jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenqing Shen | Diego Vaca | Satish Kumar
Source: Nanoscale and Microscale Thermophysical Engineering (article, 2020-08-19)
Field: Materials Science — Materials Chemistry

Reconsidering Uncertainty from Frequency Domain Thermoreflectance Measurement and Novel Data Analysis by Deep Learning Frequency-domain thermoreflectance (FDTR) is a popular technique to investigate thermal properties of bulk and thin film materials. The FDTR data analysis involves fitting experimental data to a theoretical model whose accuracy may be affected by improper fitting approach and by convergence to local minima. This work proposes a novel data analysis approach using deep learning techniques. The developed deep learning model for FDTR (DL-FDTR) can accurately predict thermal conductivity, volumetric heat capacity and thermal boundary conductance with mean error below 5% for bulk samples coated with Au. DL-FDTR predictions can serve as an initial guess to the traditional fitting algorithms and can efficiently avoid local minima with regular fitting options, therefore improving the accuracy of data fitting and uncertainty evaluation. Maxima and minima ; Thermal conductivity ; Materials science ; Convergence (economics) ; Frequency domain ; Curve fitting ; Thermal ; Deep learning ; Work (physics) ; Computer science ; Boundary (topology) ; Algorithm ; Artificial intelligence ; Machine learning ; Thermodynamics ; Mathematics ; Physics ; Mathematical analysis Thermal properties of materials ; Heat Transfer and Optimization ; Heat Transfer and Boiling Studies
Authors: Silvan Käser | Debasish Koner | Anders S. Christensen | O. Anatole von Lilienfeld | Markus Meuwly
Source: The Journal of Physical Chemistry A (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Machine Learning Models of Vibrating H<sub>2</sub>CO: Comparing Reproducing Kernels, FCHL, and PhysNet Machine learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a comparative assessment of ML models based on state-of-the-art variants of deep neural networks (NNs), reproducing kernel Hilbert space (RKHS+F), and kernel ridge regression (KRR) is presented. Learning curves for energies and atomic forces indicate rapid convergence toward excellent predictions for B3LYP, MP2, and CCSD(T)-F12 reference results for modestly sized (in the hundreds) training sets. Typically, learning curve offsets decay as one goes from NN (PhysNet) to RKHS+F to KRR (FCHL). Conversely, the predictive power for extrapolation of energies toward new geometries increases in the same order with RKHS+F and FCHL performing almost equally. For harmonic vibrational frequencies, the picture is less clear, with PhysNet and FCHL yielding accuracies of ∼1 and ∼0.2 cm<sup>-1</sup>, respectively, no matter which reference method, while RKHS+F models level off for B3LYP and exhibit continued improvements for MP2 and CCSD(T)-F12. Finite-temperature molecular dynamics (MD) simulations using the PESs from the three ML methods with identical initial conditions yield indistinguishable infrared spectra with good performance compared with experiment except for the high-frequency modes involving hydrogen stretch motion which is a known limitation of MD for vibrational spectroscopy. For sufficiently large training set sizes, all three models can detect insufficient convergence ("noise") of the reference electronic structure calculations in that the learning curves level off. Transfer learning (TL) from B3LYP to CCSD(T)-F12 with PhysNet indicates that additional improvements in data efficiency can be achieved. Computer science ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Razieh Pourdarbani | Sajad Sabzi | Davood Kalantari | Juan Ignacio Arribas
Source: Chemometrics and Intelligent Laboratory Systems (article, 2020-09-06)
Field: Chemistry — Analytical Chemistry

Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stages Malus ; Partial least squares regression ; Starch ; Wavelength ; Linear regression ; Biological system ; Chemistry ; Near-infrared spectroscopy ; Artificial neural network ; Spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Mathematics ; Botany ; Optics ; Artificial intelligence ; Computer science ; Food science ; Chromatography ; Optoelectronics ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Alexander T. Egger | Lukas Hörmann | Andreas Jeindl | Michael Scherbela | Veronika Obersteiner | Milica Todorović | Patrick Rinke | Oliver T. Hofmann
Source: Advanced Science (article, 2020-06-28)
Field: Materials Science — Materials Chemistry

Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search Abstract Density functional theory calculations are combined with machine learning to investigate the coverage‐dependent charge transfer at the tetracyanoethylene/Cu(111) hybrid organic/inorganic interface. The study finds two different monolayer phases, which exhibit a qualitatively different charge‐transfer behavior. Our results refute previous theories of long‐range charge transfer to molecules not in direct contact with the surface. Instead, they demonstrate that experimental evidence supports our hypothesis of a coverage‐dependent structural reorientation of the first monolayer. Such phase transitions at interfaces may be more common than currently envisioned, beckoning a thorough reevaluation of organic/inorganic interfaces. Tetracyanoethylene ; Monolayer ; Charge (physics) ; Chemical physics ; Organic molecules ; Density functional theory ; Materials science ; Interface (matter) ; Molecule ; Transfer (computing) ; Range (aeronautics) ; Nanotechnology ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Photochemistry ; Organic chemistry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Molecular Junctions and Nanostructures
Authors: Koki Muraoka | Watcharop Chaikittisilp | Tatsuya Okubo
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Multi-objective <i>de novo</i> molecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization Organic structure-directing agents (OSDAs) are often employed for synthesis of zeolites with desired frameworks. <i>A priori</i> prediction of such OSDAs has mainly relied on the interaction energies between OSDAs and zeolite frameworks, without cost considerations. For practical purposes, the cost of OSDAs becomes a critical issue. Therefore, the development of a computational <i>de novo</i> prediction methodology that can speed up the trial-and-error cycle in the search for less expensive OSDAs is desired. This study utilized a nature-inspired ant colony optimization method to predict physicochemically and/or economically preferable OSDAs, while also taking molecular similarity and heuristics of zeolite synthesis into consideration. The prediction results included experimentally known OSDAs, candidates having structures closely related to known OSDAs, and novel ones, suggesting the applicability of this approach. Organic molecules ; Zeolite ; Ant colony optimization algorithms ; Crystallization ; ANT ; Molecule ; Chemical space ; Computer science ; Biochemical engineering ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Artificial intelligence ; Organic chemistry ; Engineering ; Biochemistry ; Catalysis ; Drug discovery Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Mesoporous Materials and Catalysis
Authors: Sean M. Collins | Demie Kepaptsoglou | Jingwei Hou | Christopher W. Ashling | Guillaume Radtke | Thomas D. Bennett | Paul A. Midgley | Quentin M. Ramasse
Source: Nano Letters (article, 2020-01-16)
Field: Materials Science — Surfaces, Coatings and Films

Functional Group Mapping by Electron Beam Vibrational Spectroscopy from Nanoscale Volumes Vibrational spectroscopies directly record details of bonding in materials, but spatially resolved methods have been limited to surface techniques for mapping functional groups at the nanoscale. Electron energy loss spectroscopy (EELS) in the scanning transmission electron microscope presents a route to functional group analysis from nanoscale volumes using transmitted subnanometer electron probes. Here, we now use vibrational EELS to map distinct carboxylate and imidazolate linkers in a metal-organic framework (MOF) crystal-glass composite material. Domains <100 nm in size are observed using vibrational EELS, with recorded spatial resolution <15 nm at interfaces in the composite. This nanoscale functional group mapping is confirmed by correlated EELS at core ionization edges as well as X-ray energy dispersive spectroscopy for elemental mapping of the metal centers of the two constituent MOFs. These results present a complete nanoscale analysis of the building blocks of the MOF composite and establish spatially resolved functional group analysis using electron beam spectroscopy for crystalline and amorphous organic and metal-organic solids. Nanoscopic scale ; Electron energy loss spectroscopy ; Spectroscopy ; Scanning transmission electron microscopy ; Amorphous solid ; Materials science ; Nanostructure ; Metal-organic framework ; Energy-dispersive X-ray spectroscopy ; Infrared spectroscopy ; Scanning electron microscope ; Density functional theory ; Transmission electron microscopy ; Chemistry ; Nanotechnology ; Chemical physics ; Crystallography ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Composite material Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Jeffery A. Aguiar | Andrea Jokisaari | Matthew Kerr | Robert Allen Roach
Source: Nature Communications (article, 2020-05-22)
Field: Materials Science — Materials Chemistry

Bringing nuclear materials discovery and qualification into the 21st century Time horizons for nuclear materials development and qualification must be shortened to realize future nuclear energy concepts. Inspired by the Materials Genome Initiative, we present an integrated approach to materials discovery and qualification to insert new materials into service. Nuclear material ; Nanotechnology ; Computer science ; Materials science ; Physics ; Nuclear physics Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Titanium Alloys Microstructure and Properties
Authors: Fausto Martelli | Fabio Leoni | Francesco Sciortino | John Russo
Source: The Journal of Chemical Physics (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Connection between liquid and non-crystalline solid phases in water The origin of water anomalies hides in an experimentally inaccessible region of the phase diagram known as no-man's land, bounded at low temperature by the domain of stability of amorphous glasses, and at high temperature by the homogeneous nucleation line, below which liquid water loses its metastability. The existence of at least two different forms of glass on one side, i.e., the low-density amorphous (LDA) and the high-density amorphous (HDA) ices, and of one anomalous liquid on the other side, points to a hidden connection between these states, whose understanding has the potential to uncover what happens in no-man's land and shed light on the complex nature of water's behavior. Here, we develop a Neural Network scheme capable of discerning local structures beyond tetrahedrality. Applied over a wide region of the water's phase diagram, we show that the local structures that characterize both LDA and HDA amorphous phases are indeed embedded in the supercooled liquid phase. Remarkably, the rapid increase in the LDA-like population with supercooling occurs in the same temperature and pressure region where thermodynamic fluctuations are maximized, linking these structures with water's anomalies. At the same time, the population of HDA-like environments rapidly increases with pressure, becoming the majority component at high density. Our results show that both LDA and HDA are genuine glasses, and provide a microscopic connection between the non-equilibrium and equilibrium phase diagrams of water. Supercooling ; Phase diagram ; Amorphous solid ; Metastability ; Nucleation ; Population ; Phase (matter) ; Connection (principal bundle) ; Thermodynamics ; Chemical physics ; Polyamorphism ; Materials science ; Mineralogy ; Statistical physics ; Physics ; Chemistry ; Crystallography ; Mathematics ; Geometry Material Dynamics and Properties ; Theoretical and Computational Physics ; Complex Systems and Time Series Analysis
Authors: Moritz Balkenhohl | Harish Jangra | Ilya S. Makarov | Shumei Yang | Hendrik Zipse | Paul Knochel
Source: Angewandte Chemie International Edition (article, 2020-05-13)
Field: Chemistry — Organic Chemistry

A Predictive Model Towards Site‐Selective Metalations of Functionalized Heterocycles, Arenes, Olefins, and Alkanes using TMPZnCl⋅LiCl The development of a predictive model towards site-selective deprotometalation reactions using TMPZnCl⋅LiCl is reported (TMP=2,2,6,6-tetramethylpiperidinyl). The pK<sub>a</sub> values of functionalized N-, S-, and O-heterocycles, arenes, alkenes, or alkanes were calculated and compared to the experimental deprotonation sites. Large overlap (>80 %) between the calculated and empirical deprotonation sites was observed, showing that thermodynamic factors strongly govern the metalation regioselectivity. In the case of olefins, calculated frozen state energies of the deprotonated substrates allowed a more accurate prediction. Additionally, various new N-heterocycles were analyzed and the metalation regioselectivities rationalized using the predictive model. Deprotonation ; Metalation ; Chemistry ; Regioselectivity ; Computational chemistry ; Organic chemistry ; Catalysis Coordination Chemistry and Organometallics ; Asymmetric Synthesis and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Tomohiko Iwano | Kenichi Yoshimura | Shunsuke Inoue | Toru Odate | Koretsugu Ogata | Shinji Funatsu | Hiroshi Tanihata | Tetsuo Kondo | Daisuke Ichikawa | Sén Takeda
Source: British journal of surgery (article, 2020-04-04)
Field: Chemistry — Spectroscopy

Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry Probe electrospray ionization mass spectrometry (PESI-MS) is an ambient ionization-based mass spectrometry method that surpasses the original electrospray ionization technique in features such as the rapidity of analysis, simplicity of the equipment and procedure, and lower cost. This study found that the PESI-MS system with machine learning has the potential to establish a lipid-based diagnosis of breast cancer with higher accuracy, using a simpler approach. Rapid mass spectrometry for breast cancer. Medicine ; Mass spectrometry ; Profiling (computer programming) ; Breast cancer ; Electrospray ionization ; Chromatography ; Cancer ; Internal medicine Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Carla Santos | Rebeca Cruz | Diogo B Gonçalves | Rafael Queirós | Mark Bloore | Zoltán Kovács | Isabel Hoffmann | Susana Casal
Source: Journal of AOAC International (article, 2020-08-21)
Field: Chemistry — Analytical Chemistry

Non-Destructive Measurement of the Internal Quality of Citrus Fruits Using a Portable NIR Device The citrus industry has grown exponentially as a result of increasing demand on its consumption, giving it high standing among other fruit crops. Therefore, the citrus sector seeks rapid, easy, and non-destructive approaches to evaluate in real time and in situ the external and internal changes in physical and nutritional quality at any stage of fruit development or storage. In particular, vitamin C is among the most important micronutrients for consumers, but its measurement relies on laborious analytical methodologies. In this study, a portable near infrared spectroscopy (NIRS) sensor was used in combination with chemometrics to develop robust and accurate models to study the ripeness of several citrus fruits (oranges, lemons, clementines, tangerines, and Tahiti limes) and their vitamin C content. Ascorbic acid, dehydroascorbic acid, and total vitamin C were determined by HILIC-HPLC-UV, while soluble solids and total acidity were evaluated by standard analytical procedures. Partial least squares regression (PLSR) was used to build regression models which revealed suitable performance regarding the prediction of quality and ripeness parameters in all tested fruits. Models for ascorbic acid, dehydroascorbic acid, total vitamin C, soluble solids, total acidity, and juiciness showed Rcv2 = 0.77-0.87, Rcv2 = 0.29-0.79, Rcv2 = 0.77-0.86, Rcv2 = 0.75-0.97, Rcv2 = 0.24-0.92, and Rcv2 = 0.38-0.75, respectively. Prediction models of oranges and Tahiti limes showed good to excellent performance regarding all tested conditions. The resulting models confirmed that NIRS technology is a time- and cost-effective approach for predicting citrus fruit quality, which can easily be used by the various stakeholders from the citrus industry. Partial least squares regression ; Ripeness ; Dehydroascorbic acid ; Ascorbic acid ; Vitamin C ; Chemistry ; Chemometrics ; Food science ; Micronutrient ; Vitamin ; Chromatography ; Mathematics ; Biochemistry ; Ripening ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Essential Oils and Antimicrobial Activity
Authors: Stephen J. Cox
Source: Proceedings of the National Academy of Sciences (article, 2020-08-03)
Field: Materials Science — Materials Chemistry

Dielectric response with short-ranged electrostatics The dielectric nature of polar liquids underpins much of their ability to act as useful solvents, but its description is complicated by the long-ranged nature of dipolar interactions. This is particularly pronounced under the periodic boundary conditions commonly used in molecular simulations. In this article, the dielectric properties of a water model whose intermolecular electrostatic interactions are entirely short-ranged are investigated. This is done within the framework of local molecular-field theory (LMFT), which provides a well-controlled mean-field treatment of long-ranged electrostatics. This short-ranged model gives a remarkably good performance on a number of counts, and its apparent shortcomings are readily accounted for. These results not only lend support to LMFT as an approach for understanding solvation behavior, but also are relevant to those developing interaction potentials based on local descriptions of liquid structure. Intermolecular force ; Dielectric ; Chemical physics ; Electrostatics ; Polar ; Dielectric response ; Work (physics) ; Molecule ; Solvent ; Materials science ; Chemical polarity ; Computational chemistry ; Chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Organic chemistry ; Optoelectronics ; Quantum mechanics Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies ; Force Microscopy Techniques and Applications
Authors: Saima Kanwal | Jing Wen | Binbin Yu | Dileep Kumar | Xu Chen | Yi Kang | Chunyan Bai | Dawei Zhang
Source: Nanomaterials (article, 2020-03-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Efficiency, Broadband, Near Diffraction-Limited, Dielectric Metalens in Ultraviolet Spectrum Ultraviolet (UV) optical devices have plenteous applications in the fields of nanofabrication, military, medical, sterilization, and others. Traditional optical components utilize gradual phase accumulation phenomena to alter the wave-front of the light, making them bulky, expensive, and inefficient. A dielectric metasurface could provide an auspicious approach to precisely control the amplitude, phase, and polarization of the incident light by abrupt, discrete phase changing with high efficiency due to low absorption losses. Metalenses, being one of the most attainable applications of metasurfaces, can extremely reduce the size and complexity of the optical systems. We present the design of a high-efficiency transmissive UV metalens operating in a broadband range of UV light (250-400 nm) with outstanding focusing characteristics. The polarization conversion efficiency of the nano-rod unit and the focusing efficiency of the metasurface are optimized to be as high as 96% and 77%, respectively. The off-axis focusing characteristics at different incident angles are also investigated. The designed metalens that is composed of silicon nitride nanorods will significantly uphold the advancement of UV photonic devices and can provide opportunities for the miniaturization and integration of the UV nanophotonics and its applications. Materials science ; Optoelectronics ; Nanophotonics ; Miniaturization ; Optics ; Ray ; Photonics ; Ultraviolet ; Broadband ; Dielectric ; Polarization (electrochemistry) ; Nanorod ; Nanotechnology ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Hui Su | Jiaxin Lv | Liansheng Yang | Li Feng | Yongze Liu | Ziwen Du | Liqiu Zhang
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Rapid and selective adsorption of a typical aromatic organophosphorus flame retardant on MIL-101-based metal–organic frameworks Rapid and selective adsorption of TPhP on Cr-MIL-101. Fire retardant ; Adsorption ; Metal-organic framework ; Selective adsorption ; Chemistry ; Metal ; Chemical engineering ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Toxic Organic Pollutants Impact ; Lanthanide and Transition Metal Complexes
Authors: Zhen Wang | Jing Liu | Feng Huang | Yunjie Bi | Shiqi Zhang
Source: Frontiers in Materials (article, 2020-12-08)
Field: Materials Science — Metals and Alloys

Hydrogen Diffusion and Its Effect on Hydrogen Embrittlement in DP Steels With Different Martensite Content The hydrogen diffusion behavior and hydrogen embrittlement susceptibility of dual phase (DP) steels with different martensite content were investigated using the slow strain-rate tensile test and hydrogen permeation measurement. Results showed that a logarithmic relationship was established between the hydrogen embrittlement index ( I HE ) and the effective hydrogen diffusion coefficient ( D eff ). When the martensite content is low, ferrite/martensite interface behaves as the main trap that captures the hydrogen atoms. Also, when the D eff decreases, I HE increases with increasing martensite content. However, when the martensite content reaches approximately 68.3%, the martensite grains start to form a continuous network, D eff reaches a plateau and I HE continues to increase. This is mainly related to the reduction of carbon content in martensite and the length of ferrite/martensite interface, which promotes the diffusion of hydrogen atoms in martensite and the aggregation of hydrogen atoms at the ferrite/martensite interface. Finally, a model describing the mechanism of microstructure-driven hydrogen diffusion with different martensite distribution was established. Martensite ; Materials science ; Hydrogen embrittlement ; Hydrogen ; Ferrite (magnet) ; Metallurgy ; Embrittlement ; Bainite ; Diffusion ; Microstructure ; Composite material ; Thermodynamics ; Chemistry ; Corrosion ; Organic chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Microstructure and Mechanical Properties of Steels