jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chia-Nan Yeh | Sergei Iskakov | Dominika Zgid | Emanuel Gull
Source: Physical review. B./Physical review. B (article, 2021-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electron correlations in the cubic paramagnetic perovskite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Sr</mml:mi><mml:mo>(</mml:mo><mml:mi mathvariant="normal">V</mml:mi><mml:mo>,</mml:mo><mml:mi>Mn</mml:mi><mml:mo>)</mml:mo><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math>: Results from fully self-consistent self-energy embedding calculations In this paper, we use the thermodynamically consistent and conserving self-energy embedding theory (SEET) to study the spectra of the prototypical undistorted cubic perovskites ${\mathrm{SrVO}}_{3}$ and ${\mathrm{SrMnO}}_{3}$. In the strongly correlated metallic ${\mathrm{SrVO}}_{3}$ we find that the usual attribution of the satellite peaks at --1.8 eV to Hund or Hubbard physics in the ${t}_{2g}$ orbitals is inconsistent with our calculations. In the strongly correlated insulator ${\mathrm{SrMnO}}_{3}$ we recover insulating behavior due to a feedback effect between the strongly correlated orbitals and the weakly correlated environment. Our calculation shows a systematic convergence of spectral features as the space of strongly correlated orbitals is enlarged, paving the way to a systematic parameter-free study of correlated perovskites. Atomic orbital ; Perovskite (structure) ; Embedding ; Electron ; Spectral line ; Strongly correlated material ; Metal ; Space (punctuation) Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Advanced Condensed Matter Physics
Authors: Yechuan Zhu | Xiaolin Chen | Weizheng Yuan | Zhiqin Chu | Kwok‐Yin Wong | Dangyuan Lei | Yiting Yu
Source: Opto-Electronic Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A waveguide metasurface based quasi-far-field transverse-electric superlens The imaging capability of conventional lenses is mainly limited by the diffraction of light, and the so-called superlens has been developed allowing the recovery of evanescent waves in the focal plane. However, the remarkable focusing behavior of the superlens is greatly confined in the near-field regime due to the exponential decay of evanescent waves. To tackle this issue, we design a waveguide metasurface-based superlens with an extraordinary quasi-far-field focusing capability beyond the diffraction limit in the present work. Specifically, we analyze the underlying physical mechanism and provide experimental verification of the proposed superlens. The metasurface superlens is formed by an array of gradient nanoslits perforated in a gold slab, and supports transverse-electric (TE) waveguide modes under linearly polarized illumination along the long axis of the slits. Numerical results illustrate that exciting such TE waveguide modes can modulate not only optical phase but also evanescent waves. Consequently, some high-spatial-frequency waves can contribute to the focusing of the superlens, leading to the quasi-far-field super-resolution focusing of light. Under 405 nm illumination and oil immersion, the fabricated superlens shows a focus spot of 98 nm (i.e. <em>λ</em>/4.13) at a focal distance of 1.49 μm (i.e. 3.68<em>λ</em>) using an oil immersion objective, breaking the diffraction limit of <em>λ</em>/2.38 in the quasi-far field regime. The developed metasurface optical superlens with such extraordinary capabilities promises exciting avenues to nanolithography and ultra-small optoelectronic devices. Superlens ; Optics ; Diffraction ; Transverse plane ; Waveguide ; Physics ; Electric field ; Near and far field ; Evanescent wave ; Finite-difference time-domain method ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: S. Soreto Teixeira | M.P.F. Graça | José M.F. Lucas | M.A. Valente | Paula I. P. Soares | M. Carmo Lança | Tânia Vieira | Jorge Silva | João Paulo Borges | Luiza-Izabela Jinga | G. Socol | Cristiane Mello Salgueiro | José Ferreira Nunes | L. C. Costa
Source: Nanomaterials (article, 2021-01-14)
Field: Materials Science — Materials Chemistry

Nanostructured LiFe5O8 by a Biogenic Method for Applications from Electronics to Medicine The physical properties of the cubic and ferrimagnetic spinel ferrite LiFe<sub>5</sub>O<sub>8</sub> has made it an attractive material for electronic and medical applications. In this work, LiFe<sub>5</sub>O<sub>8</sub> nanosized crystallites were synthesized by a novel and eco-friendly sol-gel process, by using powder coconut water as a mediated reaction medium. The dried powders were heat-treated (HT) at temperatures between 400 and 1000 °C, and their structure, morphology, electrical and magnetic characteristics, cytotoxicity, and magnetic hyperthermia assays were performed. The heat treatment of the LiFe<sub>5</sub>O<sub>8</sub> powder tunes the crystallite sizes between 50 nm and 200 nm. When increasing the temperature of the HT, secondary phases start to form. The dielectric analysis revealed, at 300 K and 10 kHz, an increase of ε' (≈10 up to ≈14) with a tanδ almost constant (≈0.3) with the increase of the HT temperature. The cytotoxicity results reveal, for concentrations below 2.5 mg/mL, that all samples have a non-cytotoxicity property. The sample heat-treated at 1000 °C, which revealed hysteresis and magnetic saturation of 73 emu g<sup>-1</sup> at 300 K, showed a heating profile adequate for magnetic hyperthermia applications, showing the potential for biomedical applications. Ferrimagnetism ; Spinel ; Materials science ; Crystallite ; Dielectric ; Ferrite (magnet) ; Magnetic hysteresis ; Chemical engineering ; Nanotechnology ; Analytical Chemistry (journal) ; Composite material ; Magnetization ; Optoelectronics ; Metallurgy ; Chemistry ; Magnetic field ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials ; Electromagnetic wave absorption materials
Authors: Simon Carter | Robert Clough | Andrew Fisher | Bridget Gibson | Ben Russell
Source: Journal of Analytical Atomic Spectrometry (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials This review covers advances in the analysis of advanced materials, metals, fuels and lubricants, nanostructures, ceramics, refractories, organic and inorganic chemicals, catalysts and nuclear materials by a range of techniques including X-ray, ICP, LIBS, mass spectrometry, synchrotron-based techniques, plus non-destructive and ablation surface techniques. Mass spectrometry ; Materials science ; Synchrotron ; Ceramic ; Nanotechnology ; Environmental chemistry ; Analytical Chemistry (journal) ; Chemistry ; Metallurgy ; Chromatography ; Physics Analytical chemistry methods development ; Ion-surface interactions and analysis ; Laser-induced spectroscopy and plasma
Authors: Peyman Saidi | Hadi Pirgazi | Mehdi Sanjari | Saeed Tamimi | Mohsen Mohammadi | Laurent Karim Béland | Mark R. Daymond | Isaac Tamblyn
Source: Computer Methods in Applied Mechanics and Engineering (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

Deep learning and crystal plasticity: A preconditioning approach for accurate orientation evolution prediction Artificial neural network ; Artificial intelligence ; Plasticity ; Orientation (vector space) ; Rotation (mathematics) ; Computer science ; Experimental data ; Surrogate model ; Texture (cosmology) ; Algorithm ; Crystal plasticity ; Crystal (programming language) ; Machine learning ; Materials science ; Mathematics ; Geometry ; Statistics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties
Authors: Zhongyu Wan | Quan‐De Wang | Dongchang Liu | Jinhu Liang
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials Metal oxides are widely used in the fields of chemistry, physics and materials science. Oxygen vacancy formation energy is a key parameter to describe the chemical, mechanical, and thermodynamic properties of metal oxides. How to acquire quickly and accurately oxygen vacancy formation energy remains a challenge for both experimental and theoretical researchers. Herein, we propose a machine learning model for the prediction of oxygen vacancy formation energy via data-driven analysis and the definition of simple descriptors. Starting with the database containing oxygen vacancy formation energies for 1750 metal oxides with enough structural diversity, new descriptors that effectively avoid the defects of molecular fingerprints, molecular graphic descriptors and site descriptors are defined. The descriptors have obvious physical meanings and wide practicability. Multiple linear regression analysis is then used to screen important features for machine learning model development, and two strongly associated features are obtained. The selected descriptors are used as input for the training of 21 machine learning models to select and develop the most accurate machine learning model. Finally, it is shown that the least squares support vector regression method exhibits the best performance for accurate prediction of the targeted oxygen vacancy formation energy through systematic error analysis, and the prediction accuracy is also verified by the external dataset. Our work establishes a novel and simple computational approach for accurate prediction of the oxygen vacancy formation energy of metal oxides and highlights the availability of data-driven analysis for metal oxide material research. Oxide ; Oxygen ; Metal ; Vacancy defect ; Energy (signal processing) ; Materials science ; Chemical physics ; Metallurgy ; Chemistry ; Physics ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Solid Oxide Fuel Cells
Authors: Bing Huang | O. Anatole von Lilienfeld
Source: edoc (University of Basel) (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

Ab initio machine learning in chemical compound space Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest sub-sets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an {\em ab initio} view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics. Chemical space ; Ab initio ; Computer science ; Universality (dynamical systems) ; Quantum ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Jinliang Du | Yun Li Feng | Minghe Zhang
Source: Journal of Materials Research and Technology (article, 2021-10-28)
Field: Materials Science — Materials Chemistry

Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy In recent years, ultra-fine-grained Fe–C alloy have received widespread attention because of their high levels of strength, hardness, and wear resistance. However, these alloy have strict requirements on control of the process parameters during production to meet product specifications. This study was aimed at designing a prediction model for their mechanical properties, which could ultimately promote the industrial development of ultra-fine-grained Fe–C alloy. The effect of process parameters on the mechanical properties of ultra-fine-grained Fe–C alloy was analyzed by electron backscatter diffraction (EBSD) and scanning electron microscope (SEM). By fusing experimental combinations of alloy composition, rolling process, annealing process, and resultant mechanical properties into a dataset for training and verification, a machine-learning-based prediction model of mechanical properties of ultra-fine-grained Fe–C alloy was constructed and tested. When choosing the machine learning algorithm, the best network model was obtained for this application by optimizing a conventional backpropagation (BP) network structure: a three-layer network structure, with a learning rate of 0.5, 18 hidden layer neurons, and activation functions of the hidden and output layers, namely Tansig and Purelin (MATLAB functions), respectively. Finally, the sample data was divided into training set and verification set, and the prediction accuracy, efficiency and generalization ability of BP neural network (BPNN), genetic neural network (GA-BP) and support vector machine (SVM) were compared and analyzed. The superiority of BPNN in the prediction of mechanical properties of ultrafine grained Fe–C alloy was determined, and the mean absolute percentage error (MAPE) of the BPNN for tensile strength, yield strength and uniform elongation are 3.02%, 4.47% and 6.47% respectively, and the number of training steps is 412, which are less than the other two models. The result lays a foundation for the production of high stability and high performance steel products. Materials science ; Artificial neural network ; Alloy ; Backpropagation ; Electron backscatter diffraction ; Machine learning ; Scanning electron microscope ; Support vector machine ; Artificial intelligence ; Computer science ; Composite material ; Microstructure Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Microstructure and Mechanical Properties of Steels
Authors: Yuchu Wang | Bita Ghaffari | Christopher D. Taylor | Simon N. Lekakh | Mei Li | Yue Fan
Source: Scripta Materialia (article, 2021-08-10)
Field: Materials Science — Metals and Alloys

Predicting the energetics and kinetics of Cr atoms in Fe-Ni-Cr alloys via physics-based machine learning Electronegativity ; Materials science ; Austenite ; Kinetic energy ; Interatomic potential ; Thermodynamics ; Grain boundary ; Metallurgy ; Molecular dynamics ; Microstructure ; Physics ; Computational chemistry ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Ademola Soyemi | Tibor Szilvási
Source: Dalton Transactions (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Trends in computational molecular catalyst design Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved. Perspective (graphical) ; Automation ; Computer science ; Nanotechnology ; Catalysis ; Management science ; Biochemical engineering ; Systems engineering ; Engineering ; Artificial intelligence ; Materials science ; Chemistry ; Mechanical engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Bumjoon Seo | Zih‐Yu Lin | Qiyuan Zhao | Michael A. Webb | Brett M. Savoie
Source: Journal of Chemical Information and Modeling (article, 2021-09-17)
Field: Materials Science — Materials Chemistry

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here, we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental properties of 87 organic liquids. The consistent performance of these force fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force fields. Force field (fiction) ; Transferability ; Computer science ; Field (mathematics) ; Topology (electrical circuits) ; Consistency (knowledge bases) ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Jian‐Min Zuo | Renliang Yuan | Yu‐Tsun Shao | Haw-Wen Hsiao | Saran Pidaparthy | Yang Hu | Qun Yang | Jiong Zhang
Source: Microscopy (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

Data-driven electron microscopy: electron diffraction imaging of materials structural properties Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs) and process DPs to extract crystallographic information to form images or tomograms based on crystal structural properties, giving rise to data-driven electron microscopy. Critical to this kind of imaging is the type of crystallographic information being collected, which can be achieved with a judicious choice of electron diffraction techniques, and the efficiency and accuracy of DP processing, which requires the development of new algorithms. Here, we review recent progress made in data collection, new algorithms, and automated electron DP analysis. These progresses will be highlighted using application examples in materials research. Future opportunities based on smart sampling and machine learning are also discussed. Electron crystallography ; Electron diffraction ; Electron tomography ; Diffraction ; Nanocrystalline material ; Reflection high-energy electron diffraction ; Characterization (materials science) ; Transmission electron microscopy ; Materials science ; Energy filtered transmission electron microscopy ; Electron ; Electron backscatter diffraction ; Detector ; Computer science ; Scanning transmission electron microscopy ; Nanotechnology ; Crystallography ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Alexander Skorikov | Wouter Heyvaert | Wiebke Albecht | Daniël M. Pelt | Sara Bals
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex, multi-component systems, such as semiconductor devices, as well as catalytic and plasmonic nanoparticles. Unfortunately, the applicability of EDX tomography is severely limited because of extremely long acquisition times and high electron irradiation doses required to obtain 3D EDX reconstructions with an adequate signal-to-noise ratio. One possibility to address this limitation is intelligent denoising of experimental data using prior expectations about the objects of interest. Herein, this approach is followed using the deep learning methodology, which currently demonstrates state-of-the-art performance for an increasing number of data processing problems. Design choices for the denoising approach and training data are discussed with a focus on nanoparticle-like objects and extremely noisy signals typical for EDX experiments. Quantitative analysis of the proposed method demonstrates its significantly enhanced performance in comparison to classical denoising approaches. This allows for improving the tradeoff between the reconstruction quality, acquisition time and radiation dose for EDX tomography. The proposed method is therefore especially beneficial for the 3D EDX investigation of electron beam-sensitive materials and studies of nanoparticle transformations. Nanoparticle ; Noise reduction ; Materials science ; Artificial neural network ; Computed tomography ; Reduction (mathematics) ; Tomography ; Artificial intelligence ; Nanotechnology ; Computer science ; Mathematics ; Physics ; Radiology ; Optics ; Medicine Machine Learning in Materials Science ; Image and Signal Denoising Methods ; Photoacoustic and Ultrasonic Imaging