jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Razan O. M. Aboljadayel | C. J. Kinane | C. A. F. Vaz | D. M. Love | Robert S. Weatherup | Philipp Braeuninger‐Weimer | M. -B. Martin | A. Ionescu | A. J. Caruana | T. R. Charlton | J. Llandro | Pedro M. S. Monteiro | C. H. W. Barnes | S. Hofmann | S. Langridge
Source: arXiv (Cornell University) (preprint, 2021-01-25)
Field: Materials Science — Materials Chemistry

Determining the Proximity Effect Induced Magnetic Moment in Graphene by Polarized Neutron Reflectivity and X-ray Magnetic Circular Dichroism We report the magnitude of the induced magnetic moment in CVD-grown epitaxial and rotated-domain graphene in proximity with a ferromagnetic Ni film, using polarized neutron reflectivity (PNR) and X-ray magnetic circular dichroism (XMCD). The XMCD spectra at the C K-edge confirms the presence of a magnetic signal in the graphene layer and the sum rules give a magnetic moment of up to $\sim\,0.47\,μ$_B/C atom induced in the graphene layer. For a more precise estimation, we conducted PNR measurements. The PNR results indicate an induced magnetic moment of $\sim$ 0.53 $μ$_B/C atom at 10 K for rotated graphene and $\sim$ 0.38 $μ$_B/C atom at 10 K for epitaxial graphene. Additional PNR measurements on graphene grown on a non-magnetic Ni_9Mo_1 substrate, where no magnetic moment in graphene is measured, suggest that the origin of the induced magnetic moment is due to the opening of the graphene's Dirac cone as a result of the strong C pz-3d hybridization. X-ray magnetic circular dichroism ; Magnetic circular dichroism ; Circular dichroism ; Magnetic moment ; Circular polarization ; Neutron ; Condensed matter physics ; Graphene ; Moment (physics) ; Reflectivity ; Physics ; Optics ; Materials science ; Chemistry ; Crystallography ; Spectral line ; Nuclear physics ; Quantum mechanics ; Microstrip Graphene research and applications ; Characterization and Applications of Magnetic Nanoparticles ; Advanced NMR Techniques and Applications
Authors: Yuanfei Xue | Jianing Wang | Wenxin Hu | Jun Zheng | Yongle Li | Xiaoliang Pan | Yan Mo | Yihan Shao | Lu Wang | Ye Mei
Source:  (preprint, 2021-08-02)
Field: Chemistry — Spectroscopy

Affordable Ab Initio Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential Path integral molecular dynamics (PIMD) is becoming a routinely applied method for the incorporation of the nuclear quantum effect in computer simulations. However, direct PIMD simulations at an ab initio level of theory are formidably expensive. Using the protonated 1,8-bis(dimethylamino)naphthalene molecule as an example, we show in this work that the computational expense for the intra-molecular proton transfer between the two nitrogen atoms can be remarkably reduced by implementing the idea of reference-potential methods. The simulation time can be easily extended to a scale of nanosecond while maintaining the accuracy on an ab initio level of theory for thermodynamic properties. In addition, the post-processing can be carried out in parallel on massive computer nodes. A 545-fold reduction in the total CPU time can be achieved in this way as compared to a direct PIMD simulation at the same ab initio level of theory. Ab initio ; Molecular dynamics ; Path integral formulation ; Proton ; Work (physics) ; Computational chemistry ; Statistical physics ; Chemistry ; Physics ; Computer science ; Quantum ; Thermodynamics ; Quantum mechanics Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: A.R. Paul | Ravani, Bahram
Source: DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

Determination of Aggregate Elastic Properties of Powder-Beds in Additive Manufacturing Using Convolutional Neural Networks The most popular strategy for the estimation of effective elastic properties of powder-beds in Additively Manufactured structures (AM structures) is through either the Finite Element Method (FEM) or the Discrete Element Method (DEM). Both of these techniques, however, are computationally expensive for practical applications. This paper presents a novel Convolutional Neural Network (CNN) regression approach to estimate the effective elastic properties of powder-beds in AM structures. In this approach, the time-consuming DEM is used for CNN training purposes and not at run time. The DEM is used to model the interactions of powder particles and to evaluate the macro-level continuum-mechanical state variables (volume average of stress and strain). For the Neural Network training purposes, the DEM code creates a dataset, including hundreds of AM structures with their corresponding mechanical properties. The approach utilizes methods from deep learning to train a CNN capable of reducing the computational time needed to predict the effective elastic properties of the aggregate. The saving in computational time could reach 99.9995% compared to DEM, and on average, the difference in predicted effective elastic properties between the DEM code and trained CNN is less than 4%. The resulting sub-second level computational time can be considered as a step towards the development of a near real-time process control system capable of predicting the effective elastic properties of the aggregate at any given stage of the manufacturing process. Computer science ; Machining ; Process (computing) ; Voxel ; Machine learning ; Rapid prototyping ; Artificial intelligence ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Additive Manufacturing Materials and Processes
Authors: Leila Khalili
Source: NCSU Libraries Repository (North Carolina State University Libraries) (article, 2021-06-09)
Field: Materials Science — Materials Chemistry

Exploring the Five Dimensional Crystallography-Property Relationships of Grain Boundaries. KHALILI, LEILA.Exploring the Five Dimensional Crystallography-Property Relationships of Grain Boundaries.(Under the direction of Srikanth Patala.)Grain boundaries (GBs) profoundly affect the physical, chemical, and mechanical properties of polycrystalline solids.The GB properties are known to be anisotropic and can vary significantly with misorientation and boundary-plane inclination.Despite its recognized importance, the experimental and theoretical literature on GB crystallography-property relationships and the anisotropy of these properties has not been systematically quantified.Some of the challenges are the complex topology of the GB space and the time-consuming nature of both experimental and computational investigations in mapping the crystallography-property relationships.Experimental characterization on individual grain boundaries is difficult, and simulation methods are limited due to computational costs.In this thesis, we developed several tools to facilitate high-throughput simulations of grain boundaries using atomistic simulations.First, we present a hybrid Monte Carlo Molecular Dynamics (MC-MD) algorithm to determine the minimum energy structures of grain boundaries.This MC-MD technique is based on atom removal or insertion in the grain boundary region, followed by an annealing process.This algorithm makes the sampling of the microscopic degrees of freedom more efficient and decreases the computational costs required to find minimum energy structures.This framework shows that this algorithm can be applied on low symmetry crystal structures to find the minimum energy structure and the meta-stable phases.This algorithm is simple and flexible enough to facilitate the simulation of GBs in low-symmetry crystal systems and those with mixed crystallography.In the second part of this thesis, we develop an algorithm to determine the Coincidence Site Lattices (CSLs) or near-CSLs and their corresponding -misorientations for any kind of Bravais lattices.This algorithm is based on finding all the unique sub-lattices and eliminating the ones related to each other by the symmetry operations of the underlying crystal.If two lattices create a near-CSL the transformation will be a rotation followed by a deformation which makes the mapping more complicated.The novelty of current work is applying a Point Pattern Matching technique to find the transformation to map the sub-lattices pair.This algorithm is the most straightforward and more general available algorithm for finding CSL/near-CSL in any Bravais lattices. Property (philosophy) ; Crystallography ; Chemistry ; Epistemology ; Philosophy Microstructure and mechanical properties ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Aditya Pandya
Source:  (preprint, 2021-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fibre Optic SERS Probes For Remote Sensing Surface Enhanced Raman Spectroscopy (SERS) enhances spontaneous Raman spectroscopy by the virtue of plasmon resonance of nanoparticles. Clinical application of SERS is challenging as nanoparticles remain in the body for long periods of time and a full toxicity analysis has yet to be extensively studied. In this study, Nanosphere lithography (NSL) was used to create optical fibers with nanoparticle enhanced tips for remote sensing using SERS. A custom designed RS collection setup was created for optimal collection of spectra from the optical fibers. It was found that an optical fiber with 0.5 numerical aperture (NA) allowed for better detection of Raman peaks while mitigating the fluorescence background of the optical fiber without any optical filters. Such a sensing platform can potentially be used to temporarily introduce nanoparticles into a sensing environment as it allows retracting the nanoparticles along with the tip. Nanoporous SERS platform has been fabricated using nanoporous silica glass with 7 nm and 17 nm pore diameters. An inexpensive fabrication approach of sputter deposition of Au layers was employed on prefabricated nanoporous silica glasses. 7 nm pore glasses provided larger enhancement than the glasses with 17 nm pores. A gold layer thickness of 25 nm was observed to produce largest enhancements. Nanoporous SERS substrates allow a larger effective SERS area compared to NSL based fabrication substrates and such nanoporous structures can be potentially fabricated on optical fiber tips for remote sensing. Finite Element Modeling (FEM) method was implemented for simulating single nanoparticles, an infinite periodic array of nanoparticles and nanoporous films using COMSOL Multiphysics software package. The extinction spectra obtained theoretically were found to match the experimental results for single nanoparticles. The maximum enhancement for the periodic array was two orders of magnitude larger than single particles while the integrated (average) enhancement was only two and a half times larger. Nanoporous films were also modelled using the FEM technique. Preliminary clinical data were collected from excised breast tissues for evaluating RS as a tool for cancer diagnostics. Spectral peaks from healthy tissues were found to be prominent than cancerous tissues and further experiments are needed to create a multivariate classification model for diagnostics. Nanoporous ; Materials science ; Raman spectroscopy ; Fabrication ; Optical fiber ; Nanoparticle ; Nanotechnology ; Surface plasmon resonance ; Fiber ; Plasmon ; Colloidal gold ; Nanocomposite ; Optoelectronics ; Optics ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Photoacoustic and Ultrasonic Imaging
Authors: Elham Alehosseini | Seid Mahdi Jafari | Hoda Shahiri Tabarestani
Source: Food Science and Technology (article, 2021-07-01)
Field: Materials Science — Biomaterials

Evaluating the performance of artificial neural networks (ANNs) for predicting the physical, rheological, and colorimetric properties of chitosan nanoparticles (CSNPs) Poly dispersity index (PDI) 2. Power law model 1. Response surface methodology (RSM) 2. Central Composite Design (CCD) 3. Face Center 4. Chitosan ; Rheology ; Artificial neural network ; Nanoparticle ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Artificial intelligence ; Composite material ; Engineering Nanocomposite Films for Food Packaging ; Polysaccharides Composition and Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Hao Zhang | Xinyi Wang | Hai‐Bin Yu | Jack F. Douglas
Source: arXiv (Cornell University) (preprint, 2021-01-27)
Field: Materials Science — Materials Chemistry

Dynamic Heterogeneity, Cooperative Motion, and Johari-Goldstein $β$-Relaxation in a Metallic Glass-Forming Material Exhibiting a Fragile to Strong Transition We investigate the Johari-Goldstein (JG) $β$-relaxation process in a model metallic glass-forming (GF) material (Al90Sm10), previously studied extensively by both frequency-dependent mechanical measurements and simulation studies devoted to equilibrium properties, by molecular dynamics simulations based on validated and optimized interatomic potentials with the primary aim of better understanding the nature of this universal relaxation process from a dynamic heterogeneity (DH) perspective. The present relatively low temperature and long-time simulations reveal a direct correspondence between the JG $β$-relaxation time $τ_{JG}$ and the lifetime of the mobile particle clusters $τ_M$, defined as in previous DH studies, a relationship dual to the corresponding previously observed relationship between the $α$-relaxation time $τ_α$ and the lifetime of immobile particle clusters $τ_{IM}$. Moreover, we find that the average diffusion coefficient D nearly coincides with D$_{Al}$, of the smaller atomic species (Al), and that the hopping time associated with D coincides with $τ_{JG}$ to within numerical uncertainty, both trends being in accord with experimental studies. This indicates that the JG $α$-relaxation is dominated by the smaller atomic species and the observation of a direct relation between this relaxation process and rate of molecular diffusion in GF materials at low temperatures where the JG $β$-relaxation becomes the prevalent mode of structural relaxation. As an unanticipated aspect of our study, we find that Al90Sm10 exhibits fragile-to-strong (FS) glass-formation, as found in many other metallic GF liquids, but this fact does not greatly alter the geometrical nature of DH in this material and the relation of DH to dynamical properties. Relaxation (psychology) ; Glass transition ; Diffusion ; Molecular dynamics ; Materials science ; Thermodynamics ; Atomic diffusion ; Chemical physics ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry ; Polymer ; Composite material Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Glass properties and applications
Authors: Andrew Wagner | J. C. Nugent | Kumar Virwani
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Materials and process discovery by correlated STEM imaging and spectroscopy with electrical testing An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Materials science ; Process (computing) ; Spectroscopy ; Computer science ; Physics ; Astronomy Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Machine Learning in Materials Science
Authors: Amany M. Mohamed | Shahzad Latif | Ahmed Alwan
Source: Journal of University of Shanghai for Science and Technology (article, 2021-06-26)
Field: Chemistry — Analytical Chemistry

Support vector regression is an improvement for principle component analysis The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size. Principal component regression ; Support vector machine ; Sigmoid function ; Principal component analysis ; Mathematics ; Polynomial kernel ; Mean squared error ; Radial basis function kernel ; Kernel (algebra) ; Kernel method ; Kernel principal component analysis ; Polynomial regression ; Regression analysis ; Artificial intelligence ; Statistics ; Pattern recognition (psychology) ; Computer science ; Artificial neural network Spectroscopy and Chemometric Analyses ; Face and Expression Recognition ; Neural Networks and Applications
Authors: Mahdi Ghazal | Michel Daher Mansour | Corentin Scholaert | Thomas Dargent | Yannick Coffinier | Sébastien Pecqueur | Fabien Alibart
Source: Advanced Electronic Materials (preprint, 2021-11-10)
Field: Materials Science — Polymers and Plastics

Bio‐Inspired Adaptive Sensing through Electropolymerization of Organic Electrochemical Transistors Abstract Organic electrochemical transistors are considered today as a key technology to interact with a biological medium through their intrinsic ionic‐electronic coupling. In this paper, the authors show how this coupling can be finely tuned (in operando) post‐microfabrication via the electropolymerization technique. This strategy exploits the concept of adaptive sensing where both transconductance and impedance are tunable and can be modified on‐demand to match different sensing requirements. Material investigation through Raman spectroscopy, atomic force microscopy, and scanning electron microscopy reveals that electropolymerization can lead to a fine control of poly(3,4‐ethylenedioxythiophene) (PEDOT) microdomains organization, which directly affects the iono‐electronic properties of organic electrochemical transistors (OECTs). They further highlight how volumetric capacitance and effective mobility of PEDOT:polystyrene sulfonate influence distinctively the transconductance and impedance of OECTs. This approach shows to improve the transconductance by 150% while reducing their variability by 60% in comparison with standard spin‐coated OECTs. Finally, they show how the technique can influence voltage spike rate hardware classification with direct interest in bio‐signals sorting applications. PEDOT:PSS ; Transconductance ; Materials science ; Microfabrication ; Nanotechnology ; Capacitance ; Transistor ; Optoelectronics ; Dielectric spectroscopy ; Raman spectroscopy ; Voltage ; Electrochemistry ; Electrode ; Electrical engineering ; Chemistry ; Layer (electronics) Conducting polymers and applications ; Analytical Chemistry and Sensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Jonny Proppe | Johannes Kircher
Source: ChemRxiv (preprint, 2021-02-25)
Field: Materials Science — Materials Chemistry

Transforming Predictions into Testable Hypotheses: The Case of Polar Organic Reactivity Herbert Mayr’s research on reactivity scales tells a success story of how polar organic synthesis can be rationalized by a simple empirical relationship. In this work, we propose an extension to Mayr’s reactivity approach that is rooted in uncertainty quantification (UQ). It transforms the unique values of reactivity parameters (sN, N, E) into value distributions. Through uncertainty propagation, these distributions can be exploited to quantify the uncertainty of bimolecular rate constants. Our UQ-based extension serves three purposes. First, predictions of polar organic reactivity can be transformed into testable hypotheses, which increases the overall reliability of the method and guides the exploration of new research directions. Second, it is also possible to quantify the discriminability of two competing reactions, which is particularly important if subtle reactivity differences matter. Third, since rate constant uncertainty can also be quantified for reactions that have yet to be observed, new opportunities arise for benchmarking computational chemistry methods (benchmarking under uncertainty). We demonstrate the functionality and performance of the UQ-extended reactivity approach at the example of the 2001/12 reference data set released by Mayr and co-workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As a by-product of the new approach, we obtain revised reactivity parameters for the electrophiles and the nucleophiles of the reference set. Reactivity (psychology) ; Electrophile ; Nucleophile ; Benchmarking ; Set (abstract data type) ; Polar ; Chemistry ; Computer science ; Computational chemistry ; Organic chemistry ; Physics ; Economics ; Programming language Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions