jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ayman Negm | Mohamed H. Bakr | Matiar M. R. Howlader | Shirook Ali
Source: 2021 International Applied Computational Electromagnetics Society Symposium (ACES) (article, 2021-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Deep-learning Approach for Modeling Phase-change Metasurface in the Mid-infrared Reconfigurable metasurface constitutes an important block for future adaptive and smart nanophotonic applications. In this work we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model the material properties as image maps. The dimensionality mismatch problem is avoided by using the operating wavelength as an input to the network, so the model is used as single-point solver. As a case study, we model the response of a reconfigurable absorber employing phase transition of vanadium dioxide in the mid-infrared. The results show that our model provides accurate prediction of the metasurface response using small training dataset. Computer science ; Solver ; Deep learning ; Vanadium dioxide ; Convolutional neural network ; Phase (matter) ; Infrared ; Electronic engineering ; Artificial intelligence ; Optics ; Phase transition ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Image Enhancement Techniques
Authors: Sadia Noureen | Muhammad Zubair | Mohsen Ali | Muhammad Qasim Mehmood
Source: 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) (article, 2021-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning based Sequence Modeling for Optical response retrieval of photonic nanostructures State of the art research in nanophotonics focuses on the development of compact and efficient on-chip devices that are compatible with integrated photonics. Optical Metasurfaces consisting two-dimensional array of subwavelength featured nanostructures have come forth as a perfect candidate to realize such compact photonics chips. They exhibit a unique capability of controlling and manipulating the electromagnetic waves to achieve novel optical responses. Regardless of the vast capabilities and immense potential of nanoscale optical components, their design procedure still suffers from some major drawbacks such as extreme time consumption and high computational resources requirement. A lot of manual work, numerical solutions and multiple simulations are required to achieve metasurfaces having desired responses. Here we present a unique extremely time efficient methodology for finding the optical response of photonic nanostructures using a hybrid deep neural network model that combines convolutional neural networks(CNN), Sequence modeling, and transfer learning. This model incorporates deep residual CNN (ResNet) to extract the spatial information form the images and other geometrical parameters of nanostructures, followed by a gated recurrent unit (GRU) based Sequence Model to map the feature vector to the output and predict the optical absorption spectrum of these structures. Later we utilize transfer learning to extend the model to accommodate nanostructures made up of different materials having diverse physical properties. Our experiments indicate that the proposed methodology accurately predicts the optical response with in a fraction of seconds, which makes it a potential alternative for the conventional time consuming and computationally exhaustive numerical simulations and EM software's. Photonics ; Computer science ; Nanophotonics ; Deep learning ; Convolutional neural network ; Electronic engineering ; Artificial neural network ; Artificial intelligence ; Materials science ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Yong Wang | Wei Wang | Dongbin Zhang | Xiaolin Tian | Qu Jiaojiao | Mingshan Zhang | Liyuan Sun | Huanxin Li | Youliang Wei | Zhouqiang Qu
Source:  (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Intelligent laser machining system for multi-function processing of superhard materials An intelligent laser machining system is developed for precision processing of superhard materials, such as diamond, silicon carbide, silicon nitride, cubic boron nitride, etc. Multiple processing functions, such as laser cutting, laser engraving, laser cleaning and polishing, can be realized by automatically adjusting laser beam parameters. The laser wavelength is 532 nm that is obtained by the second-harmonic generation of a diode-pumped solid-state laser at 1064 nm. The processing system is highly intelligent and automated, from surface scanning and measurement to self-adjustment of laser beam parameters and motion control variables during processing. In our processing, the workpiece is first scanned using a laser sensor and a 3-D profile of the workpiece is created for customer evaluation. Then, with operator’s input of processing requirements, an optimal processing procedure is designed in terms of comprehensive consideration of machining efficiency, processing duration, machine cost, surface quality, material loss, etc. Next, the material is flatten to a certain flatness by using a suitable engraving process, followed by polishing to a certain roughness, and finally cut to shaped pieces. In both the procedure design and material processing, an artificial neural network method is used in the optimization and adjustment of laser beam and motion control parameters. In the experiment of silicon carbide processing, &gt;10-mm thick bulk materials can be cut with nearly vertical edges and polished with ~0.1 um roughness of surface. The system exhibits broad prospects in the processing applications of superhard materials. Materials science ; Machining ; Laser ; Surface roughness ; Surface micromachining ; Polishing ; Silicon nitride ; Surface finish ; Laser beam machining ; Silicon carbide ; Optics ; Laser cutting ; Optoelectronics ; Silicon ; Composite material ; Metallurgy ; Laser beams Diamond and Carbon-based Materials Research ; Advanced Surface Polishing Techniques ; Laser Material Processing Techniques
Authors: Zhong Ni | Anlin Wang | Lingyu Kang | Tiancheng Zhang
Source: The Protein Journal (article, 2021-01-16)
Field: Chemistry — Spectroscopy

QSSR Modeling of Bacillus Subtilis Lipase A Peptide Collision Cross-Sections in Ion Mobility Spectrometry: Local Descriptor Versus Global Descriptor Tripeptide ; Bacillus subtilis ; Ion-mobility spectrometry ; Cross-validation ; Biological system ; Chemistry ; Partial least squares regression ; Support vector machine ; Peptide ; Artificial intelligence ; Mass spectrometry ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Chromatography ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Vishvak Kannan | Karthik Somasundaram | Adrian C. Fisher | Erik Birgersson
Source: International Journal of Energy Research (article, 2021-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Monte Carlo‐based sensitivity analysis of an electrochemical capacitor The operation of electrochemical capacitors depends not only on extrinsic operating and design parameters, but also intrinsic physical, material, and electrochemical parameters. Fluctuations in these stochastic parameters can significantly influence the performance and may lead to quicker degradation of the electrochemical capacitors, thereby affecting their durability and reliability. Thus, it is important to quantify the sensitivities of these extrinsic and intrinsic parameters and correlate them with the performance, to delay the inevitable performance degradation. To achieve this, we perform Monte Carlo simulations (MCS) followed by sensitivity analysis under high and low charge/discharge current (load) conditions. The MCS is statistically performed by varying all the stochastic parameters simultaneously. We then identify the critical parameters that affect the performance under different load conditions, which provides insights into optimal operation of electrochemical capacitors. The thickness of positive electrode and the radius of active material were identified as the most significant parameters under low and high load conditions, respectively. Furthermore, we derive reduced order surrogate models with at least 95% accuracy using supervised machine learning techniques to predict the performance without solving the full physics-based model. Monte Carlo method ; Capacitor ; Sensitivity (control systems) ; Reliability (semiconductor) ; Materials science ; Degradation (telecommunications) ; Electrochemistry ; Durability ; Electrode ; Control theory (sociology) ; Computer science ; Biological system ; Reliability engineering ; Voltage ; Simulation ; Electronic engineering ; Engineering ; Mathematics ; Chemistry ; Physics ; Power (physics) ; Statistics ; Composite material ; Thermodynamics ; Electrical engineering Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advanced Battery Technologies Research
Authors: Silvan Käser | Eric D. Boittier | Meenu Upadhyay | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2021-03-09)
Field: Materials Science — Materials Chemistry

MP2 Is Not Good Enough: Transfer Learning ML Models for Accurate VPT2 Frequencies The calculation of the anharmonic modes of small to medium sized molecules for assigning experimentally measured frequencies to the corresponding type of molecular motions is computationally challenging at sufficiently high levels of quantum chemical theory. Here, a practical and affordable way to calculate coupled-cluster quality anharmonic frequencies using second order vibrational perturbation theory (VPT2) from machine-learned models is presented. The approach, referred to as "NN + VPT2", uses a high-dimensional neural network (PhysNet) to learn potential energy surfaces (PESs) at different levels of theory from which harmonic and VPT2 frequencies can be efficiently determined. The NN + VPT2 approach is applied to eight small to medium sized molecules (H$_2$CO, trans-HONO, HCOOH, CH$_3$OH, CH$_3$CHO, CH$_3$NO$_2$, CH$_3$COOH and CH$_3$CONH$_2$) and frequencies are reported from NN-learned models at the MP2/aug-cc-pVTZ, CCSD(T)/aug-cc-pVTZ and CCSD(T)-F12/aug-cc-pVTZ-F12 levels of theory. For the largest molecules and at the highest levels of theory, transfer learning (TL) is used to determine the necessary full-dimensional, near-equilibrium PESs. Overall, NN + VPT2 yields anharmonic frequencies to within 20 cm$^{-1}$ of experimentally determined frequencies for close to 90 % of the modes for the highest quality PES available and to within 10 cm$^{-1}$ for more than 60 % of the modes. For the MP2 PESs only around 60 % of the NN + VPT2 frequencies were within 20~cm$^{-1}$ of the experiment, with outliers up to 150 cm$^{-1}$ compared with experiment. It is also demonstrated that the approach allows to provide correct assignments for strongly interacting modes such as the OH bending and the OH torsional modes in formic acid monomer and the CO-stretch and OH-bend mode in acetic acid. Anharmonicity ; Perturbation theory (quantum mechanics) ; Outlier ; Physics ; Molecule ; Harmonic ; Coupled cluster ; Quality (philosophy) ; Potential energy surface ; Quantum ; Perturbation (astronomy) ; Potential energy ; Statistical physics ; Atomic physics ; Computational chemistry ; Quantum mechanics ; Chemistry ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Zhijie Qin | Zhang Zhong-fu | Xiangdong Hua | Wanneng Yang | Xiuying Liang | Ruifang Zhai | Chenglong huang
Source: Research Square (preprint, 2021-09-17)
Field: Chemistry — Analytical Chemistry

Cereal Grain 3D Point Cloud Analysis Method For Shape Extraction And Filled/Unfilled Grain Identification Based On Structured Light Imaging Abstract Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 seconds per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The accuracy for indica and japonica identification was about 99.785%,while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research. Point cloud ; Preprocessor ; Segmentation ; Identification (biology) ; Cluster analysis ; Artificial intelligence ; Extraction (chemistry) ; Materials science ; Computer science ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Seema Singh Saharan | Pankaj Nagar | Kate Townsend Creasy | Eveline O. Stock | James Feng | Mary J. Malloy | John P. Kane
Source: 2021 International Conference on Computational Science and Computational Intelligence (CSCI) (article, 2021-12-01)
Field: Chemistry — Analytical Chemistry

Implementation of PCA enabled Support Vector Machine using cytokines to differentiate smokers versus nonsmokers Presently, the role of cytokines in severe illness like COPD, cancer, cardiac disease associated with smoking is being explored to enable preemptive diagnosis and delivery of treatment interventions. We are investigating the connection between the elevation of inflammatory plasma cytokine in smokers versus nonsmokers. Disease indicator cytokines can be used to monitor the progression of disease which can help in the crucial task of prognosis and definitive diagnosis. Powerful and versatile Machine Learning algorithms can be leveraged to extract insights that cannot be obtained manually. We have applied Support Vector Machine (SVM) on 65 plasma cytokines and other traditional biomarkers to differentiate smokers and nonsmokers. To optimize the classification separability, we have used the following techniques: Principal component analysis (PCA), 10-fold cross validation and variable importance. The primary metric of evaluation is Area Under Receiver Operating Curve (AUROC), though we have additionally recorded and compared prediction accuracy across classifiers. The results are very promising. The AUROC classification accuracy achieved by SVM using the selected predictor feature variables is 89.2% with a 95%CI (85.4%,93.1%). The most prominent cytokines, contributing to the classification, in the order of importance are: I-TAC, Age, TG, G-CSF-CSF-3, MDC-CCL22, Eotaxin-3, LIF, IL-2, Eotaxin-2, MIP-3alpha. The AUROC classification accuracy improved to 93% with a 95% CI (90.1%,99.5%) upon choosing the five most prominent cytokines. The versatile prowess of Machine Learning algorithms such as Support Vector Machine can translate pioneering molecular discoveries into actionable insights that can be applied in the field of translational and precision medicine to save life. Support vector machine ; Computer science ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Helene Fog Froriep Halberg | Anette Yde Holst | Niels Kaufmann | Rasmus Bro
Source: Journal of Chemometrics (article, 2021-05-14)
Field: Chemistry — Analytical Chemistry

Calibration model fusion Abstract Calibration model maintenance is often overlooked but is a significant part of successful use of multivariate calibration models, for example, in process monitoring and optimization. In some cases, companies are maintaining tens or even hundreds of calibration models. This could be partial least squares (PLS) calibration models pertaining to different recipes or raw materials or neural network based models covering different production sites. Maintaining such a high number of models is cumbersome and expensive. Sometimes, a solution presented for this problem is to merge all the models into one, but this often comes at the expense of significantly higher prediction errors. In this paper, we suggest a new approach for rationally merging calibration models in order to optimally balance the prediction error and maintenance workload. We do this by systematically merging models that lower the error as much as possible and hence provide a sort of optimal clustering or fusion of calibration models. We showcase the new approach on a case based on infrared spectroscopy applied to dairy production. Calibration ; Merge (version control) ; Computer science ; Cluster analysis ; Chemometrics ; Partial least squares regression ; Artificial neural network ; Data mining ; Machine learning ; Artificial intelligence ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jianxiao Hong
Source: IOP Conference Series Earth and Environmental Science (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

System Identification of ZnO Nanostructure Based On Physical Field Analysis and Deep Learning Abstract Zinc oxide nanowire is a widely used one-dimensional nanomaterial. The topological structure of nanowire determines its performance. At present, many researches have made on the growth conditions and structural parameters of zinc oxide nanowires through experimental methods. However, there is a lack of quantitative research on the correlation between macro physical fields and the parameter of structure. In this paper, we propose NPPN (Nanostructure-Parameter Prediction Network) based on deep learning algorithms to explore the correlation between physical fields and nanostructure parameters. The dataset is firstly created by experimental results and CFD simulation. We carry out large batch of preparation experiment of zinc oxide nanowires using the KQS automated machine based on the low-temperature two-step hydrothermal method. Meanwhile, we simulate the real physical field distribution of the reaction chamber through CFD software. The results show that the NPPN model has good predictive performance for the structural parameters of zinc oxide nanowires. Nanowire ; Nanostructure ; Materials science ; Zinc ; Nanotechnology ; Nanomaterials ; Hydrothermal circulation ; Field (mathematics) ; Topology (electrical circuits) ; Computer science ; Engineering ; Mathematics ; Metallurgy ; Chemical engineering ; Electrical engineering ZnO doping and properties ; Machine Learning and ELM ; Gas Sensing Nanomaterials and Sensors
Authors: Cats, Peter | Kuipers, Sander | De Wind, Sacha | Van Damme, Robin | Coli, Gabriele M. | Dijkstra, Marjolein | Van Roij, René | Soft Condensed Matter and Biophysics | Theoretical Physics | Sub Cond-Matter Theory, Stat & Comp Phys | Sub Algemeen Theoretical Physics | Sub Soft Condensed Matter
Source: Utrecht University Repository (Utrecht University) (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Machine-learning free-energy functionals using density profiles from simulations The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneously and consistently describing thermodynamic and structural properties of interacting many-body systems in arbitrary external potentials. In practice, however, DFT hinges on approximate (free-)energy functionals from which density profiles (and hence the thermodynamic potential) follow via an Euler-Lagrange equation. Here, we explore a relatively simple Machine-Learning (ML) approach to improve the standard mean-field approximation of the excess Helmholtz free-energy functional of a 3D Lennard-Jones system at a supercritical temperature. The learning set consists of density profiles from grand-canonical Monte Carlo simulations of this system at varying chemical potentials and external potentials in a planar geometry only. Using the DFT formalism, we nevertheless can extract not only very accurate 3D bulk equations of state but also radial distribution functions using the Percus test-particle method. Unfortunately, our ML approach did not provide very reliable Ornstein-Zernike direct correlation functions for small distances. Materials science ; Energy (signal processing) ; Density functional theory ; Engineering physics ; Chemical physics ; Condensed matter physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: A.A. Adly | Salwa K. Abd–El–Hafiz
Source: AIP Advances (article, 2021-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Utilizing four-node tetrahedra-shaped Hopfield neural network configurations in the local magnetization assessment of 3d objects exhibiting hysteresis There is no doubt that the accurate assessment of local magnetization in three-dimensional objects exhibiting hysteresis is crucial to accurate performance estimation of a variety of electromagnetic devices. Recently, it has been demonstrated that a Stoner-Wohlfarth-like elementary hysteresis operator may be constructed using two-node Hopfield neural network (HNN) having internal positive feedback. Based upon the previously mentioned approach, this paper presents a methodology using which local magnetization in 3D objects exhibiting hysteresis may be assessed. The approach utilizes a four-node tetrahedron-shaped HNN with activation functions constructed using a weighted superposition of a step and sigmoidal functions in accordance with the M − H curve of the material under consideration. In this approach, the internal feedback factors between the different nodes for any tetrahedron are dependent on its geometrical configuration. Hence, shape configuration effects on the magnetization patterns of any three-dimensional object approximated by an ensemble of tetrahedra are implicitly taken into consideration. To demonstrate the applicability of the proposed approach, computations were carried out for different three-dimensional magnetized bodies having different M − H curves. Theoretical and computational details of the approach are given in the paper. Tetrahedron ; Magnetization ; Hysteresis ; Superposition principle ; Node (physics) ; Artificial neural network ; Computation ; Sigmoid function ; Computer science ; Magnetic hysteresis ; Physics ; Algorithm ; Topology (electrical circuits) ; Mathematics ; Condensed matter physics ; Mathematical analysis ; Artificial intelligence ; Magnetic field ; Acoustics ; Geometry ; Quantum mechanics Magnetic Properties and Applications ; Magnetic properties of thin films ; Neural Networks and Applications