jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Natasha Dropka | Stefan Ecklebe | Martin Holeňa
Source: Crystals (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks. Autoregressive model ; Mean squared error ; Nonlinear autoregressive exogenous model ; Artificial neural network ; Computer science ; Recurrent neural network ; Artificial intelligence ; Nonlinear system ; Position (finance) ; Process (computing) ; Transient (computer programming) ; Visualization ; Gallium arsenide ; Dynamics (music) ; Deep learning ; Pattern recognition (psychology) ; Machine learning ; Algorithm ; Mathematics ; Statistics ; Optics ; Physics ; Acoustics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: He Ning | Runhai Ouyang | Quan Qian
Source: AIP Advances (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Learning interpretable descriptors for the fatigue strength of steels While the new paradigm of data-driven materials science has proven efficient in accelerated materials discovery, one challenge is whether the data-driven methods could deliver interpretable models that provide scientific insights in addition to accuracy. In this work, with the example of data-driven materials design for high-strength steels, we compared the efficiency of the recent Sure Independence Screening and Sparsifying Operator (SISSO) with several other conventional machine learning methods, Support Vector Regression (SVR), Decision Tree (DTe), and Gradient Boost Decision Tree (GBDT). The results show that SISSO gives interpretable and simple descriptors, while the accuracy is comparable to that of the relatively “black-box” model from SVR, GBDT, and DTe. The best SISSO descriptor was found to be scientifically consistent with that in previous studies. In addition, we show that combining with particle swarm optimization, the simple and explicit expression of the descriptor also bears advantages in reverse materials design, which is a general way for machine learning to not only predict but also tell what is the next possible action to be done. Computer science ; Simple (philosophy) ; Particle swarm optimization ; Decision tree ; Artificial intelligence ; Machine learning ; Support vector machine ; Operator (biology) ; Independence (probability theory) ; Black box ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Statistics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Sensong An | Bowen Zheng | Mikhail Y. Shalaginov | Hong Tang | Hang Li | L. P. Zhou | Yunxi Dong | Mohammad Haerinia | Anu Agarwal | Clara Rivero‐Baleine | Myungkoo Kang | Kathleen A. Richardson | Tian Gu | Juejun Hu | Clayton Fowler | Hualiang Zhang
Source: arXiv (Cornell University) (preprint, 2021-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since the near-field coupling effects between elements will change when surrounded by non-identical structures. In this paper, we propose a deep learning approach to predict the actual electromagnetic (EM) responses of each target meta-atom placed in a large array with near-field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta-atom and its neighbors as input, and calculates its phase and amplitude in milliseconds. This approach can be applied to explain metasurfaces' performance deterioration caused by mutual coupling and further used to optimize their efficiencies once combined with optimization algorithms. To demonstrate the efficacy of this methodology, we obtain large improvements in efficiency for a beam deflector and a metalens over the conventional design approach. Moreover, we show the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, we envision that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs. Coupling (piping) ; Realization (probability) ; Computer science ; Convolutional neural network ; Boundary (topology) ; Phase (matter) ; Artificial neural network ; Field (mathematics) ; Electronic engineering ; Topology (electrical circuits) ; Physics ; Artificial intelligence ; Materials science ; Engineering ; Electrical engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ling Qiao | Jingchuan Zhu
Source: Advanced Engineering Materials (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties Herein, attempts have been made to design and develop pearlitic steels for application in heavy‐haul rails. The hardness plays a vital role in studying the mechanical and tribological properties, which is theoretically related to the alloying composition of steel. With aid of machine learning (ML) method, the particle swarm optimization (PSO) improved generalized regression neural network (GRNN) is utilized to model the relationship between composition and hardness of pearlitic steel. The results show that the designed steel exhibits superior hardness and mechanical properties with fine pearlite lamellar microstructure. In addition, the wear behavior of the steel and its wear mechanism are systematically studied by tribological testing and electron probe microanalysis (EPMA) observations of worn surface and wear particles. With composition optimization, the wear resistance has further improved as evidenced by the lower friction coefficient and reduction of wear volume. The pearlitic steels exhibit a combined wear mechanism including adhesive wear, abrasion, delamination, and plastic deformation. As a result, the designed steels offer high hardness with very good mechanical and tribological properties which are far superior to previously reported pearlitic steels. This work may assist in developing the appropriate composition to create the desired hardness, mechanical, and tribological properties. Materials science ; Tribology ; Pearlite ; Abrasion (mechanical) ; Metallurgy ; Delamination (geology) ; Microstructure ; Abrasive ; Composite material Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Mechanical stress and fatigue analysis
Authors: Christopher T. Nelson | Ayana Ghosh | Mark P. Oxley | Xiaohang Zhang | Maxim Ziatdinov | Ichiro Takeuchi | Sergei V. Kalinin
Source: npj Computational Materials (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding Abstract Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations. Maxima and minima ; Scanning transmission electron microscopy ; Polarization (electrochemistry) ; Convolutional neural network ; Ferroelectricity ; Atomic units ; Physics ; Deep learning ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Optics ; Chemistry ; Transmission electron microscopy ; Optoelectronics ; Mathematics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Yu Hou | Jimei Qi | Jiwei Hu | Wenqian Ruan | Yiqiu Xiang | Xionghui Wei
Source: International Journal of Environmental Science and Technology (article, 2021-04-24)
Field: Chemistry — Organic Chemistry

Decolorizing brilliant green by mesoporous Pd–Fe magnetic nanoparticles immobilized on reduced graphene oxide: artificial neural network modeling Aqueous solution ; Adsorption ; Materials science ; Freundlich equation ; Mesoporous material ; Graphene ; Brilliant green ; Nanoparticle ; Chemical engineering ; Oxide ; Endothermic process ; Response surface methodology ; Magnetic nanoparticles ; Nanocomposite ; Nanomaterials ; Nanotechnology ; Chemistry ; Chromatography ; Organic chemistry ; Catalysis ; Metallurgy Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Nikolaos Tziolas | Stella A. Ordoudi | Apostolos Tavlaridis | Konstantinos Karyotis | George Zalidis | Ioannis Mourtzinos
Source: Sustainability (article, 2021-06-09)
Field: Chemistry — Analytical Chemistry

Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques A sustainable process for valorization of onion waste would need to entail preliminary sorting out of exhausted or suboptimal material as part of decision-making. In the present study, an approach for monitoring red onion skin (OS) phenolic composition was investigated through Visible Near-Short-Wave infrared (VNIR-SWIR) (350–2500 nm) and Fourier-Transform-Mid-Infrared (FT-MIR) (4000–600 cm−1) spectral analyses and Machine-Learning (ML) methods. Our stepwise approach consisted of: (i) chemical analyses to obtain reference values for Total Phenolic Content (TPC) and Total Monomeric Anthocyanin Content (TAC); (ii) spectroscopic analysis and creation of OS spectral libraries; (iii) generation of calibration and validation datasets; (iv) spectral exploratory analysis and regression modeling via several ML algorithms; and (v) model performance evaluation. Among all, the k-nearest neighbors model from 1st derivative VNIR-SWIR spectra at 350–2500 nm resulted promising for the prediction of TAC (R2 = 0.82, RMSE = 0.52 and RPIQ = 3.56). The 2nd derivative FT-MIR spectral fingerprint among 600–900 and 1500–1600 cm−1 proved more informative about the inherent phenolic composition of OS. Overall, the diagnostic value and predictive accuracy of our spectral data support the perspective of employing non-destructive spectroscopic tools in real-time quality control of onion waste. VNIR ; Partial least squares regression ; Anthocyanin ; Chemometrics ; Content (measure theory) ; Chemistry ; Artificial intelligence ; Hyperspectral imaging ; Mathematics ; Computer science ; Machine learning ; Food science Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Alexander V. Shapeev | Dmitry Bocharov | Alexei Kuzmin
Source: arXiv (Cornell University) (article, 2021-08-11)
Field: Materials Science — Materials Chemistry

Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra Machine-learning potentials for materials, namely the moment tensor potentials (MTPs), were validated using experimental EXAFS spectra for the first time. The MTPs for four metals (bcc W and Mo, fcc Cu and Ni) were obtained by the active learning algorithm of fitting to the results of the calculations using density functional theory (DFT). The MTP accuracy was assessed by comparing metal K-edge EXAFS spectra obtained experimentally and computed from the results of molecular dynamics (MD) simulations. The sensitivity of the method to various aspects of the MD and DFT models was demonstrated using Ni as an example. Good agreement was found for W, Mo and Cu using the recommended PAW pseudopotentials, whereas a more accurate pseudopotential with 18 valence electrons was required for Ni to achieve a similar agreement. The use of EXAFS spectra allows one to estimate the MTP ability in reproducing both average and dynamic atomic structures. Pseudopotential ; Extended X-ray absorption fine structure ; Spectral line ; Density functional theory ; Valence (chemistry) ; Tensor (intrinsic definition) ; Chemistry ; Valence electron ; Electron ; Materials science ; Molecular physics ; Atomic physics ; Computational chemistry ; Absorption spectroscopy ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Niuchang Ouyang | Chen Wang | Zezhu Zeng | Yue Chen
Source: Applied Physics Letters (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Anharmonic lattice dynamics of SnS across phase transition: A study using high-dimensional neural network potential SnS that exhibits strong lattice anharmonicity and a structural phase transition between the Pnma and Cmcm phases has emerged as a high-performance thermoelectric material. Herein, the lattice dynamics of SnS have been investigated by molecular dynamics to reveal the soft mode mechanisms across the phase transition. We construct a first-principles-based machine-learning potential, which is capable of reproducing the dynamical nature of the structural phase transition of SnS. We reproduce an explicit softening of the zone-center phonon mode and unveil a similar behavior at the zone boundary U = (0.5, 0.0, 0.5) of SnS, which are attributed to the large anharmonicity induced by the phase transition. Our results reveal a non-monotonic temperature dependence of the linewidth of the soft modes beyond the perturbative regime. The zone-center optical soft mode collapses across the structural phase transition, corresponding to a second-order nature. These results pave the way for further phonon engineering of SnS and the related IV–VI thermoelectrics. Anharmonicity ; Soft modes ; Phonon ; Condensed matter physics ; Phase transition ; Materials science ; Thermoelectric materials ; Lattice (music) ; Laser linewidth ; Softening ; Thermoelectric effect ; Physics ; Quantum mechanics ; Optoelectronics Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Thermal properties of materials
Authors: Lirong Xu | Mei Xue | Jiarui Chang | Gangcheng Wu | Qingzhe Jin | Xingguo Wang
Source: Journal of Oleo Science (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R<sup>2</sup> > 0.926, RMSEC < 0.481) compared with partial least-squares model (R<sup>2</sup> > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying. Fourier transform infrared spectroscopy ; French fries ; Chemistry ; Partial least squares regression ; Absorbance ; Acid value ; Sunflower oil ; Deep frying ; Vegetable oil ; Hydrolysis ; Food science ; Mathematics ; Chromatography ; Organic chemistry ; Chemical engineering ; Biochemistry Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: J. Jugo | J. Feuchtwanger | J. Corres
Source: Sensors and Actuators A Physical (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Numerical optimization based control design for a ferromagnetic shape memory alloy actuator Ferromagnetic shape memory alloys (FSMA) based actuators are able to get very good precision, allowing, indeed, under nanometer positioning applications. To get those precisions, a good controller is needed, and different strategies have been developed. Experimental tests with a FSMA actuator designed by the research group have proven that the use of a controller operating in a “set-and-forget”mode and following an event-based control scheme allows for a position with a given precision and transient behavior to be achieved, with a reduced number of control actions and therefore a reduced energy consumption. However, the tuning of all control parameter can be hard, especially when taking into account the nonlinear characteristics of the actuator. It can be remarked that the NiMnGa alloy used in the actuator is highly hysteric, and that the control action is pulsed. In this work, a numerical optimization based design methodology is proposed, making easier the control design procedure. The tuning procedure use a model, that for the particular actuator considered in this work, is obtained using machine learning tools, in particular the Tensorflow/Keras framework. An application example shows the good results obtained, including experimental result. Actuator ; Control theory (sociology) ; Controller (irrigation) ; Computer science ; Shape-memory alloy ; Position (finance) ; Work (physics) ; Nonlinear system ; Set (abstract data type) ; Energy consumption ; Control engineering ; Control (management) ; Engineering ; Mechanical engineering ; Artificial intelligence ; Physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Oskar Tainio | Leevi Viitanen | Jonatan R. Mac Intyre | Mehmet Serhat Aydın | Juha Koivisto | Antti Puisto | Mikko J. Alava
Source: Physical Review Materials (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

Predicting and following T1 events in dry foams from geometric features Machine learning techniques have been recently applied in predicting deformation in amorphous materials. In this study, we extract structural features around liquid film vertices from images of flowing 2D foam and apply a multilayer perceptron to predict local yielding. We evaluate their importance in the description of the T1 events and show that a high level of predictability may be achieved using well-chosen combinations of features as the prediction data. The most relevant features are extracted by performing the predictions separately for isolated sets of features, and these findings are verified using principal component analysis. Using this approach, we determine which properties of the images are most important with regard to the physics of the processes. Our findings indicate that film lengths and angles between the liquid films joining at the vertex are the most important features that predict the local yield events. These two features describe 83% of the yield events. As an application, we extract the statistics of event waiting times from the experiment. Predictability ; Materials science ; Principal component analysis ; Yield (engineering) ; Amorphous solid ; Vertex (graph theory) ; Characterization (materials science) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Statistics ; Composite material ; Mathematics ; Crystallography ; Nanotechnology Pickering emulsions and particle stabilization ; Cultural Heritage Materials Analysis
Authors: Zhou Tingwei
Source: Journal of Physics Conference Series (article, 2021-07-01)
Field: Chemistry — Analytical Chemistry

Red wine quality prediction through active learning Abstract General machine learning requires a large number of training samples, meaning a lot of manpower and material resources. To solve this problem, active learning and its algorithms are often used. Active learning is a form of semi-supervised machine learning where the algorithm can choose which data it wants to learn from and then use the smallest and most effective labeled data set to make predictions or classifications. This article will show an example of using active learning to predict red wine quality. The predictive modeling approach the author chose was the K-Nearest Neighbor and the active learning algorithm was ranked batch-mode sampling. By observing the learning curve, the author found that generally the prediction accuracy of active learning would increase as the number of iterations increased. The author compared the experiment with another case using classic iris flower data set, and concluded that the prediction accuracy of active learning for different data sets depends on many factors, such as the correlation between the independent and dependant variables, the size of the data set and the number of iterations. Active learning (machine learning) ; Machine learning ; Artificial intelligence ; Computer science ; Instance-based learning ; Set (abstract data type) ; Online machine learning ; Quality (philosophy) ; Semi-supervised learning ; Wine ; Data mining Spectroscopy and Chemometric Analyses ; Machine Learning and Algorithms ; Mineral Processing and Grinding