jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Savas Delikanli | Furkan Işık | Farzan Shabani | Hamed Dehghanpour Baruj | Nima Taghipour | Hilmi Volkan Demir
Source: Advanced Optical Materials (article, 2021-02-15)
Field: Materials Science — Materials Chemistry

Ultralow Threshold Optical Gain Enabled by Quantum Rings of Inverted Type‐I CdS/CdSe Core/Crown Nanoplatelets in the Blue Abstract Achieving low‐threshold optical gain for solution‐processed materials is crucial for their real‐life applications and deployment as gain media. However, the realization of low gain threshold in the blue region has shown to be technically an extremely challenging task using colloidal nanocrystals as a result of fast nonradiative Auger rates in smaller nanocrystals. Here, ultralow‐threshold blue amplified spontaneous emission (ASE) (≈2.7 µJ cm −2 ) accompanied with a large net modal gain coefficient of 360 cm −1 in the blue enabled by blue‐emitting (≈455–465 nm) colloidal quantum rings (QRs) of inverted type‐I CdS/CdSe core/crown nanoplatelets (NPLs) is proposed and demonstrated. The synthesized QRs with controlled crown size outperform the best reported ASE thresholds and net modal gain coefficients from the solution‐processed materials by ≈2.5‐ and ≈4‐fold, respectively, in the similar blue spectral window. Utilizing this QR architecture, it is also shown that the ASE peak can be spectrally tuned by controlling the lateral size of the crown and hence quantum confinement in the lateral direction. These outstanding results support the prospects of these solution‐processed QRs made of 2D hetero‐NPLs in the challenging blue region as colloidal gain. Materials science ; Amplified spontaneous emission ; Optoelectronics ; Core (optical fiber) ; Nanocrystal ; Net gain ; Blueshift ; Exciton ; Colloid ; Realization (probability) ; Optics ; Nanotechnology ; Photoluminescence ; Laser ; Physics ; Composite material ; Chemical engineering Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: Alexia Moreno-Peñarrubia | Jorge Teniente | С. А. Кузнецов | Bakhtiyar Orazbayev | Miguel Beruete
Source: Applied Physics Letters (article, 2021-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrathin and high-efficiency Pancharatnam–Berry phase metalens for millimeter waves Applying the Pancharatnam–Berry (PB) principle to half-wave plate (HWP) metasurfaces allows the manipulation of wavefronts along with the conversion of the handedness of circularly polarized incident waves by simply rotating the meta-atoms that compose the metasurface. PB metasurfaces (PBM) working in transmission mode with four or more layers have been demonstrated to reach levels of transmission efficiency near 100% but also have resulted in bulky structures. On the other hand, compact tri-layer ultrathin (λ/8) designs have reached levels near 90% but are more challenging than single- or bi-layer structures from a manufacturing viewpoint. Here, we propose a compact ultrathin (&amp;lt;λ/13) transmissive PBM with only two layers (which significantly simplifies the fabrication process) achieving a transmission efficiency level of around 90%, focusing the wavefront of a circularly polarized incident wave and converting its handedness. The metasurface is composed of identical bi-layered H-shaped unit cells (meta-atoms) whose transmission phases are chosen by introducing different rotation angles to each unit cell according to a lens spatial phase profile. The structure is analytically and numerically studied and experimentally measured, verifying an excellent behavior as an HWP PB metalens at 87 GHz. Geometric phase ; Wavefront ; Optics ; Physics ; Transmission (telecommunications) ; Fabrication ; Rotation (mathematics) ; Phase (matter) ; Optoelectronics ; Millimeter ; Waveplate ; Geometry ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: D. Parajuli | K. Samatha
Source: Bibechana (article, 2021-05-06)
Field: Materials Science — Materials Chemistry

Morphological analysis of Cu substituted Ni\Zn in Ni-Zn ferrites Cu substituted Ni in Ni0.5-xCuxZn0.5Fe2O4 (x = 0, 0.05, 0.1, 0.15 and 0.2) samples and Cu substituted Zn in Ni0.5Zn0.5−xCuxFe2O4 (x = 0, 0.05, 0.1, 0.15 and 0.2) is synthesized using the sol-gel auto-combustion process. Recently, we have carried out their structural analysis using XRD and FTIR and found a cubic spinel structure. In this paper, we have studied their morphological and compositional structure with the help of a Scanning Electron Microscope (SEM) attached with an Energy Dispersive Spectrometer (EDS). The comparative study shows that the grain size of Cu substituted Ni is greater than Cu substituted Zn in Ni-Zn ferrite. These smaller grain-sized ferrites are preferred for many microstructural applications. Depending on the available magnetic field, sintering temperature, and atmosphere, they can have different nucleation, and hence their application mode is different. They can have a critical concentration that can tune their properties. The EDS attached with the SEM confirmed the proper composition of samples. BIBECHANA 18 (2) (2020) 80-86 Zinc ; Materials science ; Metallurgy ; Chemistry ; Nuclear chemistry Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Semiconductor materials and interfaces
Authors: Tomoya Ebi | Abhijit Sen | Raghu Nath Dhital | Yoichi M. A. Yamada | Hiromasa Kaneko
Source: ACS Omega (article, 2021-10-05)
Field: Materials Science — Materials Chemistry

Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts To improve product yields in synthetic reactions, it is important to use appropriate catalysts. In this study, we used machine learning to design catalysts for a reaction system in which both Buchwald-Hartwig-type and Suzuki-Miyaura-type cross-coupling reactions proceed simultaneously. First, using an existing dataset, yield prediction models were constructed with machine learning between experimental conditions, including the substrate and catalyst and the yields of the two products. Seven methods for calculating both the substrate and catalyst descriptors were proposed, and the predictive ability of the yield prediction models was discussed in terms of the descriptors and machine learning methods. Then, the constructed models were used to predict the compound yields for new combinations of substrates and catalysts, and the predictions were experimentally validated with high reproducibility, confirming that machine learning can predict yields from experimental conditions with high accuracy. In addition, to design catalysts that will improve the yields in our dataset, we added datasets collected from scientific papers and designed catalyst ligands. The proposed catalyst candidates were tested in actual synthetic experiments, and the experimental results exceeded the existing yields. Catalysis ; Yield (engineering) ; Substrate (aquarium) ; Machine learning ; Artificial intelligence ; Computer science ; Chemistry ; Process engineering ; Biological system ; Materials science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Konstantin Gubaev | Yuji Ikeda | Ferenc Tasnádi | Jörg Neugebauer | Alexander V. Shapeev | Blazej Grabowski | Fritz Körmann
Source: Physical Review Materials (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from <i>ab initio</i> trained machine-learning potentials High-throughput finite-temperature phase stability and materials property predictions directly from first-principles are extremely resource demanding. Here, the authors combine density functional theory calculations with accurate machine-learning potentials and an active learning scheme to obtain fast, yet highly accurate interatomic potentials. The approach is used to analyze the bcc-$\ensuremath{\omega}$ phase stability and elastic properties of a series of bcc TiZrHfTa${}_{x}$ alloys. The phase stability is evaluated by analyzing the projections of atomic displacements from molecular dynamics trajectories revealing fingerprints of bcc-$\ensuremath{\omega}$ martensitic transformations. Materials science ; Stability (learning theory) ; Interatomic potential ; Structural stability ; Ab initio ; Phase (matter) ; Molecular dynamics ; Density functional theory ; Martensite ; Omega ; Thermodynamics ; Property (philosophy) ; Ab initio quantum chemistry methods ; Statistical physics ; Condensed matter physics ; Chemical physics ; Computational chemistry ; Machine learning ; Computer science ; Physics ; Microstructure ; Molecule ; Quantum mechanics ; Metallurgy ; Structural engineering Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Yun Peng | Qian Li | Jun Luo | Cheng Zhi Huang | Jun Zhou
Source: Analytical Chemistry (article, 2021-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10<sup>-10</sup> mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing. Nanoprobe ; Plasmon ; Chemistry ; Scattering ; Surface plasmon resonance ; Nanotechnology ; Light scattering ; Silver nanoparticle ; Plasmonic nanoparticles ; Nanoparticle ; Design for manufacturability ; Optoelectronics ; Optics ; Materials science ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced Biosensing Techniques and Applications
Authors: Jeong Mok Oh | P.L. Narayana | Jae‐Keun Hong | Jong‐Taek Yeom | N.S. Reddy | Namhyun Kang | Chan Hee Park
Source: Journal of Alloys and Compounds (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Property optimization of TRIP Ti alloys based on artificial neural network Ultimate tensile strength ; Alloy ; Materials science ; Ductility (Earth science) ; Artificial neural network ; Elongation ; Melting point ; Plasticity ; Metallurgy ; Composite material ; Machine learning ; Computer science ; Creep Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: W. Gerrard | Calvin Yiu | Craig P. Butts
Source: Magnetic Resonance in Chemistry (article, 2021-08-18)
Field: Chemistry — Spectroscopy

Prediction of <sup>15</sup> N chemical shifts by machine learning We demonstrate the potential for machine learning systems to predict three-dimensional (3D)-relevant NMR properties beyond traditional <sup>1</sup> H- and <sup>13</sup> C-based data, with comparable accuracy to density functional theory (DFT) (but orders of magnitude faster). Predictions of DFT-calculated <sup>15</sup> N chemical shifts for 3D molecular structures can be achieved using a machine learning system-IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei), with an accuracy of 6.12-ppm mean absolute error (∼1% of the δ<sup>15</sup> N chemical shift range) and an error of less than 20 ppm for 95% of the chemical shifts. It provides less accurate raw predictions of experimental chemical shifts, due to the limited size and chemical space diversity of the training dataset used in its creation, coupled with the limitations of the underlying DFT methodology in reproducing experiment. Chemical shift ; Chemistry ; Chemical space ; Density functional theory ; Range (aeronautics) ; Scalar (mathematics) ; Chemical species ; Machine learning ; Artificial intelligence ; Biological system ; Algorithm ; Computational chemistry ; Computer science ; Physical chemistry ; Mathematics Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Machine Learning in Materials Science
Authors: Huimin Chen | Zhongwen Shang | Wencong Lu | Minjie Li | Fuping Tan
Source: Advanced Engineering Materials (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning Low‐melting alloys (LMAs) have an extensive application prospect due to their extremely low melting points for further research of other properties. However, it is difficult to design new multiple alloys with required melting point based on experiments in vast chemical space. Herein, a property‐driven stepwise design strategy for multiple alloys design based on complete machine learning process is developed. The R‐X‐S integrated model based on Ridge Regression (RR), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) performs well in melting point prediction with the root mean squared error (RMSE) and correlation coefficient ( R ) on the validation set of 4.578 and 0.988, respectively. After model construction, the stepwise strategy is used to design potential LMAs with smaller estimation error according to the prediction error function combining variance and bias. The candidates with melting point of 90 °C provide the possibility for solder applications with melting points below 100 °C, and the low‐cost candidates with melting point of 16 °C may be used to replace the expensive 75Ga–25In alloy. The stepwise strategy with different step length can improve the search efficiency and map the relationship between LMAs compositions and melting points, which may also be applied to explore other functional materials with high performance. Mean squared error ; Melting point ; Stepwise regression ; Support vector machine ; Materials science ; Kriging ; Mathematical optimization ; Computer science ; Algorithm ; Machine learning ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Amir Kordijazi | Swaroop Behera | Dhrumil Patel | Pradeep K. Rohatgi | Michael Nosonovsky
Source: Langmuir (article, 2021-03-17)
Field: Materials Science — Surfaces, Coatings and Films

Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling Wetting of multiphase alloys and their composites depends on multiple parameters, and these relationships are difficult to predict from first principles only. We study correlations between the composition, surface finish, and microstructure of Al-Si alloys (Si content 7-50%) and Al metal matrix composites (MMCs) with graphite (Gr), NiAl<sub>3</sub>, and SiC and the water contact angle (CA) experimentally, theoretically, and with machine learning (ML) techniques. Their surface properties were modified by mechanical abrasion, etching, and addition of alloying elements. An ML approach was developed to investigate correlations between the predictor variables (properties of the materials) and the CA. Theoretical models of wetting of rough surfaces (Wenzel, Cassie-Baxter, and their modifications) do not fully capture the CA, while ML models follow the experimental values. A full factorial design is utilized with combinations of all levels of the predictor factors (grit size, silicon percentage, droplet size, elapsed time, etching, reinforcing particles). To map the predictor variables to the response variables, 409 experimental data points were applied to train and test various supervised ML models, namely, regression, artificial neural network (ANN), chi-square automatic interaction detection (CHAID), extreme gradient boosting (XGBoost), and random forest. The correlations between the most significant factors and CA are explored through visualization techniques. The most accurately trained model shows a strong positive linear correlation (<i>r</i> > 0.9) between predicted and observed CA values in the test set, indicating the robustness of the model. The experimental measurements and artificial intelligence results demonstrate that CA increases following mechanically abrading the surface, etching, and adding Gr to the surface. The ML methods are promising to predict wetting properties and to provide a deeper understanding of the physical phenomena associated with the wettability of metallic alloys and their metal matrix composites. Wetting ; Materials science ; Contact angle ; Surface roughness ; Linear regression ; Composite material ; Artificial neural network ; Silicon carbide ; Surface finish ; Machine learning ; Computer science Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Graphene research and applications
Authors: Sami Kaappa | Casper Larsen | Karsten W. Jacobsen
Source: Physical Review Letters (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine-learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75. Maxima and minima ; Bayesian optimization ; Interpolation (computer graphics) ; Global optimization ; Adsorption ; Gaussian ; Materials science ; Range (aeronautics) ; Energy minimization ; Computer science ; Energy (signal processing) ; Statistical physics ; Chemical physics ; Algorithm ; Physics ; Machine learning ; Artificial intelligence ; Chemistry ; Physical chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yunwei Gui | Quanan Li | Kaige Zhu | Yibei Xue
Source: Materials Today Communications (article, 2021-03-27)
Field: Materials Science — Biomaterials

A combined machine learning and EBSD approach for the prediction of {10-12} twin nucleation in an Mg-RE alloy Nucleation ; Electron backscatter diffraction ; Materials science ; Microstructure ; Artificial intelligence ; Crystal twinning ; Machine learning ; Alloy ; Metallurgy ; Computer science ; Thermodynamics ; Physics Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Natasha Dropka | Klaus Böttcher | Martin Holeňa
Source: Crystals (article, 2021-10-09)
Field: Materials Science — Materials Chemistry

Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques The aim of this study was to assess the ability of the various data mining and supervised machine learning techniques: correlation analysis, k-means clustering, principal component analysis and decision trees (regression and classification), to derive, optimize and understand the factors influencing VGF-GaAs growth. Training data were generated by Computational Fluid Dynamics (CFD) simulations and consisted of 130 datasets with 6 inputs (growth rate and power of 5 heaters) and 5 outputs (interface position and deflection, and temperatures at various positions in GaAs). Data mining results confirmed a good dispersion of the training data without the feasibility of a dimensionality reduction. Data clustering was observed in relation to the position of the crystallization front relative to the side heaters. Based on the statistical performance criteria and training results, decision trees identified the most decisive inputs and their ranges for a favorable interface shape and to keep GaAs temperature beyond limits for heavy arsenic evaporation. Decision trees are a recommendable machine learning technique with short training times and acceptable predictive accuracy based on small volume of CFD training data, capable of providing guidelines for understanding the crystal growth process, which is a prerequisite for the growth of low-cost, high-quality bulk crystals. Cluster analysis ; Decision tree ; Principal component analysis ; Machine learning ; Dimensionality reduction ; Computer science ; Artificial intelligence ; Data mining ; Crystal (programming language) Machine Learning in Materials Science ; Software Engineering Research
Authors: Pieter Plehiers | István Lengyel | David West | Guy Marin | Christian V. Stevens | Kevin M. Van Geem
Source: Chemical Engineering Journal (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

Fast estimation of standard enthalpy of formation with chemical accuracy by artificial neural network correction of low-level-of-theory ab initio calculations Artificial neural network ; Interatomic potential ; Enthalpy ; Computation ; Ab initio ; Spurious relationship ; Standard enthalpy of formation ; Convolution (computer science) ; Function (biology) ; Density functional theory ; Standard deviation ; Chemistry ; Thermodynamics ; Computer science ; Biological system ; Algorithm ; Molecular dynamics ; Computational chemistry ; Mathematics ; Physical chemistry ; Physics ; Artificial intelligence ; Statistics ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure