer precision. This, in turn, enabled direct mapping of ferroelectric and ferroelastic order parameter fields via the top-down approach, where the atomic coordinates are directly mapped on the mesoscopic order parameters. Here, we explore the alternative bottom-up approach, where the atomic coordinates derived from the STEM image are used to explore the extant atomic displacement patterns in the material and build the collection of the building blocks for the distorted lattice. This approach is illustrated for the La-doped BiFeO3 system. Ferroelectricity ; Atomic units ; Scanning transmission electron microscopy ; Mesoscopic physics ; Piezoresponse force microscopy ; Materials science ; Atomic force microscopy ; Condensed matter physics ; Optics ; Nanotechnology ; Transmission electron microscopy ; Physics ; Optoelectronics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Qinyu Wang | Gennaro Senatore | K.M.B. Jansen | Arjan Habraken | Patrick Teuffel
Source: Materials & Design (article, 2019-11-18)
Field: Materials Science — Polymers and Plastics

Design and characterization of variable stiffness structural joints Design and characterization of variable stiffness structural joints to control the dynamic response of frame structures. Materials science ; Stiffness ; Viscoelasticity ; Structural engineering ; Joint (building) ; Transient response ; Joint stiffness ; Modal analysis ; Characterization (materials science) ; Composite material ; Finite element method ; Engineering Polymer composites and self-healing ; Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis
Authors: Magale Karine Diel Rambo | Márcia M. C. Ferreira | Polyana Morais de Melo | Cláudio Carneiro Santana | Daniel Assumpção Bertuol | Michele Cristiane Diel Rambo
Source: Food Science and Technology (article, 2019-10-29)
Field: Chemistry — Analytical Chemistry

Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis The real-time prediction in biorefinery industries has become essential. Models using partial least square regression (PLS) were developed to predict moisture, ash, volatile matter, fixed carbon and organic matter of coconut and coffee residues. In this study, 49 samples were collected and near infrared spectroscopy were applied to predict moisture, ash, volatile matter, fixed carbon and organic matter. For external validation 25% of the set samples were used. Moisture and volatile matter were predicted with coefficients of determination (R2cal) above 0.90, and standard errors (RSD) of the estimate of 14.4% and 2.26%, respectively. Models of ash and organic matter show R2cal > 0.77 and RSD values < 20.4%. For the external validation, the low deviations show the approximation between reference and predicted values and good prediction with R2pred > 0.70. All calibration models were acceptable for sample screening. This study demonstrates that PLS can be used to predict biomass composition of different species, with very low costs and time. Proximate ; Quality (philosophy) ; Spectroscopy ; Food quality ; Environmental science ; Food science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Phytochemicals and Antioxidant Activities
Authors: Editha Giese | Sascha Rohn | Jan Fritsche
Source: Analytical and Bioanalytical Chemistry (article, 2019-08-10)
Field: Chemistry — Analytical Chemistry

Chemometric tools for the authentication of cod liver oil based on nuclear magnetic resonance and infrared spectroscopy data Cod liver oil is a popular dietary supplement marketed as a rich source of omega-3 fatty acids as well as vitamins A and D. Due to its high market price, cod liver oil is vulnerable to adulteration with lower priced vegetable oils. In this study, <sup>1</sup>H and <sup>13</sup>C nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, and gas chromatography (coupled to a flame ionization detector) were used in combination with multivariate statistics to determine cod liver oil adulteration with common vegetable oils (sunflower and canola oils). Artificial neural networks (ANN) were able to differentiate adulteration levels based on infrared spectra with a detection limit of 0.22% and a root mean square error of prediction (RMSEP) of 0.86%. ANN models using <sup>1</sup>H NMR and <sup>13</sup>C NMR data yielded detection limits of 3.0% and 1.8% and RMSEPs of 2.7% and 1.1%, respectively. In comparison, the ANN model based on fatty acid profiles determined by gas chromatography achieved a detection limit of 0.81% and an RMSEP of 1.1%. The approach of using spectroscopic techniques in combination with multivariate statistics can be regarded as a promising tool for the authentication of cod liver oil and may pave the way for a holistic quality assessment of fish oils. Graphical abstract. Detection limit ; Chemistry ; Gas chromatography ; Chromatography ; Multivariate statistics ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Mathematics ; Statistics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Silvia Pittolo | Hyojung Lee | Anna Lladó | Sébastien Tosi | Miquel Bosch | Lídia Bardia | Xavier Gómez‐Santacana | Amadeu Llebaria | Eduardo Soriano | Julien Colombelli | Kira E. Poskanzer | Gertrudis Perea | Pau Gorostiza
Source: Proceedings of the National Academy of Sciences (article, 2019-06-13)
Field: Materials Science — Materials Chemistry

Reversible silencing of endogenous receptors in intact brain tissue using 2-photon pharmacology The physiological activity of proteins is often studied with loss-of-function genetic approaches, but the corresponding phenotypes develop slowly and can be confounding. Photopharmacology allows direct, fast, and reversible control of endogenous protein activity, with spatiotemporal resolution set by the illumination method. Here, we combine a photoswitchable allosteric modulator (alloswitch) and 2-photon excitation using pulsed near-infrared lasers to reversibly silence metabotropic glutamate 5 (mGlu<sub>5</sub>) receptor activity in intact brain tissue. Endogenous receptors can be photoactivated in neurons and astrocytes with pharmacological selectivity and with an axial resolution between 5 and 10 µm. Thus, 2-photon pharmacology using alloswitch allows investigating mGlu<sub>5</sub>-dependent processes in wild-type animals, including synaptic formation and plasticity, and signaling pathways from intracellular organelles. Endogeny ; Allosteric regulation ; Glutamate receptor ; Allosteric modulator ; Receptor ; Neuroscience ; Intracellular ; Metabotropic receptor ; Cell biology ; Biology ; Metabotropic glutamate receptor ; G protein-coupled receptor ; Chemistry ; Biophysics ; Signal transduction ; Biochemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Receptor Mechanisms and Signaling
Authors: Fei Liu | Wei Wang | Tingting Shen | Jiyu Peng | Wenwen Kong
Source: Sensors (article, 2019-03-25)
Field: Chemistry — Analytical Chemistry

Rapid Identification of Kudzu Powder of Different Origins Using Laser-Induced Breakdown Spectroscopy The rapid identification of kudzu powder of different origins is of great significance for studying the authenticity identification of Chinese medicine. The feasibility of rapidly identifying kudzu powder origin was investigated based on laser-induced breakdown spectroscopy (LIBS) technology combined with chemometrics methods. The discriminant models based on the full spectrum include extreme learning machine (ELM), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN) and random forest (RF), and the accuracy of models was more than 99.00%. The prediction results of KNN and RF models were best: the accuracy of calibration and prediction sets of kudzu powder from different producing areas both reached 100%. The characteristic wavelengths were selected using principal component analysis (PCA) loadings. The accuracy of calibration set and the prediction set of discrimination models, based on characteristic wavelengths, is all higher than 98.00%. Random forest and KNN have the same excellent identification results, and the accuracy of calibration and prediction sets of kudzu powder from different producing areas reached 100%. Compared with the full spectrum discriminant analysis model, the discriminant analysis model based on the characteristic wavelength had almost the same discriminant effects, and the input variables were reduced by 99.92%. The results of this research show that the characteristic wavelength can be used instead of the LIBS full spectrum to quickly identify kudzu powder from different producing areas, which had the advantages of reducing input, simplifying the model, increasing the speed and improving the model effect. Therefore, LIBS technology is an effective method for rapid identification of kudzu powder from different habitats. This study provides a basis for LIBS to be applied in the genuineness and authenticity identification of Chinese medicine. Kudzu ; Laser-induced breakdown spectroscopy ; Linear discriminant analysis ; Chemometrics ; Principal component analysis ; Random forest ; Calibration ; Artificial intelligence ; Pattern recognition (psychology) ; Identification (biology) ; Biological system ; Computer science ; Materials science ; Mathematics ; Machine learning ; Statistics ; Laser ; Optics ; Physics ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Trinh Tran | Çağrı Temuçin Ünal | Daniel Severín | László Záborszky | Horacio G. Rotstein | Alfredo Kirkwood | Jorge Golowasch
Source: Scientific Reports (article, 2019-02-08)
Field: Chemistry — Electrochemistry

Ionic current correlations are ubiquitous across phyla Ionic currents, whether measured as conductance amplitude or as ion channel transcript numbers, can vary many-fold within a population of identified neurons. In invertebrate neuronal types multiple currents can be seen to vary while at the same time their magnitudes are correlated. These conductance amplitude correlations are thought to reflect a tight homeostasis of cellular excitability that enhances the robustness and stability of neuronal activity over long stretches of time. Although such ionic conductance correlations are well documented in invertebrates, they have not been reported in vertebrates. Here we demonstrate with two examples, identified mouse hippocampal granule cells (GCs) and cholinergic basal forebrain neurons, that the correlation of ionic conductance amplitudes between different ionic currents also exists in vertebrates, and we argue that it is a ubiquitous phenomenon expressed by many species across phyla. We further demonstrate that in dentate gyrus GCs these conductance correlations are likely regulated in a circadian manner. This is reminiscent of the known conductance regulation by neuromodulators in crustaceans. However, in GCs we observe a more nuanced regulation, where for some conductance pairs the correlations are completely eliminated while for others the correlation is quantitatively modified but not obliterated. Phylum ; Current (fluid) ; Ionic bonding ; Computer science ; Data science ; Biology ; Chemistry ; Ion ; Geology ; Oceanography ; Gene ; Genetics Electrochemical Analysis and Applications ; Plant and Biological Electrophysiology Studies ; Neuroscience and Neural Engineering