jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rajat Sainju | Graham Roberts | Wei‐Ying Chen | Brian Hutchinson | Qian Yang | Caiwen Ding | Danny J. Edwards | Meimei Li | Yuanyuan Zhu
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning for Automated Quantification of Irradiation Defects in TEM Data: Relating Pixel-level Errors to Defect Properties Journal Article Deep Learning for Automated Quantification of Irradiation Defects in TEM Data: Relating Pixel-level Errors to Defect Properties Get access Rajat Sainju, Rajat Sainju Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA Search for other works by this author on: Oxford Academic Google Scholar Graham Roberts, Graham Roberts Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA Search for other works by this author on: Oxford Academic Google Scholar Wei-Ying Chen, Wei-Ying Chen Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Brian Hutchinson, Brian Hutchinson Computer Science Department, Western Washington University, Bellingham, WA, USANational Security Directorate, AI and Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA Search for other works by this author on: Oxford Academic Google Scholar Qian Yang, Qian Yang Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA Search for other works by this author on: Oxford Academic Google Scholar Caiwen Ding, Caiwen Ding Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA Search for other works by this author on: Oxford Academic Google Scholar Danny J Edwards, Danny J Edwards Energy and Environment Directorate, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA Search for other works by this author on: Oxford Academic Google Scholar Meimei Li, Meimei Li Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Yuanyuan Zhu Yuanyuan Zhu Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USAEnergy and Environment Directorate, Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA Corresponding Author: yuanyuan.2.zhu@uconn.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1559–1560, https://doi.org/10.1093/micmic/ozad067.802 Published: 22 July 2023 National laboratory ; Library science ; Artificial intelligence ; Computer science ; Art history ; History ; Engineering ; Engineering physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning and Algorithms ; Machine Learning in Materials Science
Authors: Akhil Shajan | Madushanka Manathunga | Andreas Goetz | Kenneth M. Merz
Source: ChemRxiv (preprint, 2023-07-17)
Field: Materials Science — Materials Chemistry

Geometry Optimization: A Comparison of Different Open-Source Geometry Optimizers Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open- source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and energy calcula- tions. The findings demonstrate how the choice of the coordinate system influences the optimization process to reach an equilibrium structure. With fewer steps, internal co- ordinates outperform Cartesian coordinates while the choice of the initial Hessian and Hessian update method in quasi-Newton approaches made by different optimization al- gorithms also contributes to the rate of convergence. Furthermore, an available open- source machine learning method based on Gaussian Process Regression (GPR) was evaluated for energy minimizations over surrogate potential energy surfaces with both Cartesian and internal coordinates, with internal coordinates outperforming Cartesian. Overall, geomeTRIC, ASE, and DL-FIND with their default optimization method as well as with GPR-based model using Hartree–Fock theory with the 6-31G** basis set, needed a comparable number of geometry optimization steps to the approach of Baker using a unit matrix as the initial Hessian to reach the optimized geometry. Due to its performance and the fact that DL-FIND is feature rich we use it as the default optimizer for QUICK. Hessian matrix ; Cartesian coordinate system ; Energy minimization ; Geometry ; Analytic geometry ; Curvilinear coordinates ; Kernel (algebra) ; Computer science ; Mathematics ; Algorithm ; Mathematical optimization ; Applied mathematics ; Physics ; Pure mathematics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Field-Flow Fractionation Techniques
Authors: L Berry | Yang Yang | Sungchan Oh | Charles Brummer | Allen Van Deynze | Lori Hogland
Source:  (preprint, 2023-10-30)
Field: Chemistry — Analytical Chemistry

Using hyperspectral imaging to quantify cadmium stress and predict uptake across different spinach genotypes Cadmium (Cd) is a naturally occurring toxic heavy metal found in trace concentrations in most soils. However, some soils have high levels of Cd due to parent materials or anthropogenic factors such as industrial waste and legacy fertilizers contaminated with Cd. Complications arise when Cd bioaccumulates in soil and plant tissues with carcinogenic effects in humans such as kidney and skeletal dysfunction. Leafy greens including spinach and kale are especially susceptible to Cd accumulation due to high translocation of the metal into the edible leaf tissues of the crop. However, while high concentrations of Cd in soil are generally toxic to plants, unlike other plant stresses, Cd toxicity has no distinguishing visual features that indicate stress or uptake of the metal in the plant, making it difficult to determine if remediation strategies are effective. In this study, emergent plant sensing tools such as Hyperspectral Imaging (HSI) and red-green-blue (RGB) wavelengths were utilized as high-throughput screening techniques to quickly distinguish wavelengths reflected by spinach plants grown in Cd contaminated soil. Feature selection of the HSI and RGB bands were used to identify regions of interest that exhibited significant differences of Cd uptake within multiple spinach genotypes previously shown to vary in Cd uptake. Applications of these technologies provide nondestructive sampling of crops to identify wavelengths sensitive to Cd stress and uptake, with potential applications using machine learning to provide a framework to model these interactions, informing plant breeding programs, and ultimately reducing human health risks. Spinach ; Cadmium ; Hyperspectral imaging ; Soil water ; Contamination ; Environmental science ; Environmental chemistry ; Chemistry ; Agronomy ; Biology ; Remote sensing ; Soil science ; Ecology ; Geology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Metin Aydoğdu | Kadir Akan
Source: Uludağ Üniversitesi Ziraat Fakültesi dergisi/Bursa uludağ üniversitesi ziraat fakültesi dergisi (article, 2023-10-02)
Field: Chemistry — Analytical Chemistry

Investigation of Disease-Yield Relationship of Yellow Rust in Some Bread and Durum Wheat Varieties by Phenological Periods Using Hyperspectral Data The aim of this study was to evaluate the severity of yellow rust in different phenological periods by subjecting bread (Bayraktar 2000, Demir 2000, Eser and Kenanbey) and durum (Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000) wheat varieties to different spore doses (0%, 25%, 50% and 100%) under controlled epidemic conditions. The research was conducted in Yenimahalle, Ankara, Turkey during the 2018-2019 growing season. In the study, the morphological changes in yellow rust severity were determined at different phenological developmental stages of the test materials with the reflectance values obtained by using handheld spectroradiometer in different spore dose applications during the period from tillering to stalk emergence. These reflectance values were converted into vegetation index values expressed by mathematical formulae and used in determining yield estimates. Considering the results obtained, it was determined that the spectral indices calculated especially in the early flowering period (25 May 2019, Feekes 10.5.1) were effective in yield estimation for all bread varieties except Kenanbey variety (15 June 2019, Feekes 10.5.4). It was determined that the spectral band region of 25 May 2019 (Feekes 10.5.1), which includes all indices determined to predict yield in all bread and durum varieties and which is the beginning of flowering, was effective. In grain yield estimation, it was determined that there was a decrease in the correlation values of the spectral indices starting from the early flowering period (Feekes 10.5.1) towards the grain setting period (Feekes 10.5.3) and milk maturity period (Feekes 10.5.4). When the correlations between these index values and yield values were examined, it was determined that prominent phenological periods and high correlation indices could be calculated for these periods. Nowadays, with the use of optical sensor technology instead of traditional disease surveillance methods, the way has paved the way for the development of new approaches for early, fast and accurate yield estimation as a result of the verification of images taken by unmanned aerial vehicles on which multispectral and hyperspectral cameras are located with ground data using artificial intelligence and deep learning techniques. Horticulture ; Rust (programming language) ; Yield (engineering) ; Biology ; Mathematics ; Botany ; Physics Spectroscopy and Chemometric Analyses ; Wheat and Barley Genetics and Pathology ; Mycotoxins in Agriculture and Food
Authors: Kai Sun | Ming Lei | Lei Yuan | Bo Peng | Miao Yu | Xinming Xie | Yuming Zhang | Renxu Jia
Source: Research Square (preprint, 2023-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Crystallographic Orientation-Dependent Resistive Switching in Ga2O3 Thin Films Abstract Resistive random-access memories (RRAMs) based on wide-bandgap oxides is not only a promising candidate for next-generation non-volatile storage technology but also a suitable family of materials capable of neural network computing. However, the exact mechanism of resistive switching (RS) is not yet clearly understood. In this paper, we investigate Ga 2 O 3 -based RRAMs to understand the microscopic-level RS behavior and its relation to the actual process. We find that the oxygenation process during magnetron sputtering affects the crystallization orientation of Ga 2 O 3 thin films. The XRD analysis reveals that the crystalline orientation of Ga 2 O 3 films deposited with O 2 flow is [006], and the prepared devices exhibit a lower operating voltage, a higher high/low resistance state ratio, and a more concentrated distribution. By using first-principles calculations and the climbing image nudged elastic band (CI-NEB) method, we show that the oxygen vacancies of the [006] crystalline Ga 2 O 3 films only need to migrate in the (110) plane to form conductive filaments with an energy barrier of 0.65 eV. In contrast, [122] crystalline Ga 2 O 3 films require additional movement in the Z-axis direction, resulting in a much higher energy barrier. Our results can be utilized to modulate the operating voltage and improve the endurance of Ga 2 O 3 -based RRAMs. Materials science ; Orientation (vector space) ; Resistive touchscreen ; Sputter deposition ; Thin film ; Voltage ; Climbing ; Texture (cosmology) ; Crystallization ; Optoelectronics ; Resistive random-access memory ; Hysteresis ; Sputtering ; Nanotechnology ; Condensed matter physics ; Chemical engineering ; Computer science ; Electrical engineering ; Geometry Ga2O3 and related materials ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Source: Journal of Physics Conference Series (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

28th International Nuclear Physics Conference (INPC2022) The 28th International Nuclear Physics Conference (INPC2022) was successfully held in Cape Town, South Africa from 11 to 16 September 2022, marking the first time the conference was held on the African continent. This event, overseen by the International Union of Pure and Applied Physics (IUPAP), coincided with three major scientific events: The International Year of Basic Sciences for Sustainable Development (IYBSSD), the centenaries of the Nobel Prize awarded to Niels Bohr and the establishment of IUPAP. The conference program spanned a wide range of topics across the breadth of Nuclear Physics, from hadrons to nuclei, from fundamental science to applications, covering topics such as Hadron Structure and Spectroscopy and QCD Partonic Phenomena, Hot and Dense Nuclear Matter, Nuclear Structure and Nuclear Reactions, Nuclear Astrophysics, Applications and Societal Impact, Neutrinos and Nuclei, Fundamental Interactions and Symmetries, New Facilities and Instrumentation, Outreach and Science Education, and Quantum Computing, Machine Learning, and Artificial Intelligence in Nuclear Physics. The in-person conference was the largest to bring our scientific community together since the COVID-19 pandemic, and provided an excellent opportunity for the 468 delegates to explore the latest progress in the field and reconnect with colleagues. The INPC2022 was a great success, providing an excellent platform for the delegates to share their latest research work, techniques, experiences, challenges, and innovative solutions with colleagues from around the world and provided an opportunity for over 40 African students to be exposed to the field. The conference coincided with the completion of many projects that constitute the pillars of the 2017-2022 Long-Range Plan of iThemba LABS. The delegates had the opportunity to visit the facility and appreciate the advancement and completion of many of these pillars. The INPC2022 was sponsored and supported by the South African National Convention Bureau, Department of Science and Innovation, National Research Foundation, Cape Peninsula University of Technology, University of the Witwatersrand, University of the Western Cape, University of Cape Town, Metrological and Applied Sciences University Research Unit, IUPAP, NuPECC, ACF Metals, XIA and IBA. List of Local Organizing Committee, International Advisory Committee, Session Conveners are available in this pdf. Outreach ; Political science ; Nuclear astrophysics ; Physics ; Library science ; Nuclear physics ; Computer science Graphite, nuclear technology, radiation studies
Authors: Matheus Máximo‐Canadas | Itamar Borges
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Absorption Spectra of P-Nitroaniline Derivatives: Charge Transfer Effects and the Role of Substituents Charge (physics) ; Absorption (acoustics) ; Spectral line ; Nitroaniline ; Absorption spectroscopy ; Transfer (computing) ; Chemistry ; Photochemistry ; Computational chemistry ; Materials science ; Physical chemistry ; Physics ; Optics ; Computer science ; Quantum mechanics Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Electrochemical Analysis and Applications
Authors: Zhilong Wang | Junfei Cai | An Chen | Yanqiang Han | Kehao Tao | Simin Ye | Shiwei Wang | Imran Ali | Jinjin Li
Source: arXiv (Cornell University) (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

AlphaMat: A Material Informatics Hub Connecting Data, Features, Models and Applications The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials, which are hampered by months to years of painstaking attempts, resulting in only a small fraction of materials being determined in a vast chemical space. Artificial intelligence (AI)-based methods are promising to address this gap, but face many challenges such as data scarcity and inaccurate material descriptor coding. Here, we develop an AI platform, AlphaMat, that connects materials and applications. AlphaMat is not limited by the data scale (from 101 to 106) and can design structural and component descriptors that are effective for docking with various AI models. With prediction time of milliseconds and high accuracy, AlphaMat exhibits strong powers to model at least 12 common attributes (formation energy, band gap, ionic conductivity, magnetism, phonon property, bulk modulus, dielectric constant, adsorption energy, etc.), resulting in an unexplored material database with over 117,000 entries. We further demonstrate the ability of AlphaMat to mine and design materials, which successfully discover thousands of new materials in photonics, batteries, catalysts, and capacitors from the largest inorganic compound databases that cover all elements in periodic table. This work proposes the first material informatics hub that does not require users to have strong programming knowledge to build AI models to design materials. Users can either directly retrieve our database or easily build AI models through AlphaMat to discover and design the required materials. AlphaMat can shorten the cycle of database construction and material discovery by at least decades, and its effective use will facilitate the applications of AI technology in material science and lead scientific and technological progress to a new height. Computer science ; Materials informatics ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Liqun Kang | Ruoyu Xu | Jay Hon Cheung Yan | Fernando Cacho‐Nerin | Juila E. Parker | Christopher S. Allen | Feng Ryan Wang
Source: ChemRxiv (preprint, 2023-09-24)
Field: Materials Science — Surfaces, Coatings and Films

Quantification and visualisation statistics of Pt cathode degradation via correlative electron and X-ray imaging Catalytic degradation in proton exchange membrane fuel cells (PEMFCs) has always been a challenging subject. Despite the monumental research on the subject1-6, the complex relationship between the catalyst deactivation mechanisms and electrochemical degradation remains obscure owing to the heterogenous nature of the catalyst structure. Here we report a correlative electron and X-ray imaging method that enables a multi-modal and multi-length-scale quantification and visualisation of Pt degradation mechanisms in the oxygen reduction reaction (ORR) for proton exchange membrane fuel cell (PEMFC). This technique provides statistical information of up to 107 Pt nanoparticle with 2-dimensional (2D) statistical distribution of Pt content and nanoparticle number in length scales from 2 nm to 20 µm. With the accelerated durability test (ADT) under an O2-saturated 0.1 M HClO4 electrolyte between 0.4 and 1.2 V vs. reversible hydrogen electrode (RHE), the statistical results reveal that the carbon supported Pt nanoparticle catalyst experiences 1) 1:1.5 ratio between the type I degradation mechanisms, Pt dissolution and particle detachment, and type II, Ostwald ripening and particle agglomeration; 2) electrocatalytic activity losses of 33.4% and 41.8% for type I and II degradations, respectively; and 3) macro-scale Pt redistribution from Pt dense regions to sparse regions at an average distance of 1.6 ± 0.9 µm. Proton exchange membrane fuel cell ; Catalysis ; Electrochemistry ; Electrolyte ; Dissolution ; Nanoparticle ; Degradation (telecommunications) ; Materials science ; Chemical engineering ; Ostwald ripening ; Platinum ; Electrode ; Chemistry ; Nanotechnology ; Computer science ; Physical chemistry Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Filippo Longo | Marin Nikolić | Andreas Borgschulte
Source: Surface and Interface Analysis (article, 2023-11-07)
Field: Materials Science — Surfaces, Coatings and Films

Deep core level hard X‐ray photoelectron spectroscopy for catalyst characterization Abstract Heterogeneous catalysts are the crucial element for many catalytic processes. In most of the cases, the pivotal structure consists of catalytic metals/alloy particles supported by oxides. Knowledge of the interaction between metal and oxide is central to understand the structure–performance relationship of such systems. X‐ray photoelectron spectroscopy provides access to the chemical–physical properties of metal and oxide interface as well as polarization effects. The results are usually derived from changes of the measured binding energies based on initial state analysis. We propose to extend the analysis using photoelectron as well as Auger transition to include final state effects (Wagner/Hohlneichner plots). This gives additional information on the specific chemical neighborhood of the excited atom. Three archetypal systems are investigated by hard X‐ray photoelectron spectroscopy (HAXPES) to introduce two approaches to this analysis for the most common support elements Al and Si. X-ray photoelectron spectroscopy ; Chemical state ; Transition metal ; Oxide ; Binding energy ; Auger ; Catalysis ; Auger electron spectroscopy ; Chemistry ; Electronic structure ; Alloy ; Metal ; Atom (system on chip) ; Electron spectroscopy ; Oxidation state ; Materials science ; Atomic physics ; Computational chemistry ; Physics ; Nuclear magnetic resonance ; Computer science ; Metallurgy Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Miharu Eguchi | Minsu Han | Yusuke Asakura | Jonathan P. Hill | Joel Henzie | Katsuhiko Ariga | Alan E. Rowan | Watcharop Chaikittisilp | Yusuke Yamauchi
Source: Angewandte Chemie (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Materials Space‐Tectonics: Atomic‐level Compositional and Spatial Control Methodologies for Synthesis of Future Materials Abstract Reactions occurring at surfaces and interfaces necessitate the creation of well‐designed surface and interfacial structures. To achieve a combination of bulk material ( i.e ., framework) and void spaces, a meticulous process of “nano‐architecting” of the available space is necessary. Conventional porous materials such as mesoporous silica, zeolites, and metal–organic frameworks lack advanced cooperative functionalities owing to their largely monotonous pore geometries and limited conductivities. To overcome these limitations and develop functional structures with surface‐specific functions, the novel materials space‐tectonics methodology has been proposed for future materials synthesis. This review summarizes recent examples of materials synthesis based on designing building blocks ( i.e ., tectons) and their hybridization, along with practical guidelines for implementing materials syntheses and state‐of‐the‐art examples of practical applications. Lastly, the potential integration of materials space‐tectonics with emerging technologies, such as materials informatics, is discussed. Nanotechnology ; Materials science ; Space (punctuation) ; Mesoporous material ; Tectonics ; Computer science ; Geology ; Chemistry Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science