jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Refaldi Intri Dwi Putra | Arifin Luthfi Maulana | Adhitya Gandaryus Saputro
Source: AIP conference proceedings (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Study on building machine learning model to predict biodegradable-ready materials A material could be harmful to the environment if it does not decay for a long period of time. Regulations such as European REACH are also established to ensure the products from manufacturers have information about biodegradability. However, not all the products have the biodegradation information since testing it all requires time and efforts. QSARs (Quantitative Structure-Activity Relationships) model is encouragingly used to predict the classification of biodegradation for a chemical compound. QSARs model could be extracted from experimental result or by material design simulation. In this study, artificial neural network (ANN) and support vector machine (SVM) model were built to predict ready-biodegradation of a chemical compound. The model was built based on dataset that had been used by Mansouri et al (Kamel Mansouri et al, 2013) on their study. Dataset consisted of 1055 chemical compounds and 41 molecular descriptions for each chemical compound. During building machine learning model, dataset were randomly separated into 791 data for training dataset and 294 data for testing dataset. Scaler was used to standardize the data and since the data consists of multi features, it was reduced by applying 4 principal component analysis (PCA). ANN was built based on many layer perceptrons, several activation functions and solvers were used to find the optimum model. SVM was also built with optimum parameters. The result was the models could classify the biodegradability very well and have quite high accuracy prediction. Support vector machine ; Computer science ; Perceptron ; Artificial neural network ; Biodegradation ; Artificial intelligence ; Machine learning ; Principal component analysis ; Multilayer perceptron ; Data mining ; Chemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Water Quality Monitoring and Analysis
Authors: V. Meenakshisundaram | Jui‐Hsiang Hung | David Simmons
Source: Soft Matter (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Design rules for glass formation from model molecules designed by a neural-network-biased genetic algorithm The glass transition - an apparent amorphous solidification process - is a central feature of the physical properties of soft materials such as polymers and colloids. A key element of this phenomenon is the observation of a broad spectrum of deviations from an Arrhenius temperature of dynamics in glass-forming liquids, with the extent of deviation quantified by the "fragility" of glass formation. The underlying origin of "fragile" glass formation and its dependence on molecular structure remain major open questions in condensed matter physics and soft materials science. Here we employ molecular dynamics simulations, together with a neural-network-biased genetic algorithm, to design and study model rigid molecules spanning a broad range of fragilities of glass formation. Results indicate that fragility of glass formation can be controlled by tuning molecular asphericity, with extended molecules tending to exhibit low fragilities and compact molecules tending toward higher fragilities. The glass transition temperature itself, on the other hand, correlates well with high-temperature activation behavior and with density. These results point the way towards rational design of glass-forming liquids spanning a range of dynamical behavior, both via these physical insights and via future extensions of this evolutionary design strategy to real chemistries. Finally, we show that results compare well with predictions of the nonlinear Langevin theory of liquid dynamics, which is a precursor of the more recently developed elastically collective nonlinear Langevin equation theory of Mirigian and Schweizer, identifying this framework as a promising basis for molecular design of the glass transition. Fragility ; Artificial neural network ; Glass transition ; Genetic algorithm ; Algorithm ; Statistical physics ; Molecule ; Materials science ; Biological system ; Computer science ; Thermodynamics ; Artificial intelligence ; Chemistry ; Physics ; Machine learning ; Polymer ; Composite material ; Organic chemistry ; Biology Material Dynamics and Properties ; Glass properties and applications ; Liquid Crystal Research Advancements
Authors: Shengwen Bai | Gang Fang | Jie Zhou
Source: Journal of Materials Processing Technology (article, 2019-08-10)
Field: Materials Science — Biomaterials

Construction of three-dimensional extrusion limit diagram for magnesium alloy using artificial neural network and its validation Extrusion ; Die swell ; Materials science ; Magnesium alloy ; Metallurgy ; Die (integrated circuit) ; Alloy ; Mechanical engineering ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Zhilei Wang | Yoshitaka Adachi | Zhong‐Chun Chen
Source: Advanced Theory and Simulations (article, 2019-11-19)
Field: Materials Science — Materials Chemistry

Processing Optimization and Property Predictions of Hot‐Extruded Bi–Te–Se Thermoelectric Materials via Machine Learning Abstract Traditional experiment‐based materials research is becoming increasingly insufficient to thoroughly understand materials’ characteristics and thus it is becoming difficult to develop novel materials with better performance. Machine learning is applied to hot‐extruded Cu x Bi 2 Te 2.85+ y Se 0.15 thermoelectric materials, and the relationships between the processing, microstructure, and properties are further investigated via a data‐driven approach. A properties‐to‐microstructure‐to‐processing inverse analysis is proposed and performed by a genetic algorithm. The analysis results indicate that hot‐extruded materials have a potential best figure of merit ( ZT ) value of 1.15, which is 1.32 times larger than their best experimental value (0.87). To obtain this optimal property, processing variables such as higher extrusion temperature and larger Cu content and microstructure with a larger average grain size and higher density are required. The proposed data‐driven approach is expected to provide a new avenue for designing high‐performance Bi–Te–Se thermoelectric materials and thus to accelerate discoveries of novel materials. Microstructure ; Thermoelectric effect ; Materials science ; Extrusion ; Thermoelectric materials ; Grain size ; Figure of merit ; Inverse ; Property (philosophy) ; Process engineering ; Mechanical engineering ; Computer science ; Engineering physics ; Composite material ; Optoelectronics ; Thermodynamics ; Mathematics ; Physics ; Geometry ; Engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Kevin Rossi | James Cumby
Source: International Journal of Quantum Chemistry (article, 2019-12-27)
Field: Materials Science — Materials Chemistry

Representations and descriptors unifying the study of molecular and bulk systems Abstract Establishing a unified framework for describing the structures of molecular and periodic systems is a long‐standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need for such a method. This perspective aims to discuss the development and use of three promising approaches—topological, atom‐density, and symmetry‐based—for the prediction and rationalization of physical, chemical, and mechanical properties of atomistic systems across different scales and compositions. Rationalization (economics) ; Perspective (graphical) ; Nanotechnology ; Statistical physics ; Theoretical physics ; Computer science ; Chemical physics ; Physics ; Materials science ; Artificial intelligence ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Lee A. Griffin | Iaroslav Gaponenko | Shujun Zhang | Nazanin Bassiri‐Gharb
Source: npj Computational Materials (article, 2019-08-12)
Field: Materials Science — Materials Chemistry

Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking Abstract Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data, the interplay between the physical and chemical phenomena underpinning materials’ functionalities is still often poorly understood. Dimensional reduction techniques have been used to tackle the challenge of understanding materials’ behavior, leveraging the very large amount of data available. Here, we present a method for applying physical and chemical constraints to dimensional reduction analysis, through dimensional stacking. Compared to traditional, uncorrelated techniques, this approach enables a direct and simultaneous assessment of behaviors across all measurement parameters, through stacking of data along specific dimensions as required by physical or chemical correlations. The proposed method is applied to the nanoscale electromechanical relaxation response in (1 − x )PMN- x PT solid solutions, enabling a direct comparison of electric field- and chemical composition-dependent contributors. A poling-like, and a relaxation-like behavior with a domain glass state are identified, and their evolution is tracked across the phase diagram. The proposed dimensional stacking technique, guided by the knowledge of the underlying physics of correlated systems, is valid for the analysis of any multidimensional dataset, opening a spectrum of possibilities for multidisciplinary use. Stacking ; Computer science ; Reduction (mathematics) ; Artificial intelligence ; Relaxation (psychology) ; Field (mathematics) ; Machine learning ; Domain (mathematical analysis) ; Dimensionality reduction ; Materials science ; Nanotechnology ; Physics ; Mathematics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Hyungro Lee | Matteo Turilli | Shantenu Jha | Debsindhu Bhowmik | Heng Ma | Arvind Ramanathan
Source:  (preprint, 2019-11-01)
Field: Materials Science — Materials Chemistry

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding The following topics are dealt with: learning (artificial intelligence); convolutional neural nets; neural nets; parallel processing; stochastic processes; graphics processing units; message passing; parallel machines; feature extraction; face recognition. Molecular dynamics ; Computer science ; Folding (DSP implementation) ; Workflow ; Curse of dimensionality ; Protein folding ; Scaling ; Computational science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: S Remya | R. Sasikala
Source: Soft Computing (article, 2019-04-05)
Field: Materials Science — Polymers and Plastics

RETRACTED ARTICLE: Classification of rubberized coir fibres using deep learning-based neural fuzzy decision tree approach Coir ; Decision tree ; Artificial intelligence ; Computer science ; Artificial neural network ; Tree (set theory) ; Fuzzy logic ; Machine learning ; Materials science ; Mathematics ; Composite material Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Esmail Sharifzadeh
Source: Chinese Journal of Polymer Science (article, 2019-06-04)
Field: Materials Science — Polymers and Plastics

Modeling of the Tensile Strength of Immiscible Binary Polymer Blends Considering the Effects of Polymer/Polymer Interface and Morphological Variation Ultimate tensile strength ; Materials science ; Polymer ; Composite material ; Tensile testing ; Binary number ; Polymer blend ; Yield (engineering) ; Artificial neural network ; Work (physics) ; Biological system ; Computer science ; Thermodynamics ; Copolymer ; Mathematics ; Physics ; Artificial intelligence Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Rheology and Fluid Dynamics Studies
Authors: Andrius Laucka | Vaida Adaškevičiūtė | Darius Andriukaitis
Source: Symmetry (article, 2019-06-27)
Field: Chemistry — Analytical Chemistry

Research of the Equipment Self-Calibration Methods for Different Shape Fertilizers Particles Distribution by Size Using Image Processing Measurement Method The fourth digital revolution of industry makes substantive changes to the rate and methodology of work performance. Machines and robots do the majority of work in robotized and automated factories, while people only supervise them. After an increase of production efficiency, quality control became a critical point. Therefore, quality control systems of computer visions are increasingly installed. The branch of chemical industry requires measurements of quality at as great a frequency as possible. Consequently, indirect measurements are effectively used at this point. This research presents the method of indirect particle measurement. Particles are measured using digital image processing. The algorithm is used for particle measurement to automatically adjust the measurement results. Numerical intelligence is added to the algorithm to increase the accuracy of correction results. The research deals with the problem of matching the results of indirect measurements and the results of the control equipment. For data analysis, fertilizer diameter, mean diameter, aspect ratio, symmetry, sphericity, convexity and some other parameters are used. The mismatch of the artificial neural network results with the control equipment results is slightly higher than 1%. Sphericity ; Computer science ; Artificial neural network ; Robot ; Digital image processing ; Image processing ; Artificial intelligence ; Algorithm ; Computer vision ; Mathematics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Scientific Research Methods
Authors: Xingchen Dong | Ali K. Yetisen | Michael H. Köhler | Jie Dong | Shengjia Wang | Martin Jakobi | Xiaoxing Zhang | Alexander W. Koch
Source: Advanced Optical Materials (article, 2019-05-23)
Field: Materials Science — Materials Chemistry

Microscale Spectroscopic Mapping of 2D Optical Materials Abstract 2D materials have unique optical properties due to their ultrathin layered structures, and are emerging as promising materials for optoelectronic devices. To characterize and evaluate these properties, diffraction‐limited spectroscopic mapping, also known as microscale spectroscopic imaging, has been developed to provide abundant spatial and spectral information. The usefulness of microscale spectroscopic mapping for unique property study of 2D optical materials is addressed. Advances in both mature and growing microscale spectroscopic mapping setups and methods covering broad ranges of the electromagnetic spectrum are introduced. Different forms of graphene, molybdenum disulphide (MoS 2 ), and black phosphorus, including intrinsic and engineered structures made by different methods, are selected as varied representatives of 2D optical materials from the perspective of bandgap and anisotropy. A detailed discussion of microscale spectroscopic images and their unique optical property findings, including spatial adsorption and emission, excitonic behavior, light sensitivity, and plasmonic effects, is then given to demonstrate the advantages of spectral mapping over conventional point spectroscopy. The analysis also provides a generic view of microscale spectroscopic mapping selection in 2D‐materials imaging. Furthermore, the challenges inherent in large‐scale spectral data processing are explored, and the utilization of deep learning is suggested for possible application in academica and industry. Microscale chemistry ; Materials science ; Characterization (materials science) ; Nanotechnology ; Plasmon ; Graphene ; Spectroscopy ; Optoelectronics ; Optics ; Physics 2D Materials and Applications ; Graphene research and applications ; Quantum Dots Synthesis And Properties
Authors: Rafael Garcia‐Dias | Carlos Allende Prieto | J. Sánchez Alméida | P. A. Palicio
Source: Astronomy and Astrophysics (article, 2019-07-30)
Field: Chemistry — Analytical Chemistry

Machine learning in APOGEE Context. The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. In these exploratory applications, it is important to evaluate potential existing tools and determine those that are optimal for extracting scientific knowledge from the available observations. Aims. We explore the possibility of using unsupervised clustering algorithms to separate stellar populations with distinct chemical patterns. Methods. Star clusters are likely the most chemically homogeneous populations in the Galaxy, and therefore any practical approach to identifying distinct stellar populations should at least be able to separate clusters from each other. We have applied eight clustering algorithms combined with four dimensionality reduction strategies to automatically distinguish stellar clusters using chemical abundances of 13 elements. Our test-bed sample includes 18 stellar clusters with a total of 453 stars. Results. We have applied statistical tests showing that some pairs of clusters (e.g., NGC 2458–NGC 2420) are indistinguishable from each other when chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) are used. However, for most clusters we are able to automatically assign membership with metric scores similar to previous works. The confusion level of the automatically selected clusters is consistent with statistical tests that demonstrate the impossibility of perfectly distinguishing all the clusters from each other. These statistical tests and confusion levels establish a limit for the prospect of blindly identifying stars born in the same cluster based solely on chemical abundances. Conclusion. We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data. Even though we are not able to fully separate the clusters from each other, the main confusion arises from clusters with similar ages. Because some stellar clusters are chemically indistinguishable, our study supports the notion of extending weak chemical tagging that involves families of clusters instead of individual clusters. Cluster analysis ; Star cluster ; Stars ; Metric (unit) ; Computer science ; Cluster (spacecraft) ; Galaxy ; Open cluster ; Pattern recognition (psychology) ; Astrophysics ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality ; Stellar, planetary, and galactic studies
Authors: Maksim Beliaev | Dana Zöllner | Alexandra Pacureanu | Paul Zaslansky | Luca Bertinetti | Igor Zlotnikov
Source: Journal of Structural Biology (article, 2019-12-06)
Field: Materials Science — Biomaterials

Quantification of sheet nacre morphogenesis using X-ray nanotomography and deep learning Synchrotron ; Image resolution ; Nanotechnology ; Biological system ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Optics ; Biology Calcium Carbonate Crystallization and Inhibition ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Henry Cox | Meiwen Cao | Hai Xu | Thomas Andrew Waigh | Jian R. Lu
Source: Biomacromolecules (article, 2019-03-13)
Field: Materials Science — Biomaterials

Active Modulation of States of Prestress in Self-Assembled Short Peptide Gels Peptide hydrogels are excellent candidates for medical therapeutics due to their tuneable viscoelastic properties, however, in vivo they will be subject to various osmotic pressures, temperature changes, and biological co-solutes, which could alter their performance. Peptide hydrogels formed from the synthetic peptide I<sub>3</sub>K have a temperature-induced hardening of their shear modulus by a factor of 2. We show that the addition of uncross-linked poly( N-isopropylacrylamide) chains to the peptide gels increases the gels' temperature sensitivity by 3 orders of magnitude through the control of osmotic swelling and cross-linking. Using machine learning combined with single-molecule fluorescence microscopy, we measured the modulation of states of prestress in the gels on the level of single peptide fibers. A new self-consistent mixture model was developed to simultaneously quantify the energy and the length distributions of the states of prestress. Switching the temperature from 20 to 40 °C causes 6-fold increases in the number of states of prestress. At the higher temperature, many of the fibers experience constrained buckling with characteristic small wavelength oscillations in their curvature. Self-healing hydrogels ; Peptide ; Swelling ; Viscoelasticity ; Materials science ; Curvature ; Lower critical solution temperature ; Biophysics ; Chemistry ; Chemical engineering ; Polymer chemistry ; Polymer ; Composite material ; Copolymer ; Biochemistry Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Proteoglycans and glycosaminoglycans research
Authors: Yuan Li | Yin Zhang | Wenbo Zhang | Yao-Yu Xu | Guanjun Zhang
Source: Measurement Science and Technology (article, 2019-12-06)
Field: Chemistry — Analytical Chemistry

Comparative study of partial least squares and neural network models of near-infrared spectroscopy for aging condition assessment of insulating paper Abstract Near-infrared spectroscopy (NIRS) is a rapid and non-destructive detection method for component determination and quantitative analysis with broad applications in numerous fields. In recent years, NIRS has started to be used in the aging condition assessment of power transformers. However, the real applications of NIRS are constrained by the lack of evaluation database and accurate prediction algorithms. In this paper, we aim at comparing different NIRS modeling methods and improving diagnostic accuracy. We build the evaluation database via the preparation of 230 specimens derived from three typical types of insulating paper. Calibration models are established by linear method-partial least squares (PLS) and nonlinear method-back propagation neural network (BPNN) to map the relationship between spectra and the degree of polymerization (DP). The DP prediction results show that using full NIR spectra as the input of the PLS model does not ensure a high prediction accuracy, and it is improved by competitive adaptive reweighted sampling (CARS) that selects the optimal wavelength combinations. Prediction precisions given by BPNN and CARS-BPNN models are shown to be less satisfactory than that of CARS-PLS. We process the original spectra with principal component analysis (PCA) as the input of BPNN and the PCA-BPNN model realizes high prediction precision for three types of paper (RMSE ⩽ 24, r = 0.99). With the identification of paper type by the k -nearest neighbors (KNN) method before prediction, the KNN-PCA-BPNN model solves the problem of the low prediction precision for mixed (unknown) paper samples (RMSE = 36, r = 0.98), which facilitates future field tests as well as related applications in practice. Partial least squares regression ; Principal component analysis ; Artificial neural network ; Near-infrared spectroscopy ; Computer science ; Calibration ; Pattern recognition (psychology) ; Mean squared error ; Artificial intelligence ; Statistics ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Power Transformer Diagnostics and Insulation