jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sunghwan Choi | Yeonjoon Kim | Jin Woo Kim | Zeehyo Kim | Woo Youn Kim
Source: Chemistry - A European Journal (article, 2018-02-23)
Field: Materials Science — Materials Chemistry

Feasibility of Activation Energy Prediction of Gas‐Phase Reactions by Machine Learning Machine learning based on big data has emerged as a powerful solution in various chemical problems. We investigated the feasibility of machine learning models for the prediction of activation energies of gas-phase reactions. Six different models with three different types, including the artificial neural network, the support vector regression, and the tree boosting methods, were tested. We used the structural and thermodynamic properties of molecules and their differences as input features without resorting to specific reaction types so as to maintain the most general input form for broad applicability. The tree boosting method showed the best performance among others in terms of the coefficient of determination, mean absolute error, and root mean square error, the values of which were 0.89, 1.95, and 4.49 kcal mol<sup>-1</sup> , respectively. Computation time for the prediction of activation energies for 2541 test reactions was about one second on a single computing node without using accelerators. Boosting (machine learning) ; Artificial neural network ; Support vector machine ; Computation ; Mean squared error ; Computer science ; Gradient boosting ; Regression ; Artificial intelligence ; Machine learning ; Mean absolute error ; Mathematics ; Algorithm ; Statistics ; Random forest Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Ankit Jain | Thomas Bligaard
Source: Physical review. B./Physical review. B (article, 2018-12-20)
Field: Materials Science — Materials Chemistry

Atomic-position independent descriptor for machine learning of material properties The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not available a priori for new materials, which severely limits exploration of novel materials. We overcome this limitation by using only crystallographic symmetry information in the structural description of materials. We show that for materials with identical structural symmetry, machine learning is trivial, and accuracies similar to that of density functional theory calculations can be achieved by using only atomic numbers in the material description. For machine learning of formation energies of bulk crystalline solids, this simple material descriptor is able to achieve prediction mean absolute errors of only 0.07 eV/at on a test dataset consisting of more than 85 000 diverse materials. This atomic-position independent material descriptor presents a new route of materials discovery wherein millions of materials can be screened by training a machine learning model over a drastically reduced subspace of materials. Position (finance) ; Subspace topology ; Atom (system on chip) ; Computer science ; Artificial intelligence ; A priori and a posteriori ; Symmetry (geometry) ; Machine learning ; Density functional theory ; Algorithm ; Mathematics ; Chemistry ; Computational chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hao Wang | Weitao Yang
Source: The Journal of Physical Chemistry Letters (article, 2018-05-18)
Field: Materials Science — Materials Chemistry

Force Field for Water Based on Neural Network We developed a novel neural network-based force field for water based on training with high-level ab initio theory. The force field was built based on an electrostatically embedded many-body expansion method truncated at binary interactions. The many-body expansion method is a common strategy to partition the total Hamiltonian of large systems into a hierarchy of few-body terms. Neural networks were trained to represent electrostatically embedded one-body and two-body interactions, which require as input only one and two water molecule calculations at the level of ab initio electronic structure method CCSD/aug-cc-pVDZ embedded in the molecular mechanics water environment, making it efficient as a general force field construction approach. Structural and dynamic properties of liquid water calculated with our force field show good agreement with experimental results. We constructed two sets of neural network based force fields: nonpolarizable and polarizable force fields. Simulation results show that the nonpolarizable force field using fixed TIP3P charges has already behaved well, since polarization effects and many-body effects are implicitly included due to the electrostatic embedding scheme. Our results demonstrate that the electrostatically embedded many-body expansion combined with neural network provides a promising and systematic way to build next-generation force fields at high accuracy and low computational costs, especially for large systems. Force field (fiction) ; Polarizability ; Artificial neural network ; Ab initio ; Statistical physics ; Embedding ; Computer science ; Water model ; Hamiltonian (control theory) ; Field (mathematics) ; Classical mechanics ; Physics ; Molecular dynamics ; Mathematics ; Artificial intelligence ; Molecule ; Mathematical optimization ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: John M. Alred | Ksenia V. Bets | Yu Xie | Boris I. Yakobson
Source: Composites Science and Technology (article, 2018-03-28)
Field: Materials Science — Materials Chemistry

Machine learning electron density in sulfur crosslinked carbon nanotubes Sulfur ; Materials science ; Carbon nanotube ; Zigzag ; Covalent bond ; Density functional theory ; Composite number ; Composite material ; Molecular dynamics ; Carbon fibers ; Electron transfer ; Chemical physics ; Work (physics) ; Nanotechnology ; Computational chemistry ; Photochemistry ; Thermodynamics ; Organic chemistry ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: Ba Tuan Le | Dong Xiao | Yachun Mao | Dakuo He
Source: Infrared Physics & Technology (article, 2018-07-11)
Field: Chemistry — Analytical Chemistry

Coal analysis based on visible-infrared spectroscopy and a deep neural network Coal ; Energy value of coal ; Moisture ; Elemental analysis ; Environmental science ; Computer science ; Process engineering ; Materials science ; Chemistry ; Waste management ; Coal combustion products ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Algorithms and Applications
Authors: Ilaria Armenia | Valeria Grazú | Laura De Matteis | Pavlo Ivanchenko | Gianmario Martra | Rosalba Gornati | Jesús M. de la Fuente | Giovanni Bernardini
Source: Journal of Colloid and Interface Science (article, 2018-11-14)
Field: Materials Science — Biomaterials

Enzyme activation by alternating magnetic field: Importance of the bioconjugation methodology Iron oxide nanoparticles (NPs) are attractive materials for enzyme immobilization and, thanks to their superparamagnetism, can be accessed by remote stimuli. This can be exploited to activate molecules that are not remotely actuable. Here, we demonstrate that thermophilic enzymes chemically linked to NPs can be activated in a "wireless" fashion by an external alternate magnetic field (AMF). To this aim, we have conjugated, with different binding strategies, the thermophilic enzymes α-amylase and l-aspartate oxidase to iron oxide NPs obtaining NP-enzyme systems with activities depending on the different orientations and stretching of the enzymes. Since enzyme activation occurs without a significant rise of the "overall" temperature of the systems, we have speculated a local NP-enzyme heating that does not immediately interest the rest of the solution that remains at relatively low temperature, low enough to allow non-thermophilic enzymes to work together with the NP-conjugated thermophilic enzymes. Nanoactuation of thermophilic enzymes by AMF has potential applications in different fields. Indeed, multi-enzymatic processes with enzymes with different temperature optima could be carried out in the same reaction pot and thermolabile products could be efficiently produced by thermophilic enzymes without suffering for the high temperatures. Moreover, our findings represent a proof of concept of the possibility to achieve a fine-tuning of the enzyme-NP system with the aim to intervene in cell metabolism. Bioconjugation ; Enzyme ; Chemistry ; Biochemistry Nanoparticle-Based Drug Delivery ; Monoclonal and Polyclonal Antibodies Research ; Glycosylation and Glycoproteins Research
Authors: Rachid Hsissou | Atiqa Bekhta | Ahmed Elharfi | Bouchra Benzidia | Najat Hajjaji
Source: Portugaliae electrochimica acta (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Theoretical and Electrochemical Studies of the Coating Behavior of a New Epoxy Polymer: Hexaglycidyl Ethylene of Methylene Dianiline (HGEMDA) on E24 Steel in 3.5% NaCl The objective of our work is to study, develop, characterize and apply a new epoxy macromolecular matrix in coatings, and their optimization for the conservation of marine heritage. Epoxy resins are technologically and nano-technologically compatible thermosetting macromolecule matrices that are easy to implement, according to their structures, viscosimetric and rheological properties, for the protection of the heritage possibly subject to atmospheric corrosion. In this sense, we have tested the new macromolecular binder, hexaglycidyl ethylene of methylene dianiline (HGEMDA), crosslinked and formulated for studying the behavior of steel coatings in a marine environment. In order to evaluate HGEMDA coating performance, we have studied its adsorption behavior onto the surface of corroded steel, and explained the interactions between the coating macromolecule and the steel surface. The coating effect on E24 steel was studied by quantum chemical calculations. The adsorption of HGEMDA onto the surface of E24 steel has been well described by the Quantitative Structure of Relation Property model (QSPR). The stationary and transient electrochemical studies are very interesting, since the prediction of the coating behavior was performed by the semi-empirical PM6, DFT methods and the method of three Becke compounds of parameter (UB3LYP), based on 6-311 G. All our calculations were performed using Gaussian software (03). Methylene ; Epoxy ; Ethylene ; Electrochemistry ; Coating ; Materials science ; Polymer ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry ; Electrode ; Physical chemistry Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Conducting polymers and applications
Authors: Xingyu Wang | Xiaoning Qi | Zhibin Lin | Dante Battocchi
Source: Nanomaterials (article, 2018-12-04)
Field: Materials Science — Materials Chemistry

Graphene Reinforced Composites as Protective Coatings for Oil and Gas Pipelines Corrosion and corrosion-induced damage have resulted mostly in malfunctions and sometimes even in failures of metallic structures, including oil and gas pipelines. In this study, new high-performance composite coatings were developed by incorporating nanoparticles in the polymer resins with applications to oil and gas pipelines. The graphene nanoplatelets under different concentrations were used to prepare the epoxy-based nanocomposites and were then evaluated through mechanical and electrical tests. The integration of high-speed disk and ultrasonication were adopted as the dispersion technique to overcome nanoparticle agglomeration. Electron microscopy techniques were used to investigate the agglomeration. The new composites were qualitatively and quantitatively evaluated in terms of contact angle, surface roughness, adhesion to the substrate, corrosion resistance, and abrasion resistance. The results suggested that the composite with 0.5~1.0 wt.% of the graphene nanofillers led to the largest improvement in both mechanical and electrochemical properties. Distribution of nanoparticles in the matrix was observed using scanning electron microscopy and surface roughness using atomic force microscopy. Large agglomeration that was observed at the higher concentrations mainly resulted in the reduction of corrosion resistance and abrasion resistance. Materials science ; Corrosion ; Composite material ; Graphene ; Abrasion (mechanical) ; Epoxy ; Composite number ; Scanning electron microscope ; Surface roughness ; Nanoparticle ; Nanocomposite ; Surface finish ; Nanotechnology Corrosion Behavior and Inhibition ; Graphene research and applications ; Mechanical Behavior of Composites
Authors: Caihong Li | Lingling Li | Yuan Wu | Min Lu | Yi Yang | Lian Li
Source: Journal of Spectroscopy (article, 2018-08-27)
Field: Chemistry — Analytical Chemistry

Apple Variety Identification Using Near-Infrared Spectroscopy Near-infrared (NIR) spectra of apple samples were submitted in this paper to principal component analysis (PCA) and successive projections algorithm (SPA) to conduct variable selection. Three pattern recognition methods, backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), were applied to establish models for distinguishing apples of different varieties and geographical origins. Experimental results show that ELM models performed better on identifying apple variety and geographical origin than others. Especially, the SPA-ELM model could reach 98.33% identification accuracy on the calibration set and 96.67% on the prediction set. This study suggests that it is feasible to identify apple variety and cultivation region by using NIR spectroscopy. Principal component analysis ; Extreme learning machine ; Backpropagation ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Artificial neural network ; Set (abstract data type) ; Identification (biology) ; Variety (cybernetics) ; Computer science ; Feature selection ; Calibration ; Near-infrared spectroscopy ; Data set ; Selection (genetic algorithm) ; Biological system ; Machine learning ; Mathematics ; Statistics ; Botany ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Lara Lamon | David Asturiol | Andrea-Nicole Richarz | Elisabeth Joossens | Rabea Graepel | Karin Aschberger | Andrew Worth
Source: Particle and Fibre Toxicology (article, 2018-09-24)
Field: Materials Science — Materials Chemistry

Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques The workflow proposed by ECHA to read-across NMs was applied successfully. Chemoinformatic techniques were shown to provide key evidence for the assessment of the grouping hypothesis and the definition of similar NMs. The RAAF was found to be applicable to NMs. Workflow ; Computer science ; Hazard ; Process (computing) ; Risk analysis (engineering) ; Resource (disambiguation) ; Hazard analysis ; Similarity (geometry) ; Data science ; Data mining ; Artificial intelligence ; Database ; Reliability engineering ; Medicine ; Chemistry ; Engineering Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: C. B. Cooper | Edward J. Beard | Álvaro Vázquez‐Mayagoitia | Liliana Stan | Gavin B. G. Stenning | Daniel W. Nye | Julian A. Vigil | Tina Tomar | Jingwen Jia | Govardhana Babu Bodedla | Song Chen | Lucía Gallego | Santiago Franco | Antonio Carella | K. R. Justin Thomas | Song Xue | Xunjin Zhu | Jacqueline M. Cole
Source: Advanced Energy Materials (article, 2018-12-06)
Field: Materials Science — Materials Chemistry

Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells Abstract Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications. Panchromatic film ; Workflow ; Materials science ; Photovoltaic system ; Absorption (acoustics) ; Dye-sensitized solar cell ; Computer science ; Nanotechnology ; Process engineering ; Database ; Artificial intelligence ; Electrical engineering ; Engineering ; Image resolution Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Da‐Peng Wang | Jian‐Cheng Lai | Hui-Ying Lai | Sheng-Ran Mo | Keyu Zeng | Cheng‐Hui Li | Jing‐Lin Zuo
Source: Inorganic Chemistry (article, 2018-02-26)
Field: Materials Science — Polymers and Plastics

Distinct Mechanical and Self-Healing Properties in Two Polydimethylsiloxane Coordination Polymers with Fine-Tuned Bond Strength Coordination bonds are effective for constructing highly efficient self-healing materials as their strength is highly tunable. To design self-healing polymers with better performance, it is important to get a profound understanding of the structure-property relationships. However, this is challenging for self-healing polymers based on coordination bonds, because many parameters, such as bond energy, bond dynamics, and coordination number will have an essential effect on the mechanical and self-healing properties of the polymer. In this work, we synthesized two poly(dimethylsiloxane) (PDMS) polymers cross-linked by different Zn(II)-diiminopyridine coordination complexes (denoted as PDMS-NNN-Zn, PDMS-MeNNN-Zn respectively). The two cross-linking Zn(II)-diiminopyridine complexes are similar in coordination modes, but differ in coordination dynamics. As manifested by ITC, rheology, and tensile experiments, we confirm that the coordination bond in PDMS-MeNNN-Zn polymer films is weaker but more dynamic. Consequently, the PDMS-MeNNN-Zn polymer has poorer mechanical strength but higher stretchability and better self-healing properties. The inflicted cracks on PDMS-MeNNN-Zn polymer films can be completely healed after healing at room temperature for only 30 min with healing efficiencies higher than 90%. Such fast self-healing properties have never been achieved in self-healing polymers based on coordination bonds. Our results also demonstrate the important impact of the thermodynamic stability and kinetic lability of coordination complexes on the mechanical and self-healing properties of polymers. Such a comprehensive understanding is helpful for further design of novel synthetic polymers, which can achieve an optimal balance between the mechanical strength and self-healing performance. Polymer ; Self-healing ; Ultimate tensile strength ; Self-healing material ; Chemistry ; Bond strength ; Polydimethylsiloxane ; Rheology ; Coordination number ; Chemical engineering ; Composite material ; Nanotechnology ; Materials science ; Organic chemistry ; Adhesive Polymer composites and self-healing ; Synthetic Organic Chemistry Methods ; Carbon dioxide utilization in catalysis
Authors: Qingyun Li | Young‐Shin Jun
Source: Communications Chemistry (article, 2018-09-13)
Field: Materials Science — Biomaterials

The apparent activation energy and pre-exponential kinetic factor for heterogeneous calcium carbonate nucleation on quartz Abstract Nucleation occurs widely in materials synthesis and natural environments. However, in the nucleation rate equation, values for the apparent activation energy ( E a ) and the pre-exponential kinetic factor ( A ) are thus far unknown because real-time nanoscale observations are difficult to perform. Here we experimentally determine E a and A using heterogeneous calcium carbonate nucleation on quartz as a model system. Nucleation rates are measured with in situ grazing incidence small-angle X-ray scattering and ex situ atomic force microscopy, and the experiments are conducted with a fixed supersaturation of IAP/ K sp (calc) = 10 1.65 at 12, 25, and 31 °C. E a is calculated as 45 ± 7 kJ mol −1 , and A is 10 12.0 ± 1.1 nuclei μm −2 min −1 , or 10 2.9 ± 1.3 mol m −2 min −1 . Increasing the temperature shortens the induction time, but does not change nucleus sizes. These parameter values are critical for predicting and controlling the nucleation of materials. Nucleation ; Supersaturation ; Activation energy ; Kinetic energy ; Quartz ; Calcium carbonate ; Kinetics ; Chemistry ; Thermodynamics ; Mineralogy ; Materials science ; Analytical Chemistry (journal) ; Physical chemistry ; Chromatography ; Physics ; Metallurgy ; Composite material Calcium Carbonate Crystallization and Inhibition ; nanoparticles nucleation surface interactions ; Paleontology and Stratigraphy of Fossils
Authors: Nabhanila Nandi | Thomas Scaffidi | Pallavi Kushwaha | Seunghyun Khim | Mark E. Barber | Veronika Sunko | Federico Mazzola | P. D. C. King | H. Rösner | Philip J. W. Moll | Markus König | Joel E. Moore | Sean A. Hartnoll | A. P. Mackenzie
Source: ARCA (Università Ca' Foscari Venezia) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Unconventional magneto-transport in ultrapure PdCoO2 and PtCoO2 The single-band, quasi-two dimensional metals PdCoO2 and PtCoO2 have recently come to prominence because of their extremely long mean free paths, which establish them as some of the most electronically pure materials known, and as potential hosts of previously unobservable regimes of electronic transport. To fully establish their magnetotransport properties, we have studied the magnetoresistance and Hall effect in bulk single crystals to which electrical contacts have been made with high precision using focused ion beam machining. We observe a strong temperature dependence of the Hall resistivity in small applied fields, linked to a large violation of Kohler’s rule in the magnetoresistance. We discuss the extent to which these observations can be accounted for by standard transport theory. Magnetoresistance ; Unobservable ; Condensed matter physics ; Electrical resistivity and conductivity ; Hall effect ; Magneto ; Physics ; Materials science ; Magnetic field ; Magnet ; Quantum mechanics ; Mathematics Electronic and Structural Properties of Oxides ; ZnO doping and properties ; Copper-based nanomaterials and applications