jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fengyuan Zhang | Kerisha Williams | D.N. Edwards | Aaron B. Naden | Yulian Yao | Sabine M. Neumayer | Amit Kumar | Brian J. Rodriguez | Nazanin Bassiri‐Gharb
Source: Small Methods (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect‐Rich PZT Films Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample-dependent electrostatic and chemo-electromechanical changes. Literature reports have often concentrated on the evaluation of the Off-field piezoresponse, compared to On-field piezoresponse, based on the latter's increased sensitivity to non-ferroelectric contributions. Using machine learning approaches incorporating both Off- and On-field piezoresponse response as well as Off-field resonance frequency to maximize information, switching piezoresponse in a defect-rich Pb(Zr,Ti)O<sub>3</sub> thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during On-field measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non-ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo-electromechanical response. Piezoresponse force microscopy ; Ferroelectricity ; Microscale chemistry ; Materials science ; Nanoscopic scale ; Superposition principle ; Nanotechnology ; Polarization (electrochemistry) ; Field (mathematics) ; Ferroelectric polymers ; Scanning probe microscopy ; Optoelectronics ; Physics ; Chemistry ; Dielectric Ferroelectric and Piezoelectric Materials ; Force Microscopy Techniques and Applications ; Acoustic Wave Resonator Technologies
Authors: A. Hamedani | Jesper Byggmästar | Flyura Djurabekova | Ghasem Alahyarizadeh | Reza Ghaderi | A. Minuchehr | K. Nordlund
Source: Physical Review Materials (article, 2021-11-05)
Field: Materials Science — Materials Chemistry

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential Characterization of the primary damage is the starting point in describing and predicting the irradiation-induced damage in materials. So far, primary damage has been described by traditional interatomic potentials in molecular dynamics simulations. Here, we employ a Gaussian approximation machine-learning potential (GAP) to study the primary damage in silicon with close to ab initio precision level. We report detailed analysis of cascade simulations derived from our modified Si GAP, which has already shown its reliability for simulating radiation damage in silicon. Major differences in the picture of primary damage predicted by machine-learning potential compared to classical potentials are atomic mixing, defect state at the heat spike phase, defect clustering, and recrystallization rate. Atomic mixing is higher in the GAP description by a factor of two. GAP shows considerably higher number of coordination defects at the heat spike phase and the number of displaced atoms is noticeably greater in GAP. Surviving defects are dominantly isolated defects and small clusters, rather than large clusters, in GAP's prediction. The pattern by which the cascades are evolving is also different in GAP, having more expanded form compared to the locally compact form with classical potentials. Moreover, recovery of the generated defects at the heat spike phase take places with higher efficiency in GAP. We also provide the attributes of the new defect cluster that we had introduced in our previous study. A cluster of four defects, in which a central vacancy is surrounded by three split interstitials, where the surrounding atoms are all 4-folded bonded. The cluster shows higher occurrence in simulations with the GAP potential. The formation energy of the defect is 5.57 eV and it remains stable up to 700 K, at least for 30 ps. The Arrhenius equation predicts the lifetime of the cluster to be $0.0725\phantom{\rule{0.16em}{0ex}}\ensuremath{\mu}\mathrm{s}$ at room temperature. Materials science ; Radiation damage ; Silicon ; Crystallographic defect ; Cluster (spacecraft) ; Molecular dynamics ; Irradiation ; Molecular physics ; Interatomic potential ; Cluster analysis ; Cascade ; Ab initio ; Chemical physics ; Condensed matter physics ; Optoelectronics ; Computational chemistry ; Computer science ; Machine learning ; Physics ; Nuclear physics Machine Learning in Materials Science ; Silicon and Solar Cell Technologies ; Semiconductor materials and devices
Authors: Piotr Tomaszewski | Shun Yu | Markus Borg | Jerk Rönnols
Source:  (preprint, 2021-12-02)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Analysis of Small Angle X-ray Scattering Small angle X-ray scattering (SAXS) is extensively used in materials science as a way of examining nanostructures. The analysis of experimental SAXS data involves mapping a rather simple data format to a vast amount of structural models. Despite various scientific computing tools to assist the model selection, the activity heavily relies on the SAXS analysts' experience, which is recognized as an efficiency bottleneck by the community. To cope with this decision-making problem, we develop and evaluate the open-source, Machine Learning-based tool SCAN (SCattering Ai aNalysis) to provide recommendations on model selection. SCAN exploits multiple machine learning algorithms and uses models and a simulation tool implemented in the SasView package for generating a well defined set of datasets. Our evaluation shows that SCAN delivers an overall accuracy of 95%-97%. The XGBoost Classifier has been identified as the most accurate method with a good balance between accuracy and training time. With eleven predefined structural models for common nanostructures and an easy draw-drop function to expand the number and types training models, SCAN can accelerate the SAXS data analysis workflow. Small-angle X-ray scattering ; Scattering ; Computer science ; Small-angle scattering ; Materials science ; Optics ; Physics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Radiomics and Machine Learning in Medical Imaging
Authors: Lanxin Ma | Kaixiang Hu | Chengchao Wang | Jia‐Yue Yang | Linhua Liu
Source: Nanomaterials (article, 2021-12-08)
Field: Chemistry — Inorganic Chemistry

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles' structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials. Structural coloration ; Artificial neural network ; Hue ; Monte Carlo method ; Nanoparticle ; Materials science ; Brightness ; Computer science ; Colloidal gold ; Inverse ; Biological system ; Artificial intelligence ; Optics ; Nanotechnology ; Optoelectronics ; Mathematics ; Physics ; Geometry Pigment Synthesis and Properties ; Color Science and Applications ; Nonlinear Optical Materials Studies
Authors: Pavel V Kolesnichenko | Qianhui Zhang | Changxi Zheng | Michael S Fuhrer | Jeffrey A Davis
Source: Machine Learning Science and Technology (article, 2021-01-05)
Field: Materials Science — Materials Chemistry

Multidimensional analysis of excitonic spectra of monolayers of tungsten disulphide: toward computer-aided identification of structural and environmental perturbations of 2D materials Abstract Despite 2D materials holding great promise for a broad range of applications, the proliferation of devices and their fulfillment of real-life demands are still far from being realized. Experimentally obtainable samples commonly experience a wide range of perturbations (ripples and wrinkles, point and line defects, grain boundaries, strain field, doping, water intercalation, oxidation, edge reconstructions) significantly deviating the properties from idealistic models. These perturbations, in general, can be entangled or occur in groups with each group forming a complex perturbation making the interpretations of observable physical properties and the disentanglement of simultaneously acting effects a highly non-trivial task even for an experienced researcher. Here we generalise statistical correlation analysis of excitonic spectra of monolayer WS 2 , acquired by hyperspectral absorption and photoluminescence imaging, to a multidimensional case, and examine multidimensional correlations via unsupervised machine learning algorithms. Using principal component analysis we are able to identify four dominant components that are correlated with tensile strain, disorder induced by adsorption or intercalation of environmental molecules, multi-layer regions and charge doping, respectively. This approach has the potential to determine the local environment of WS 2 monolayers or other 2D materials from simple optical measurements, and paves the way toward advanced, machine-aided, characterization of monolayer matter. Monolayer ; Principal component analysis ; Spectral line ; Observable ; Photoluminescence ; Hyperspectral imaging ; Tungsten ; Exciton ; Grain boundary 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Chemical and Physical Properties of Materials
Authors: Javier Robledo Moreno | Johannes Flick | Antoine Georges
Source: Physical Review Materials (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Machine learning band gaps from the electron density The accurate estimation of band gaps of solid-state materials is of great relevance for modern optoelectronic, electronic, and photovoltaic applications. However, the precise $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ computation of accurate band gaps is a resource intensive task. In this article, inspired by the Hohenberg-Kohn theorem of density-functional theory, the authors demonstrate the possibility of explicitly parametrizing the mapping between the electron density in the unit cell and the corresponding experimental band gap in real materials using deep neural networks. The proposed data-driven approach achieves accuracies comparable to state of the art $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ approaches, at a much lower computational cost. Computation ; Band gap ; Artificial neural network ; Relevance (law) ; Photovoltaic system ; Deep learning ; Out-of-band management Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: Yinglei Zhao | Jinnuo Zhang | Mostafa Gouda | Chenghao Zhang | Lei Lin | Pengcheng Nie | Hongbao Ye | Wei Huang | Yunxiang Ye | Chengquan Zhou | Yong He
Source: Journal of Hazardous Materials (article, 2021-12-31)
Field: Chemistry — Analytical Chemistry

Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum Raman spectroscopy ; Cadmium ; Phytoremediation ; Chemistry ; Chelation ; Metal ; Metal ions in aqueous solution ; Fluorescence ; Raman scattering ; Environmental chemistry ; Analytical Chemistry (journal) ; Heavy metals ; Physics ; Inorganic chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Heavy Metals in Plants
Authors: Yapai Song | Mengyang Lu | Gabrielle A. Mandl | Yao Xie | Guotao Sun | Jiabo Chen | Xin Liu | John A. Capobianco | Lining Sun
Source: Angewandte Chemie (article, 2021-09-03)
Field: Materials Science — Materials Chemistry

Energy Migration Control of Multimodal Emissions in an Er<sup>3+</sup>‐Doped Nanostructure for Information Encryption and Deep‐Learning Decoding Abstract Modulating the emission wavelengths of materials has always been a primary focus of fluorescence technology. Nanocrystals (NCs) doped with lanthanide ions with rich energy levels can produce a variety of emissions at different excitation wavelengths. However, the control of multimodal emissions of these ions has remained a challenge. Herein, we present a new composition of Er 3+ ‐based lanthanide NCs with color‐switchable output under irradiation with 980, 808, or 1535 nm light for information security. The variation of excitation wavelengths changes the intensity ratio of visible (Vis)/near‐infrared (NIR‐II) emissions. Taking advantage of the Vis/NIR‐II multimodal emissions of NCs and deep learning, we successfully demonstrated the storage and decoding of visible light information in pork tissue. Doping ; Ion ; Fluorescence ; Lanthanide ; Excitation ; Nanocrystal ; Decoding methods ; Wavelength ; Photochemistry ; Materials science ; Nanostructure ; Near-infrared spectroscopy ; Photon upconversion ; Chemistry ; Optoelectronics ; Nanotechnology ; Computer science ; Telecommunications ; Optics ; Physics ; Organic chemistry Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Yihuang Xiong | Weinan Chen | Wenbo Guo | Hua Wei | Ismaïla Dabo
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-driven analysis of the electronic-structure factors controlling the work functions of perovskite oxides Tuning the work functions of materials is of practical interest for maximizing the performance of microelectronic and (photo)electrochemical devices, as the efficiency of these systems depends on the ability to control electronic levels at surfaces and across interfaces. Perovskites are promising compounds to achieve such control. In this work, we examine the work functions of more than 1000 perovskite oxide surfaces (ABO<sub>3</sub>) using data-driven (machine-learning) analysis and identify the factors that determine their magnitude. While the work functions of the BO<sub>2</sub>-terminated surfaces are sensitive to the energy of the hybridized oxygen p bands, the work functions of the AO-terminated surfaces exhibit a much less trivial dependence with respect to the filling of the d bands of the B-site atom and of its electronic affinity. This study shows the utility of interpretable data-driven models in analyzing the work functions of cubic perovskites from a limited number of electronic-structure descriptors. Microelectronics ; Perovskite (structure) ; Work (physics) ; Materials science ; Electronic structure ; Simple (philosophy) ; Electronics ; Electrochemistry ; Computer science ; Nanotechnology ; Electronic engineering ; Electrical engineering ; Chemistry ; Computational chemistry ; Mechanical engineering ; Chemical engineering ; Engineering ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Transition Metal Oxide Nanomaterials
Authors: Arunkumar Munimathan | Anusha Rajendran | S. Gunasri | M. Kowsalya | C.K. Krithika
Source: Materials Today Proceedings (review, 2021-03-01)
Field: Chemistry — Analytical Chemistry

WITHDRAWN: Non-destructive fruit maturity detection methodology - A review Maturity (psychological) ; Ripening ; Identification (biology) ; Shelf life ; Selection (genetic algorithm) ; Quality (philosophy) ; Computer science ; Agricultural engineering ; Engineering ; Horticulture ; Artificial intelligence ; Biology ; Botany ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Adou Emmanuel Ehounou | Denis Cornet | Lucienne Desfontaines | Carine Marie-Magdeleine | Erick Malédon | Elie Nudol | Grégory Beurier | Lauriane Rouan | Pierre Brat | Mathieu Léchaudel | Camille Noûs | Assanvo Simon-Pierre N’guetta | Amani Michel Kouakou | Gemma Arnau
Source: Journal of Near Infrared Spectroscopy (article, 2021-04-13)
Field: Chemistry — Analytical Chemistry

Predicting quality, texture and chemical content of yam (<i>Dioscorea alata</i> L.) tubers using near infrared spectroscopy Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (&lt;25%), high sugar (&gt;4%) and high protein (&gt;6%) contents, low hardness (&lt;5 N), high springiness (&gt;0.5) and high cohesiveness (&gt;0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R 2 &gt; 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R 2 &lt; 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams. Food science ; Dioscorea ; Sugar ; Texture (cosmology) ; Starch ; Near-infrared spectroscopy ; Near infrared reflectance spectroscopy ; Dry matter ; Mathematics ; Materials science ; Chemistry ; Artificial intelligence ; Botany ; Computer science ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Food composition and properties ; Meat and Animal Product Quality
Authors: Hasan Kurban
Source: Chemical Physics (article, 2021-02-21)
Field: Materials Science — Materials Chemistry

Atom classification with Machine Learning and correlations among physical properties of ZnO nanoparticle Atom (system on chip) ; Nanoparticle ; Random forest ; Chemistry ; Tree (set theory) ; Correlation ; Type (biology) ; Nanotechnology ; Machine learning ; Computer science ; Materials science ; Mathematics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; ZnO doping and properties
Authors: Myeong Geun | Won Ki Son | Yun-Sik Choi | Hyung‐Mo Kim | Eunil Hahm | Bong‐Hyun Jun | Dae Hong Jeong
Source: Applied Surface Science (article, 2021-04-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput multiplex analysis method based on Fluorescence–SERS quantum Dot-Embedded silver bumpy nanoprobes Multiplex ; Nanoprobe ; Quantum dot ; Fluorescence ; Nanotechnology ; Materials science ; Raman spectroscopy ; Silver nanoparticle ; Nanoshell ; Raman scattering ; Nanoparticle ; Optics ; Bioinformatics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Mujde Yahyaoglu | Melis Özen | Yurii Prots | Oussama El Hamouli | Vahe Tshitoyan | Huiwen Ji | Ulrich Burkhardt | B. Lenoir | G. Jeffrey Snyder | Anubhav Jain | Christophe Candolfi | Umut Aydemir
Source: Chemistry of Materials (article, 2021-02-22)
Field: Materials Science — Materials Chemistry

Phase-Transition-Enhanced Thermoelectric Transport in Rickardite Mineral Cu<sub>3–<i>x</i></sub>Te<sub>2</sub> The binary copper chalcogenides Cu2−δX (X = S, Se, and Te) have recently gained significant interest due to their high thermoelectric performance at moderate temperatures. In an effort to unveil new Cu-based compounds with promising thermoelectric potential, Cu3–xTe2 rickardite mineral emerged as a candidate based on a purely text mining approach applied by a machine learning method. Polycrystalline samples of Cu3–xTe2 within the homogeneity range (x = 0.1, 0.2) were successfully synthesized from the raw elements by a solid-state method. High-temperature powder X-ray diffraction combined with differential scanning calorimetry and specific heat measurements showed several reversible phase transitions at around 458, 640, and 647 K. Signatures of these transitions were observed on the electronic and thermal transport properties, measured over a broad range of temperatures (5–733 K). The transition undergone by this compound at 647 K results in a crossover from metallic-like to semiconducting-like properties. The combination of high power factor and low thermal conductivity in the high-temperature phase results in improved thermoelectric performances with a peak dimensionless thermoelectric figure-of-merit zT of ∼0.14 at 733 K. The synthetic rickardite mineral is an exciting candidate to be used as a phase change material in broad application areas such as in waste heat harvesting and photovoltaic systems. Thermoelectric effect ; Materials science ; Seebeck coefficient ; Thermoelectric materials ; Differential scanning calorimetry ; Crystallite ; Phase transition ; Figure of merit ; Thermal conductivity ; Atmospheric temperature range ; Dimensionless quantity ; Analytical Chemistry (journal) ; Mineralogy ; Condensed matter physics ; Thermodynamics ; Metallurgy ; Optoelectronics ; Chemistry ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties