jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Odysseas Tsilipakos | Maria Kafesaki | E. N. Economou | Costas M. Soukoulis | Thomas Koschny
Source: Advanced Optical Materials (article, 2020-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Squeezing a Prism into a Surface: Emulating Bulk Optics with Achromatic Metasurfaces Abstract Metasurfaces promise to replace bulky prisms and lenses with 2D surfaces, revolutionizing wavefront control with technologically significant advantages in size, weight, and planar fabrication. However, conventional implementations suffer from large chromatic aberrations and cannot sustain performance over practical bandwidths of real‐world signals because of the limited phase modulation margin available in a surface. How can an infinitely thin surface generate the arbitrarily large, broadband phase delay that bulk phase accumulation can provide? Here, equivalence between bulk optics and certain multiresonant metasurfaces is demonstrated, where phase delay arises from trains of multiple resonances in the effective sheet conductivities of the surface itself instead of accumulation of propagation phase. The fundamentally required electromagnetic surface conductivities of a purely achromatic metasurface are derived and general design rules for arbitrarily broadband beam steering and lensing are obtained. Both operation in transmission and reflection can be achieved in a unified way by proper alignment of the resonances, enabling 360° directionality. The arbitrary spectral bandwidth, dual reflection/transmission operation, and complete dispensability of propagation phase constitute a major advance in the state of the art of achromatic metasurfaces for wavefront manipulation. Achromatic lens ; Optics ; Wavefront ; Broadband ; Planar ; Phase (matter) ; Chromatic scale ; Beam steering ; Physics ; Reflection (computer programming) ; Bandwidth (computing) ; Materials science ; Beam (structure) ; Computer science ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: M.D. Davydova | P.N. Skirdkov | K.A. Zvezdin | Jong-Ching Wu | Sheng-Zhe Ciou | Yi-Ru Chiou | Lin-Xiu Ye | Te-Ho Wu | Ramesh Chandra Bhatt | A.V. Kimel | A.K. Zvezdin
Source: Physical Review Applied (article, 2020-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unusual Field Dependence of the Anomalous Hall Effect in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>Ta</mml:mi><mml:mo>/</mml:mo><mml:mi>Tb</mml:mi><mml:mtext>−</mml:mtext><mml:mi>Fe</mml:mi><mml:mtext>−</mml:mtext><mml:mi>Co</mml:mi></mml:math> Experimental studies of the anomalous Hall effect are performed for thin-film $\mathrm{Ta}/\mathrm{Tb}\text{\ensuremath{-}}\mathrm{Fe}\text{\ensuremath{-}}\mathrm{Co}$ over a wide range of temperatures and magnetic fields up to 3 T. Far from the compensation temperature ($T_{M}$ = 277 K), the field dependence has a conventional shape of a single hysteresis loop; just below the compensation point, the dependence is anomalous with the shape of a triple hysteresis. To understand this behavior, we experimentally reveal the magnetic phase diagram and theoretically analyze it in terms of spin-flop-like phase transitions. In this case, we observe the dominance of the $\mathrm{Fe}\text{\ensuremath{-}}\mathrm{Co}$ sublattice, which is a subject of the strong interaction with the $\mathrm{Ta}$ layer. This $\mathrm{Fe}\text{\ensuremath{-}}\mathrm{Co}$ anisotropy enhancement is expressed in the appearance of abnormal wing-shaped hysteresis loops near the compensation point and, in the unusual phase diagram, where the first-order phase transition line deviates towards low temperatures. This effect can be useful for the design of ultrafast ferrimagnetic devices with desired switching parameters. Ferrimagnetism ; Phase diagram ; Anisotropy ; Magnetic field ; Hall effect ; Hysteresis ; Field dependence ; Instability Magnetic Properties of Alloys ; Topological Materials and Phenomena ; Magnetic properties of thin films
Authors: М. И. Бичурин | Р. В. Петров | Viktor Leontiev | Oleg Sokolov | Andrei V. Turutin | Victor V. Kuts | Ilya V. Kubasov | A. M. Kislyuk | А. А. Темиров | М. Д. Малинкович | Yu. N. Parkhomenko
Source: Sensors (article, 2020-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-Biased Bidomain LiNbO3/Ni/Metglas Magnetoelectric Current Sensor The article is devoted to the theoretical and experimental study of a magnetoelectric (ME) current sensor based on a gradient structure. It is known that the use of gradient structures in magnetostrictive-piezoelectric composites makes it possible to create a self-biased structure by replacing an external magnetic field with an internal one, which significantly reduces the weight, power consumption and dimensions of the device. Current sensors based on a gradient bidomain structure LiNbO<sub>3</sub> (LN)/Ni/Metglas with the following layer thicknesses: lithium niobate-500 μm, nickel-10 μm, Metglas-29 μm, operate on a linear section of the working characteristic and do not require the bias magnetic field. The main characteristics of a contactless ME current sensor: its current range measures up to 10 A, it has a sensitivity of 0.9 V/A, its current consumption is not more than 2.5 mA, and its linearity is maintained to an accuracy of 99.8%. Some additional advantages of a bidomain lithium niobate-based current sensor are the increased sensitivity of the device due to the use of the bending mode in the electromechanical resonance region and the absence of a lead component in the device. Metglas ; Magnetostriction ; Materials science ; Lithium niobate ; Piezoelectricity ; Current (fluid) ; Current sensor ; Sensitivity (control systems) ; Magnetic field ; Linearity ; Optoelectronics ; Nuclear magnetic resonance ; Acoustics ; Electrical engineering ; Electronic engineering ; Composite material ; Physics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Magneto-Optical Properties and Applications
Authors: Marcin Sowa | Łukasz Majka
Source: Nonlinear Dynamics (article, 2020-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ferromagnetic core coil hysteresis modeling using fractional derivatives Abstract The modeling of a ferromagnetic core coil magnetic hysteresis has been considered. The measurement basis consisted of waveforms that have been recorded for various levels of the iron core saturation levels. The investigated models included classical cases as well as models including a nonlinear fractional coil. The possibilities of solutions for transient problems including such models have been recalled. The details of the estimation process have been described next, where each model evaluation made use of an original methodology dealing with periodic steady states. The influence of the model response on parameter changes has also been studied. Further on the parameter estimation procedure has been described, and the results for the various models have been presented. Electromagnetic coil ; Hysteresis ; Saturation (graph theory) ; Nonlinear system ; Magnetic hysteresis ; Ferromagnetism ; Transient (computer programming) ; Waveform ; Core (optical fiber) ; Control theory (sociology) ; Magnetic core ; Estimation theory ; Materials science ; Mechanics ; Computer science ; Mathematics ; Physics ; Engineering ; Condensed matter physics ; Magnetization ; Magnetic field ; Algorithm ; Voltage ; Electrical engineering ; Optics Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Non-Destructive Testing Techniques
Authors: Youssef K. Hamidi | Abdelaziz Berrado | M. Cengiz Altan
Source: AIP conference proceedings (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning applications in polymer composites The primary interest in numerous research problems in both polymer composites and Machine Learning (ML) is to develop predictive models for one or more variables of interest using relevant independent variables, or inputs. However, these two fields have often adopted different approaches, where modeling of composite behavior is often based on physics-based models and phenomenological theories. These physical models are more precise and robust, but often suffer from restricted predictive capability since they are confined to a specific set of conditions. ML models, on the other hand, can be more efficient during the design phase as they allow managing massive and high dimensional data sets to extract the best fit or a predictive behavior for the application at hand.In this context, material scientists would benefit from understanding and implementing some of the powerful ML methods, in order to predict or characterize a behavior of interest of a polymer composite. In this paper, we present a general methodology aimed at employing supervised machine learning models for predicting the properties of polymer composites, including thermo-mechanical properties, environmental effects such as moisture saturation level, durability, or other important behavior, based on the composite constituents, manufacturing processes, relevant process parameters, and expected life-span of the composite product. Context (archaeology) ; Computer science ; Machine learning ; Composite number ; Predictive modelling ; Artificial intelligence ; Process (computing) ; Phenomenological model ; Materials science ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jiaqi Wang | Chengcheng Li | Seungha Shin | Hairong Qi
Source: The Journal of Physical Chemistry C (article, 2020-06-08)
Field: Materials Science — Materials Chemistry

Accelerated Atomic Data Production in <i>Ab Initio</i> Molecular Dynamics with Recurrent Neural Network for Materials Research Ab initio molecular dynamics (AIMD) is a versatile and reliable computational approach to atomic-scale materials science. However, due to the expensive computational cost on the first-principles calculation at each time step, the temporal and spatial scales are significantly limited, hindering its broader applications. Therefore, to accelerate the simulation clock of AIMD, atomic data production in AIMD using a recurrent neural network (RNN) is studied in this research. We demonstrate the feasibility of incorporating RNN-predicted time steps in AIMD, while maintaining its accuracy. The RNN models, which are trained using AIMD simulation results, directly predict atomic velocities and positions of Si atoms, reducing errors by decoupling the position and velocity update procedures from the Newtonian mechanics. Not only the predicted atomic data but also material properties calculated using the predicted data, such as the radial distribution function, temperature, velocity autocorrelation function, phonon density of states, and heat capacity, exhibit excellent agreements with the ground-truth AIMD calculations. Since the RNN prediction is much faster than the first-principles calculation of AIMD, this approach is expected to effectively accelerate AIMD, contributing to computational materials research. Recurrent neural network ; Computer science ; Decoupling (probability) ; Molecular dynamics ; Autocorrelation ; Atomic units ; Position (finance) ; Ab initio ; Statistical physics ; Artificial neural network ; Physics ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics ; Control engineering ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Wen Tong | Qun Wei | Haiyan Yan | Meiguang Zhang | Xuanmin Zhu
Source: Frontiers of Physics (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes Based on structure prediction method, the machine learning method is used instead of the density functional theory (DFT) method to predict the material properties, thereby accelerating the material search process. In this paper, we established a data set of carbon materials by high-throughput calculation with available carbon structures obtained from the Samara Carbon Allotrope Database. We then trained a machine learning (ML) model that specifically predicts the elastic modulus (bulk modulus, shear modulus, and the Young’s modulus) and confirmed that the accuracy is better than that of AFLOW-ML in predicting the elastic modulus of a carbon allotrope. We further combined our ML model with the CALYPSO code to search for new carbon structures with a high Young’s modulus. A new carbon allotrope not included in the Samara Carbon Allotrope Database, named Cmcm-C24, which exhibits a hardness greater than 80 GPa, was firstly revealed. The Cmcm-C24 phase was identified as a semiconductor with a direct bandgap. The structural stability, elastic modulus, and electronic properties of the new carbon allotrope were systematically studied, and the obtained results demonstrate the feasibility of ML methods accelerating the material search process. Materials science ; Bulk modulus ; Carbon fibers ; Crystal structure prediction ; Shear modulus ; Elastic modulus ; Modulus ; Crystal structure ; Composite material ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: A.F. Abd El-Rehim | H. Y. Zahran | D. M. Habashy | Hana M. Al-Masoud
Source: Crystals (article, 2020-04-10)
Field: Materials Science — Biomaterials

Simulation and Prediction of the Vickers Hardness of AZ91 Magnesium Alloy Using Artificial Neural Network Model In this study, an artificial neural network (ANN) model was used to simulate and predict the Vickers hardness of AZ91 magnesium alloy. The samples of AZ91 alloy were aged at different temperatures (Ta = 100 to 300 °C) for different durations (ta = 4 to 192 h) followed by water quenching at 25 °C. The age-hardening response of the samples was investigated by hardness measurements. The microstructure investigations showed that only discontinuous precipitates formed at low aging temperatures (100 and 150 °C), while continuous precipitates invaded all the samples at a high aging temperature (300 °C). Both discontinuous and continuous precipitates formed at the intermediate aging temperatures (200 and 250 °C). X-ray diffraction (XRD) analysis revealed that the microstructure comprised two phases: The α-Mg matrix and intermetallic β-Mg17Al12 phase. The alteration of the crystalline lattice parameters a, c, and c/a ratio with the aging time at various aging temperatures was also investigated. Both c and c/a ratio had the same behavior with aging time while a had an inverse trend. The observed variations of the lattice parameters were attributed to the mode of precipitation in AZ91 alloy. The ANN findings for the simulation and prediction perfectly conformed to the experimental data. Materials science ; Microstructure ; Alloy ; Vickers hardness test ; Precipitation hardening ; Intermetallic ; Quenching (fluorescence) ; Precipitation ; Metallurgy ; Magnesium ; Magnesium alloy ; Hardening (computing) ; Composite material Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Yidi Liu | Qi Yang | Yao Li | Long Zhang | Sanzhong Luo
Source: Chinese Journal of Organic Chemistry (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Application of Machine Learning in Organic Chemistry 近年来，由于计算能力、大数据和算法的不断进步，人工智能（Artificial intelligence，AI）重新兴起，已成为诸多研究领域变革性发展背后的重要推动力.机器学习（Machine learning，ML）是人工智能一个重要的研究领域.随着化学信息学的发展，机器学习在化学领域展现出巨大的发展潜力，也为有机化学的发展带来了新的机遇.为帮助有机化学家了解这一新兴领域，对如何将机器学习策略应用于有机化学研究做简单介绍，同时，概括总结了机器学习在化合物性质预测、分子从头设计、化学反应预测、逆合成分析和智能合成机器方面的应用实例，分析讨论了当前机器学习在有机化学领域面临的挑战和难题. Chemistry ; Environmental chemistry ; Organic chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Xiang Yi | Koji Shimoyama | Keiichi Shirasu | Go Yamamoto
Source: Nanomaterials (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure Carbon nanotubes (CNTs) are novel materials with extraordinary mechanical properties. To gain insight on the design of high-mechanical-performance CNT-reinforced composites, the optimal structure of CNTs with high nominal tensile strength was determined in this study, where the nominal values correspond to the cross-sectional area of the entire specimen, including the hollow core. By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls, chirality, and crosslink density. A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM). It was observed that the influence of crosslink density on the nominal tensile strength tends to gradually decrease from the outside to the inside; generally, the crosslink density between the outermost wall and its adjacent wall is highly significant. In particular, based on our calculation conditions, five-walled, armchair-type CNTs with an outer diameter of 43.39 Å and crosslink densities (between the inner wall and outer wall) of 1.38 ± 1.16%, 1.13 ± 0.69%, 1.54 ± 0.57%, and 1.36 ± 0.35% were believed to be the optimal structure, with the nominal tensile strength and nominal Young's modulus reaching approximately 58-64 GPa and 677-698 GPa. Ultimate tensile strength ; Materials science ; Carbon nanotube ; Molecular dynamics ; Composite material ; Modulus ; Tensile testing ; Nanotechnology ; Computational chemistry ; Chemistry Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Seijiro Matsubara
Source: Chemistry Letters (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Digitization of Organic Synthesis — How Synthetic Organic Chemists Use AI Technology — The core technologies of fully automated organic synthesis include the management of large amounts of accumulated reaction examples, machine learning based on these experimental results, and subsequent development of synthesis devices. Digitization of organic synthesis is key in progressing towards full automation. Various attempts at digitization are already enabling the development of a framework, in which the input of a molecular structure is converted into actual molecular synthesis. The core technologies of fully automated organic synthesis include the management of large amounts of accumulated reaction examples, machine learning based on these experimental results, and subsequent development of synthesis devices. Digitization of organic synthesis is key in progressing towards full automation. Various attempts at digitization are already enabling the development of a framework, in which the input of a molecular structure is converted into actual molecular synthesis. Digitization ; Automation ; Organic synthesis ; Chemistry ; Key (lock) ; Biochemical engineering ; Nanotechnology ; Combinatorial chemistry ; Process engineering ; Computer science ; Organic chemistry ; Telecommunications ; Engineering ; Mechanical engineering ; Catalysis Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Marc Philipp Bahlke | Natnael Mogos | Jonny Proppe | Carmen Herrmann
Source: The Journal of Physical Chemistry A (article, 2020-09-22)
Field: Materials Science — Materials Chemistry

Exchange Spin Coupling from Gaussian Process Regression Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling beyond linear regression, which has not been studied yet. We employ Gaussian process regression, since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating toward larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor and highlighting the interesting question of the role of chemical intuition vs systematic or automated selection of features for machine learning in chemistry and material science. Coupling (piping) ; Regression ; Kriging ; Statistical physics ; Gaussian process ; Process (computing) ; Spin (aerodynamics) ; Gaussian ; Biological system ; Computer science ; Physics ; Condensed matter physics ; Mathematics ; Materials science ; Statistics ; Quantum mechanics ; Biology ; Thermodynamics ; Metallurgy Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Philippe Schwaller | Benjamin Hoover | Jean‐Louis Reymond | Hendrik Strobelt | Teodoro Laino
Source: ChemRxiv (preprint, 2020-05-14)
Field: Materials Science — Materials Chemistry

Unsupervised Attention-Guided Atom-Mapping Knowing how atoms rearrange during a chemical transformation is fundamental to numerous applications aiming to accelerate organic synthesis and molecular discovery. This labelling is known as atom-mapping and is an NP-hard problem. Current solutions use a combination of graph-theoretical approaches, heuristics, and rule-based systems. Unfortunately, the existing mappings and algorithms are often prone to errors and quality issues, which limit the effectiveness of supervised approaches. Self-supervised neural networks called Transformers, on the other hand, have recently shown tremendous potential when applied to textual representations of different domain-specific data, such as chemical reactions. Here we demonstrate that attention weights learned by a Transformer, without supervision or human labelling, encode atom rearrangement information between products and reactants. We build a chemically agnostic attention-guided reaction mapper that shows a remarkable performance in terms of accuracy and speed, even for strongly imbalanced reactions. Our work suggests that unannotated collections of chemical reactions contain all the relevant information to construct coherent sets of reaction rules. This finding provides the missing link between data-driven and rule-based approaches and will stimulate machine-assisted discovery in the chemical domain.Code is available at: https://github.com/rxn4chemistry/rxnmapper Computer science ; ENCODE ; Heuristics ; Theoretical computer science ; Artificial intelligence ; Data mining ; Machine learning ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Advanced Text Analysis Techniques