jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amrita Sikder | Suhrit Ghosh
Source: Materials Chemistry Frontiers (article, 2019-01-01)
Field: Materials Science — Biomaterials

Hydrogen-bonding regulated assembly of molecular and macromolecular amphiphiles Hydrogen bonding driven assembly of amphiphiles (small molecules and polymers) produce diverse nanostructures in aqueous medium. Macromolecule ; Amphiphile ; Hydrogen bond ; Materials science ; Hydrogen ; Self-assembly ; Nanotechnology ; Chemical engineering ; Polymer science ; Chemistry ; Molecule ; Organic chemistry ; Copolymer ; Polymer ; Biochemistry ; Composite material Supramolecular Self-Assembly in Materials ; Advanced Polymer Synthesis and Characterization ; Polydiacetylene-based materials and applications
Authors: Jiashuai Wang | Zhe Li | Shaocun Yan | Xuebin Yu | Yanqing Ma | Lei Ma
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modifying the microstructure of algae-based active carbon and modelling supercapacitors using artificial neural networks An improved activated carbon material is synthesized from nostoc flagelliforme algae (NF) using an acid immersing method. The material has more pores and lower internal resistance compared with raw NF. Hydrofluoric acid can effectively decompose cellulose fibers and remove inorganic impurities, giving the carbon materials high mesopore volumes, which makes electrolyte ions rapidly transfer to the active site on the electrode surface. The specific capacitance of the sample was increased from 200 to 283 F g<sup>-1</sup> after immersing in hydrofluoric acid. In addition, the symmetric supercapacitor shows an excellent energy density of 22 W h kg<sup>-1</sup> at a power density of 80 W kg<sup>-1</sup>. The capacitance remains at 101.7% after 10 000 cycles. Furthermore, in order to find the relationship between the biochar structure and electrochemical performance in supercapacitors, an artificial neural network (ANN) method is used for studying the complex synergy mechanism. The specific capacitance is modelled using various input factors like aspect ratio (<i>r</i> <sub>L/D</sub>), cellulose ratio (CL(%)), specific surface area (<i>S</i> <sub>BET</sub>), pore volume (<i>V</i> <sub>tot</sub>), internal resistance (<i>R</i> <sub>s</sub>) and so on. The Levenberg-Marquart back propagation algorithm with sigmoid and ReLu active function is adopted to train the model. Random forest is used to analyse the relative importance of every input factor on specific capacitance. Results show that the model can estimate the energy storage with a mean squared error of 4.39 for materials with specific structure. Importance analyses indicate the first three significant variables are <i>S</i> <sub>BET</sub>, <i>R</i> <sub>s</sub> and <i>V</i> <sub>por</sub>. The ANN model can accurately predict the electrical properties of biomass-based carbon materials, and also provide guidance for the selection of energy storage materials in the future. Supercapacitor ; Artificial neural network ; Microstructure ; Algae ; Materials science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Computer science ; Artificial intelligence ; Botany ; Biology ; Engineering ; Capacitance ; Composite material Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Aerogels and thermal insulation
Authors: Vladimir Ladygin | Pavel Korotaev | A. V. Yanilkin | Alexander V. Shapeev
Source: Computational Materials Science (article, 2019-10-19)
Field: Materials Science — Materials Chemistry

Lattice dynamics simulation using machine learning interatomic potentials Interatomic potential ; Anharmonicity ; Molecular dynamics ; Phonon ; Autocorrelation ; Quantum ; Statistical physics ; Density functional theory ; Lattice (music) ; Work (physics) ; Physics ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Chun-I Wang | Mac Kevin E. Braza | Gil C. Claudio | Ricky B. Nellas | Chao‐Ping Hsu
Source: The Journal of Physical Chemistry A (article, 2019-08-20)
Field: Materials Science — Materials Chemistry

Machine Learning for Predicting Electron Transfer Coupling Electron transfer coupling is a critical factor in determining electron transfer rates. This coupling strength can be sensitive to details in molecular geometries, especially intermolecular configurations. Thus, studying charge transporting behavior with a full first-principle approach demands a large amount of computation resources in quantum chemistry (QC) calculation. To address this issue, we developed a machine learning (ML) approach to evaluate electronic coupling. A prototypical ML model for an ethylene system was built by kernel ridge regression with Coulomb matrix representation. Since the performance of the ML models highly dependent on their building strategies, we systematically investigated the generality of the ML models, the choice of features and target labels. The best ML model trained with 40 000 samples achieved a mean absolute error of 3.5 meV and greater than 98% accuracy in predicting phases. The distance and orientation dependence of electronic coupling was successfully captured. Bypassing QC calculation, the ML model saved 10-10<sup>4</sup> times the computation cost. With the help of ML, reliable charge transport models and mechanisms can be further developed. Coupling (piping) ; Electron transfer ; Transfer of learning ; Computer science ; Transfer (computing) ; Artificial intelligence ; Materials science ; Chemistry ; Parallel computing ; Photochemistry Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Electrochemical Analysis and Applications
Authors: G. Botta | Mario Romeo | António Augusto Fernandes | Stefano Trabucchi | P.V. Aravind
Source: Energy Conversion and Management (article, 2019-02-25)
Field: Materials Science — Materials Chemistry

Dynamic modeling of reversible solid oxide cell stack and control strategy development Stack (abstract data type) ; Solid oxide fuel cell ; Flexibility (engineering) ; Control theory (sociology) ; Grid ; Process engineering ; Heat transfer ; Work (physics) ; Automotive engineering ; Computer science ; Engineering ; Mechanical engineering ; Control (management) ; Chemistry ; Electrode ; Mechanics Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Catalysis and Oxidation Reactions
Authors: Dan Wei | Jie Yang | Minqiang Jiang | Bingchen Wei | Yun-Jiang Wang | Lan-Hong Dai
Source: Physical review. B./Physical review. B (article, 2019-01-31)
Field: Materials Science — Materials Chemistry

Revisiting the structure–property relationship of metallic glasses: Common spatial correlation revealed as a hidden rule What determines the glass property remains one of the major unsolved problems in both condensed matter physics and materials science. Despite extensive research attempting to identify possible structural features as property signatures in glasses obeying the conventional philosophy of ``structure determines property'' in matter, the hidden rule about why some proposed structures predict properties effectively but others do not is still poorly understood. Here we revisit some earlier proposed successful ``structural descriptors'' in glasses, e.g., vibrational mean-squared displacement, flexibility volume, participation fraction of low-frequency vibrational modes, and two-body excess entropy, correlating them with the long-time dynamic property of model glass probed via the activation energy for local structural excitation. We find that all four structural descriptors correlate strongly with the activation energy, presenting large Pearson's correlation coefficients. By examining the spatial nature of the activation energy and the structural descriptors, a common rule for the robustness of structure--property relationships in glasses is established, according to which there exists a critical characteristic correlation length. We further demonstrate the concept that complex structures determine the glass property by manipulating the cutoff distance used to define the two-body excess entropy. Only if this structural descriptor is defined involving atoms beyond the first nearest neighbor does it reproduce the feature of common spatial correlation range in glass. The presence of a common spatial correlation length strongly indicates that it is necessary to include the spatial correlation of a complex structure accounting for the dynamic property of metallic glasses. Statistical physics ; Correlation ; Configuration entropy ; Property (philosophy) ; Common property ; Spatial correlation ; Cutoff ; Coordination number ; Mathematics ; Physics ; Statistics ; Quantum mechanics ; Geometry Material Dynamics and Properties ; Glass properties and applications ; Metallic Glasses and Amorphous Alloys
Authors: Graciela Lizeth Pérez-González | Luis Jesús Villarreal-Gómez | Aracely Serrano‐Medina | Erick José Torres-Martínez | José Manuel Cornejo‐Bravo
Source: International Journal of Nanomedicine (review, 2019-07-01)
Field: Materials Science — Biomaterials

&lt;p&gt;Mucoadhesive electrospun nanofibers for drug delivery systems: applications of polymers and the parameters’ roles&lt;/p&gt; Electrospun nanofibers have been widely studied for many medical applications. They can be designed with specific features, including mucoadhesive properties. This review summarizes the polymeric scaffolds obtained by the electrospinning process that has been applied for drug release in different mucosal sites such as oral, ocular, gastroenteric, vaginal, and nasal. We analyzed the electrospinning parameters that have to be optimized to create reproducible and efficient mucoadhesive nanofibers, among them are: electrical field, polymer concentration, viscosity, flow rate, needle-collector distance, solution conductivity, solvent, environmental parameters, and electrospinning setup. We also revised the mucoadhesive theories as well as the mucoadhesive properties of the polymers used. This review shows that the most studied mucosal site is the oral cavity, because it is accessible and easy to evaluate, while the rest are uncomfortable for the patient and difficult to assess in vivo. We found problems that need to be solved for mucoadhesive electrospun nanofibers, such as improving adhesion strength and mucosal permanence time, and the design of unidirectional release, multilayer systems for the treatment of several pathologies, to ensure the drug concentration in the tissue or target organ. Electrospinning ; Nanofiber ; Materials science ; Drug delivery ; Polymer ; Mucoadhesion ; Nanotechnology ; Adhesion ; Biomedical engineering ; Chemical engineering ; Drug carrier ; Composite material ; Medicine Electrospun Nanofibers in Biomedical Applications ; Advanced Drug Delivery Systems ; Polymer Surface Interaction Studies
Authors: Siham Slassi | Adeline Fix‐Tailler | Gérald Larcher | Amina Amine | Abdelkrim El‐Ghayoury
Source: Heteroatom Chemistry (article, 2019-01-03)
Field: Chemistry — Organic Chemistry

Imidazole and Azo-Based Schiff Bases Ligands as Highly Active Antifungal and Antioxidant Components We describe, herein, the synthesis, full characterization, and optical properties of four different ligands L1-L4 which associate an azo group, an imidazole unit, and a Schiff base fragment. The UV-visible absorption bands are characteristic of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>π</mml:mi><mml:mo>→</mml:mo><mml:mi>π</mml:mi><mml:mo>⁎</mml:mo></mml:math>and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>n</mml:mi><mml:mo>→</mml:mo><mml:mi>π</mml:mi><mml:mo>⁎</mml:mo></mml:math>transitions with an additional charge transfer between the azobenzene moiety and the imino group. Finally the determination of MIC 80 values against pathogenic fungi such as S. apiospermum , A. fumigatus, and C. albicans revealed that these ligands have effective antifungal properties with highest activities (MIC 80 ) on C. albicans for the azole based ligands L1-L3 . DPPH radical scavenging of the studied ligands was also tested. Chemistry ; Moiety ; Imidazole ; Schiff base ; Azobenzene ; Stereochemistry ; Organic chemistry ; Molecule Synthesis and biological activity ; Free Radicals and Antioxidants ; Photochromic and Fluorescence Chemistry
Authors: Jikun Chen | Wei Mao | Binghui Ge | Jiaou Wang | Xinyou Ke | Vei Wang | Yiping Wang | M. Döbeli | W. T. Geng | Hiroyuki Matsuzaki | Jian Shi | Yong Jiang
Source: Nature Communications (article, 2019-02-11)
Field: Materials Science — Materials Chemistry

Revealing the role of lattice distortions in the hydrogen-induced metal-insulator transition of SmNiO3 The discovery of hydrogen-induced electronic phase transitions in strongly correlated materials such as rare-earth nickelates has opened up a new paradigm in regulating materials' properties for both fundamental study and technological applications. However, the microscopic understanding of how protons and electrons behave in the phase transition is lacking, mainly due to the difficulty in the characterization of the hydrogen doping level. Here, we demonstrate the quantification and trajectory of hydrogen in strain-regulated SmNiO<sub>3</sub> by using nuclear reaction analysis. Introducing 2.4% of elastic strain in SmNiO<sub>3</sub> reduces the incorporated hydrogen concentration from ~10<sup>21</sup> cm<sup>-3</sup> to ~10<sup>20</sup> cm<sup>-3</sup>. Unexpectedly, despite a lower hydrogen concentration, a more significant modification in resistivity is observed for tensile-strained SmNiO<sub>3</sub>, substantially different from the previous understanding. We argue that this transition is explained by an intermediate metastable state occurring in the transient diffusion process of hydrogen, despite the absence of hydrogen at the post-transition stage. Hydrogen ; Metastability ; Materials science ; Phase transition ; Doping ; Chemical physics ; Lattice (music) ; Electrical resistivity and conductivity ; Electron ; Condensed matter physics ; Nanotechnology ; Chemistry ; Physics ; Optoelectronics ; Nuclear physics Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Ting Han | Zhanshi Yao | Zijie Qiu | Zheng Zhao | Kaiyi Wu | Jianguo Wang | Andrew W. Poon | Jacky W. Y. Lam | Ben Zhong Tang
Source: Nature Communications (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Photoresponsive spiro-polymers generated in situ by C–H-activated polyspiroannulation The development of facile and efficient polymerizations toward functional polymers with unique structures and attractive properties is of great academic and industrial significance. Here we develop a straightforward C-H-activated polyspiroannulation route to in situ generate photoresponsive spiro-polymers with complex structures. The palladium(II)-catalyzed stepwise polyspiroannulations of free naphthols and internal diynes proceed efficiently in dimethylsulfoxide at 120 °C without the constraint of apparent stoichiometric balance in monomers. A series of functional polymers with multisubstituted spiro-segments and absolute molecular weights of up to 39,000 are produced in high yields (up to 99%). The obtained spiro-polymers can be readily fabricated into different well-resolved fluorescent photopatterns with both turn-off and turn-on modes based on their photoinduced fluorescence change. Taking advantage of their photoresponsive refractive index, we successfully apply the polymer thin films in integrated silicon photonics techniques and achieve the permanent modification of resonance wavelengths of microring resonators by UV irradiation. Polymer ; Materials science ; Monomer ; Fluorescence ; Photonics ; Silicon ; Functional polymers ; In situ ; Stoichiometry ; Polymer chemistry ; Nanotechnology ; Chemical engineering ; Photochemistry ; Copolymer ; Chemistry ; Optoelectronics ; Organic chemistry ; Optics Luminescence and Fluorescent Materials ; Synthetic Organic Chemistry Methods ; Oxidative Organic Chemistry Reactions
Authors: David K. Sang | Huide Wang | Meng Qiu | Rui Cao | Zhinan Guo | Jinlai Zhao | Yu Li | Quanlan Xiao | Dianyuan Fan | Han Zhang
Source: Nanomaterials (article, 2019-01-09)
Field: Materials Science — Materials Chemistry

Two Dimensional β-InSe with Layer-Dependent Properties: Band Alignment, Work Function and Optical Properties Density functional theory calculations of the layer (L)-dependent electronic band structure, work function and optical properties of β-InSe have been reported. Owing to the quantum size effects (QSEs) in β-InSe, the band structures exhibit direct-to-indirect transitions from bulk β-InSe to few-layer β-InSe. The work functions decrease monotonically from 5.22 eV (1 L) to 5.0 eV (6 L) and then remain constant at 4.99 eV for 7 L and 8 L and drop down to 4.77 eV (bulk β-InSe). For optical properties, the imaginary part of the dielectric function has a strong dependence on the thickness variation. Layer control in two-dimensional layered materials provides an effective strategy to modulate the layer-dependent properties which have potential applications in the next-generation high performance electronic and optoelectronic devices. Materials science ; Work function ; Dielectric ; Density functional theory ; Optoelectronics ; Work (physics) ; Dielectric function ; Condensed matter physics ; Electronic structure ; Layer (electronics) ; Band gap ; Electronic band structure ; Nanotechnology ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Martin R. L. Paine | Jingbo Liu | Danning Huang | Shane R. Ellis | Dennis Trede | Jan Hendrik Kobarg | Ron M. A. Heeren | Facundo M. Fernández | Tobey J. MacDonald
Source: Scientific Reports (article, 2019-02-18)
Field: Chemistry — Spectroscopy

Three-Dimensional Mass Spectrometry Imaging Identifies Lipid Markers of Medulloblastoma Metastasis Treatment for medulloblastoma (MB) - the most common malignant pediatric brain tumor - includes prophylactic radiation administered to the entire brain and spine due to the high incidence of metastasis to the central nervous system. However, the majority of long-term survivors are left with permanent and debilitating neurocognitive impairments as a result of this therapy, while the remaining 30-40% of patients relapse with terminal metastatic disease. Development of more effective targeted therapies has been hindered by our lack of understanding of the underlying mechanisms regulating the metastatic process in this disease. To understand the mechanism by which MB metastasis occurs, three-dimensional matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) experiments were performed on whole brains from a mouse model of human medulloblastoma. Analyzing the tumor and surrounding normal brain in its entirety enabled the detection of low abundance, spatially-heterogeneous lipids associated with tumor development. Boundaries of metastasizing and non-metastasizing primary tumors were readily defined, leading to the identification of lipids associated with medulloblastoma metastasis, including phosphatidic acids, phosphatidylethanolamines, phosphatidylserines, and phosphoinositides. These lipids provide a greater insight into the metastatic process and may ultimately lead to the discovery of biomarkers and novel targets for the diagnosis and treatment of metastasizing MB in humans. Medulloblastoma ; Metastasis ; Mass spectrometry imaging ; Medicine ; Brain metastasis ; Cancer research ; Carcinogenesis ; Pathology ; Oncology ; Cancer ; Mass spectrometry ; Internal medicine ; Chemistry Mass Spectrometry Techniques and Applications ; Nanopore and Nanochannel Transport Studies ; Advanced Proteomics Techniques and Applications
Authors: Guangyong Chen | Pengfei Chen | Chang‐Yu Hsieh | Chee‐Kong Lee | Benben Liao | Renjie Liao | Weiwen Liu | Jiezhong Qiu | Qiming Sun | Jie Tang | Richard S. Zemel | Shengyu Zhang
Source: arXiv (Cornell University) (preprint, 2019-06-22)
Field: Materials Science — Materials Chemistry

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy dataset expands the volume and diversity of existing molecular datasets. Our extensive benchmarks of the state-of-the-art graph neural network models on Alchemy clearly manifest the usefulness of new data in validating and developing machine learning models for chemistry and material science. We further launch a contest to attract attentions from researchers in the related fields. More details can be found on the contest website \footnote{https://alchemy.tencent.com}. At the time of benchamrking experiment, we have generated 119,487 molecules in our Alchemy dataset. More molecular samples are generated since then. Hence, we provide a list of molecules used in the reported benchmarks. Alchemy ; CONTEST ; Computer science ; Benchmarking ; Graph ; Quantum chemical ; Chemistry ; Artificial intelligence ; Molecule ; Theoretical computer science ; Art history ; Philosophy ; Organic chemistry ; Art Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Shenheng Guan | Michael F. Moran | Bin Ma
Source: Molecular & Cellular Proteomics (article, 2019-06-28)
Field: Chemistry — Spectroscopy

Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed. The LC-MS/MS properties or behaviors are indexed retention times (iRT), MS1 or survey scan charge state distributions, and sequence ion intensities of HCD spectra. A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models. Two featurization schemes were proposed and demonstrated to allow for efficient encoding of modifications. The iRT and charge state distribution models were trained with on order of 10<sup>5</sup> data points each. An HCD sequence ion prediction model was trained with 2 × 10<sup>6</sup> experimental spectra. The iRT prediction model and HCD sequence ion prediction model provide improved accuracies over the start-of-the-art models available in literature. The MS1 charge state distribution prediction model offers excellent performance. The prediction models can be used to enhance peptide identification and quantification in data-dependent acquisition and data-independent acquisition (DIA) experiments as well as to assist MRM (multiple reaction monitoring) and PRM (parallel reaction monitoring) experiment design. Deep learning ; Construct (python library) ; Sequence (biology) ; Artificial intelligence ; Computer science ; Artificial neural network ; Sequence learning ; Pattern recognition (psychology) ; Chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies