jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arijit Sehanobish | Héctor H. Corzo | Onur Kara | David van Dijk
Source: arXiv (Cornell University) (preprint, 2020-06-23)
Field: Materials Science — Materials Chemistry

Learning Potentials of Quantum Systems using Deep Neural Networks Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature. Particularly, the use of deep NNs for understanding complex physical and chemical phenomena has opened a new niche of science where the analysis tools from Machine Learning (ML) are combined with the computational concepts of the natural sciences. Reports from this unification of ML have presented evidence that NNs can learn classical Hamiltonian mechanics. This application of NNs to classical physics and its results motivate the following question: Can NNs be endowed with inductive biases through observation as means to provide insights into quantum phenomena? In this work, this question is addressed by investigating possible approximations for reconstructing the Hamiltonian of a quantum system in an unsupervised manner by using only limited information obtained from the system's probability distribution. Unification ; Computer science ; Artificial neural network ; Artificial intelligence ; Hamiltonian (control theory) ; Quantum ; Deep neural networks ; Deep learning ; Theoretical computer science ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Xiaolong He
Source:  (preprint, 2020-09-22)
Field: Materials Science — Materials Chemistry

Deep Autoencoders for Physics-Constrained Data-Driven Nonlinear Materials Modeling Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the classical constitutive model construction.However, it remains difficult to deal with high-dimensional applications and extrapolative generalization.This paper introduces deep learning techniques under the data-driven framework to address these fundamental issues in nonlinear materials modeling.To this end, an autoencoder neural network architecture is introduced to learn the underlying low-dimensional representation (embedding) of the given material database.The offline trained autoencoder and the discovered embedding space are then incorporated in the online data-driven computation such that the search of optimal material state from database can be performed on a low-dimensional space, aiming to enhance the robustness and predictability with projected material data.To ensure numerical stability and representative constitutive manifold, a convexity-preserving interpolation scheme tailored to the proposed autoencoder-based data-driven solver is proposed for constructing the material state.In this study, the applicability of the proposed approach is demonstrated by modeling nonlinear biological tissues.A parametric study on data noise, data size and sparsity, training initialization, and model architectures, is also conducted to examine the robustness and convergence property of the proposed approach. Nonlinear system ; Computer science ; Physics ; Artificial intelligence ; Data science ; Quantum mechanics Machine Learning in Materials Science
Authors: Gulrukh Turabee | Muhammad Raza Khowja | Vincenzo Madonna | Paolo Giangrande | Gaurang Vakil | Chris Gerada | Michael Galea
Source:  (article, 2020-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal Lifetime Evaluation of Electrical Machines Using Neural Network This paper proposes a surrogate approach which utilises an supervised neural network to significantly shorten the time required for thermal qualification of electrical machines' insulation. The proposed approach is based on a feedforward neural network trained with Bayesian Regularization Back-Propagation (BRP) algorithm. The network predicts the winding's insulation resistance trend with respect to its thermal aging time. The predicted insulation resistance is evaluated against experimental measurements and an excellent match is found. Its trend is used for estimating the sample's time to failure under thermal stress at various temperatures. The temperature index of the insulating material, predicted by the neural network, matches with an error of just 0.4% margin against the experimental findings. Artificial neural network ; Backpropagation ; Margin (machine learning) ; Computer science ; Thermal resistance ; Thermal ; Probabilistic neural network ; Machine learning ; Artificial intelligence ; Engineering ; Time delay neural network Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Thermal Analysis in Power Transmission
Authors: Predrag Milanović | Snežana Stanković | Milada Novaković | Dragana Grujić | Mirjana Kostić | Jovana Milanović
Source: PLoS ONE (article, 2020-11-16)
Field: Materials Science — Polymers and Plastics

Development of the automated software and device for determination of wicking in textiles using open-source tools The development of automated software and the device for determination of wicking of textile materials, using open-source ImageJ libraries for image processing, and newly designed additional algorithm for the determination of threshold, is presented in this paper. The description of the device, design of the open-source software "Kapilarko", as well as an explanation of the steps: image processing, threshold determination and reading of wicking height, are provided. We have also investigated the possibility of using the artificial neural networks for automatic recognition of the wicking height. The results showed that the recognition of the wet area of the sample, based on the application of artificial neural networks was in a very good agreement with the experimental data. The device's utility for the measurement of wicking ability of textile materials was proved by testing various knitted fabrics. The constructed device has the advantages of providing automated measurement and minimization of the subjective errors of the operators; extremely fast or long-term measurements; digital recording of results; consistency of experimental conditions; possibility of using water instead of colors and, last but not least, low cost of the device. Considering the importance and frequent measurements of wicking ability of textile materials, the advantages of the presented device, as well as the fact that commercial software without publishing the source-code, are used for most of the available devices, we believe that our idea to design the automated software and device by applying the "open-source" approach, will be of benefit to scientists and engineers in using or improving wicking experiments. Software ; Computer science ; Source code ; Consistency (knowledge bases) ; Textile ; Open source ; Artificial neural network ; Image processing ; Artificial intelligence ; Computer hardware ; Materials science ; Image (mathematics) ; Operating system Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color Science and Applications
Authors: Emily Margaret Gould | Katherine A. Macmillan | Paul S. Clegg
Source: Soft Matter (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Autonomous analysis to identify bijels from two-dimensional images Bicontinuous interfacially jammed emulsion gels (bijels) are novel composite materials that can be challenging to manufacture. As a step towards automating production, we have developed a machine learning tool to classify fabrication attempts. We use training and testing data in the form of confocal images from both successful and unsuccessful attempts at bijel fabrication. We then apply machine learning techniques to this data in order to classify whether an image is a bijel or a non-bijel. Our principal approach is to process the images to find their autocorrelation function and structure factor, and from these functions we identify variables that can be used for training a supervised machine learning model to identify a bijel image. We are able to categorise images with reasonable accuracies of 85.4% and 87.5% for two different approaches. We find that using both the liquid and particle channels helps to achieve optimal performance and that successful classification relies on the bijel samples sharing a characteristic length scale. Our second approach is to classify the shapes of the liquid domains directly; the shape descriptors are then used to classify fabrication attempts via a decision tree. We have used an adaptive design approach to find an image pre-processing step that yields the optimal classification results. Again, we find that the characteristic length scale of the images is crucial in performing the classification. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Computer vision Pickering emulsions and particle stabilization ; Image Processing and 3D Reconstruction ; Cultural Heritage Materials Analysis
Authors: David J. Ponting | Ruud van Deursen | Martin A. Ott
Source: Journal of Chemical Information and Modeling (article, 2020-09-23)
Field: Chemistry — Organic Chemistry

Machine Learning Predicts Degree of Aromaticity from Structural Fingerprints Prediction of whether a compound is "aromatic" is at first glance a relatively simple task-does it obey Hückel's rule (planar cyclic π-system with 4n + 2 electrons) or not? However, aromaticity is far from a binary property, and there are distinct variations in the chemical and biological behavior of different systems which obey Hückel's rule and are thus classified as aromatic. To that end, the aromaticity of each molecule in a large public dataset was quantified by an extension of the work of Raczyńska et al. Building on this data, a method is proposed for machine learning the degree of aromaticity of each aromatic ring in a molecule. Categories are derived from the numeric results, allowing the differentiation of structural patterns between them and thus a better representation of the underlying chemical and biological behavior in expert and (Q)SAR systems. Aromaticity ; Representation (politics) ; Ring (chemistry) ; Degree (music) ; Computer science ; Extension (predicate logic) ; Simple (philosophy) ; Molecule ; Binary number ; Chemistry ; Computational chemistry ; Biological system ; Mathematics ; Physics ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Xiang Gao | Lefan Wang | Leijiang Yao
Source: IOP Conference Series Materials Science and Engineering (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Porosity Prediction of Ceramic Matrix Composites Based on Random Forest Abstract Porosity is an important characteristic attribute of carbon fiber reinforced ceramic matrix composites (CMCs), which is closely related to material properties and directly affects the application range and prospect of composite materials. For the purpose of design optimization in Material Genetic Engineered (MGE), the porosity prediction method based on machine learning technology was proposed to provide material attribute data for propulsion material performance prediction. This study collect CMCs experimental data from papers and Materials Genome Engineering Databases. According to the composition of the materials and the characteristics of the preparation process, 9 influencing factors and 1674 experimental data were selected to establish random forest regression (RFR)and compared with support vector regression (SVR). Using R 2 scores and root mean squared error as model evaluation indicators, R 2 scores respectively are 0.751 and 0.927 in SVR and RFR, root mean squared error respectively are 0.0062 and 0.0019. The results of the study show that the RFR model predictions are well matched with the experimental values. Random forest ; Porosity ; Support vector machine ; Ceramic matrix composite ; Ceramic ; Materials science ; Composite material ; Matrix (chemical analysis) ; Mean squared error ; Material Design ; Computer science ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Advanced ceramic materials synthesis
Authors: Steven B. Torrisi | Matthew R. Carbone | Brian A. Rohr | Joseph H. Montoya | Yang Ha | Junko Yano | Santosh K. Suram | Linda Hung
Source: ChemRxiv (preprint, 2020-02-20)
Field: Materials Science — Materials Chemistry

Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra. However, machine learning models are often difficult to interpret, making it challenging to determine when they are valid and whether they are consistent with physical theories. In this work, we present three main advances to the data-driven analysis of XAS spectra: we demonstrate the efficacy of random forests in solving two new property determination tasks (predicting Bader charge and mean nearest neighbor distance), we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties, and we address the effect of normalization on model interpretability. The multiscale featurization transforms the spectrum into a vector of polynomial-fit features, and is contrasted with the commonly-used "pointwise" featurization that directly uses the entire spectrum as input. We find that across thousands of transition metal oxide spectra, the relative importance of features describing the curvature of the spectrum can be localized to individual energy ranges, and we can separate the importance of constant, linear, quadratic, and cubic trends, as well as the white line energy. This work has the potential to assist rigorous theoretical interpretations, expedite experimental data collection, and automate analysis of XAS spectra, thus accelerating discovery of new functional materials. Pointwise ; Interpretability ; X-ray absorption spectroscopy ; Spectral line ; Artificial intelligence ; Support vector machine ; Computer science ; Machine learning ; Random forest ; Absorption spectroscopy ; Statistical physics ; Algorithm ; Mathematics ; Physics ; Optics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Radiomics and Machine Learning in Medical Imaging
Authors: Shrouq H. Aleithan | Doaa Mahmoud‐Ghoneim
Source: Scientific Reports (article, 2020-11-26)
Field: Materials Science — Materials Chemistry

Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks The need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS<sub>2</sub> and WS<sub>2</sub>, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS<sub>2</sub> and WS<sub>2</sub>, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class. Texture (cosmology) ; Layer (electronics) ; Monolayer ; Computer science ; Artificial neural network ; Nanomaterials ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Image (mathematics) ; Nanotechnology 2D Materials and Applications ; Graphene research and applications ; Quantum Dots Synthesis And Properties
Authors: Alex Beatson | Jordan T. Ash | Geoffrey Roeder | Tianju Xue | Ryan P. Adams
Source: Rare & Special e-Zone (The Hong Kong University of Science and Technology) (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Learning Composable Energy Surrogates for PDE Order Reduction Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour–whether electromagnetic, thermal, or mechanical–arises from modular substructure. Simulation and optimization of these materials are computationally challenging, as rich substructures necessitate high-fidelity finite element meshes to solve the governing PDEs. To address this, we leverage parametric modular structure to learn component-level surrogates, enabling cheaper high-fidelity simulation. We use a neural network to model the stored potential energy in a component given boundary conditions. This yields a structured prediction task: macroscopic behavior is determined by the minimizer of the system’s total potential energy, which can be approximated by composing these surrogate models. Composable energy surrogates thus permit simulation in the reduced basis of component boundaries. Costly ground-truth simulation of the full structure is avoided, as training data are generated by performing finite element analysis of individual components. Using dataset aggregation to choose training data allows us to learn energy surrogates which produce accurate macroscopic behavior when composed, accelerating simulation of parametric meta-materials. Computer science ; Modular design ; Finite element method ; Leverage (statistics) ; Component (thermodynamics) ; Polygon mesh ; Parametric statistics ; Substructure ; Artificial neural network ; Computational science ; Artificial intelligence ; Engineering ; Mathematics ; Structural engineering Material Selection and Properties ; Machine Learning in Materials Science ; Chemical and Physical Properties of Materials
Authors: Fatemeh Ashrafi | Mahmoud Reza Sohrabi | Mahmoud Firouzzare | Seyed Javad Ahmadi | Morteza Khosravi
Source: International Journal of Environmental & Analytical Chemistry (article, 2020-06-05)
Field: Chemistry — Inorganic Chemistry

Polypropylene nanofibre behaviour modelling for uranium adsorption by forcespinning method: artificial neural network modelling In this study, a handmade device was developed to prepare polymeric polypropylene (PP) nanofibres by forcespinning method. The characterisations of the prepared adsorbent were investigated by Fourier transform infrared spectroscopy (FTIR) and Scanning electron microscopy (SEM). Then, the performance of the prepared nanofibres was investigated as an adsorbent for the removal of uranium (VI) ion from the aqueous solution. Experimental parameters such as contact time and pH were optimised. The maximum adsorption was obtained with the adsorbent weight of 0.03 g, contact time of 60 min, and pH of 4. This study showed that PP nanofibre is an efficient adsorbent for uranium removal from aqueous solution. Also, the uranium removal process was modelled by artificial neural network (ANN) for predicting capacity removal. In this method, feed-forward back-propagation learning with Levenberg-Marquardt (LM), adaptive learning rate back-propagation (GDX) and resilient back-propagation (RP) algorithms were used. Adsorption ; Polypropylene ; Aqueous solution ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Materials science ; Uranium ; Chemical engineering ; Nuclear chemistry ; Backpropagation ; Artificial neural network ; Composite material ; Chemistry ; Computer science ; Metallurgy ; Machine learning ; Organic chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes
Authors: Sabine M. Neumayer | Michael A. Susner | Michael A. McGuire | Sokrates T. Pantelides | Sergiy Kalnaus | Petro Maksymovych | Nina Balke
Source: IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

Lowering of <i>T</i>c in Van Der Waals Layered Materials Under In-Plane Strain The dependence of electromechanical behavior on strain in ferroelectric materials can be leveraged as parameter to tune ferroelectric properties such as the Curie temperature. For van der Waals materials, a unique opportunity arises because of wrinkling, bubbling, and Moiré phenomena accessible due to structural properties inherent to the van der Waals gap. Here, we use piezoresponse force microscopy and unsupervised machine learning methods to gain insight into the ferroelectric properties of layered CuInP<sub>2</sub>S<sub>6</sub> where local areas are strained in-plane due to a partial delamination, resulting in a topographic bubble feature. We observe significant differences between strained and unstrained areas in piezoresponse images as well as voltage spectroscopy, during which strained areas show a sigmoid-shaped response usually associated with the response measured around the Curie temperature, indicating a lowering of the Curie temperature under tensile strain. These results suggest that strain engineering might be used to further increase the functionality of CuInP<sub>2</sub>S<sub>6</sub> through locally modifying ferroelectric properties on the micro- and nanoscale. van der Waals force ; Piezoresponse force microscopy ; Ferroelectricity ; Curie temperature ; Materials science ; Condensed matter physics ; Optoelectronics ; Dielectric ; Chemistry ; Physics ; Ferromagnetism Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies