jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyu Wang | Hongzhao Fan | Dan Han | Yang Hong | Jingchao Zhang
Source: Nanotechnology (article, 2021-02-17)
Field: Materials Science — Materials Chemistry

Thermal boundary resistance at graphene-pentacene interface explored by a data-intensive approach As the machinery of artificial intelligence matures in recent years, there has been a surge in applying machine learning (ML) techniques for material property predictions. Artificial neural network (ANN) is a branch of ML and has gained increasing popularity due to its capabilities of modeling complex correlations among large datasets. The interfacial thermal transport plays a significant role in the thermal management of graphene-pentacene based organic electronics. In this work, the thermal boundary resistance (TBR) between graphene and pentacene is comprehensively investigated by classical molecular dynamics simulations combined with the ML technique. The TBR values along the<i>a</i>,<i>b</i>and<i>c</i>directions of pentacene at 300 K are 5.19 ± 0.18 × 10<sup>-8</sup>m<sup>2</sup>K W<sup>-1</sup>, 3.66 ± 0.36 × 10<sup>-8</sup>m<sup>2</sup>K W<sup>-1</sup>and 5.03 ± 0.14 × 10<sup>-8</sup>m<sup>2</sup>K W<sup>-1</sup>, respectively. Different architectures of ANN models are trained to predict the TBR between graphene and pentacene. Two important hyperparameters, i.e. network layer and the number of neurons are explored to achieve the best prediction results. It is reported that the two-layer ANN with 40 neurons each layer provides the optimal model performance with a normalized mean square error loss of 7.04 × 10<sup>-4</sup>. Our results provide reasonable guidelines for the thermal design and development of graphene-pentacene electronic devices. Pentacene ; Graphene ; Materials science ; Interfacial thermal resistance ; Artificial intelligence ; Artificial neural network ; Layer (electronics) ; Machine learning ; Electronics ; Thermal resistance ; Nanotechnology ; Thermal ; Computer science ; Thermodynamics ; Thin-film transistor ; Electrical engineering ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Zhiheng Huang
Source: Discover Materials (article, 2021-02-16)
Field: Materials Science — Materials Chemistry

Structural hierarchy from wavelet zoom and invariant construction Abstract During the past decade there have seen substantial progress being made on materials genome related research. However, coupling mechanisms across multi-scale microstructure and resulting consequences on property and performance of materials remain unsolved problems. Structural hierarchy, which was a concept developed but not quantitatively fulfilled in 1970s, is referred to as microstructure genome here and pinpoints the key enabler for materials genome engineering. Latest progress in deep learning for image recognition and understanding the underlying mathematical mechanisms have revealed the pivotal roles that directional wavelets and invariants play. Hierarchical invariants constructed by a wavelet system can provide an inherent descriptor for microstructure genome. Wavelet ; Hierarchy ; Artificial intelligence ; Zoom ; Invariant (physics) ; Computer science ; Genome ; Theoretical computer science ; Enabling ; Microstructure ; Pattern recognition (psychology) ; Mathematics ; Biology ; Materials science ; Psychology ; Paleontology ; Genetics ; Gene Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Genomics and Phylogenetic Studies
Authors: Nian Wu
Source: The Journal of Physical Chemistry C (article, 2021-08-26)
Field: Materials Science — Materials Chemistry

Feature Analysis: Revealing the Importance of Bulk Nearest-Neighbor Distance Feature for CO Adsorption Energy on Bimetallic Alloys An effective machine learning model FAIR (feature analysis based on linear regression) of high interpretation was proposed to predict CO adsorption energy on 105 kinds of bimetallic alloys with high accuracy (root-mean-square error (RMSE) 0.17 eV and R2 0.97). We assessed 524 288 kinds of combinations as input features based on 19 primary features from elemental features and surface electronic features of bimetallic alloys. The results demonstrated that the model not only displayed excellent prediction performance but also captured the hidden physical meaning, implying that the distance between nearest atoms in the bulk of the element in the first layer is the most important feature that determines the accuracy of models; in addition, the emergence of d band center also tends to make some improvements. Furthermore, the FAIR model could be independent of massive amount of database, when the number of train database is far less than that of test database with the ratio of only 3:7, the RMSE still reached 0.13 eV. Taking CO2 electrochemical reduction reaction into account, several promising alloys, especially Au/Rh/Au(111), Au/Pt/Au(111), and Au/Ni/Au(111), are screened as promising candidates capable of transforming CO2 into valuable hydrocarbons based on reasonable CO adsorption energy range. Besides, the FAIR model as an effective tool for property prediction and feature analysis could also be applied to any system with explicit primary features and target. Bimetallic strip ; Feature (linguistics) ; k-nearest neighbors algorithm ; Adsorption ; Materials science ; Energy (signal processing) ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Chemistry ; Metallurgy ; Metal ; Mathematics ; Physical chemistry ; Statistics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xinpeng Zhao | Keke Song | Haiyou Huang | Yu Yan | Yanjing Su | Ping Qian
Source: ACS Omega (article, 2021-07-28)
Field: Materials Science — Biomaterials

Effect of Alloying Elements on the Stacking Fault Energy and Ductility in Mg<sub>2</sub>Si Intermetallic Compounds Alloying elements can pronouncedly change the mechanical properties of intermetallic compounds. However, the effect mechanism of this in Mg<sub>2</sub>Si alloys is not clear yet. In this paper, systematic first-principles calculations were performed to investigate the effect of alloying elements on the ductility of Mg-Si alloys. It was found that some alloying elements such as In, Cu, Pd, etc. could improve the ductility of Mg<sub>2</sub>Si alloys. Moreover, the interatomic bonding mechanisms were analyzed through the electron localization functional. Simultaneously, the machine-learning method was employed to help identify the most important features associated with the toughening mechanisms. It shows that the ground state atomic volume (<i>V</i> <sub>GS</sub>) is strongly related to the stacking fault energy (γ<sub>us</sub>) of Mg<sub>2</sub>Si alloys. Interestingly, the alloying elements with appropriate <i>V</i> <sub>GS</sub> and higher Allred-Rochow electronegativity (En) would reduce the γ<sub>us</sub> in the Mg-Si-<i>X</i> system and yield a better ductility. This work demonstrates how a fundamental theoretical understanding at the atomic and electronic levels can rationalize the mechanical properties of Mg<sub>2</sub>Si alloys at a macroscopic scale. Electronegativity ; Intermetallic ; Ductility (Earth science) ; Materials science ; Stacking-fault energy ; Atomic radius ; Stacking ; Metallurgy ; Stacking fault ; Composite material ; Dislocation ; Microstructure ; Creep ; Alloy ; Chemistry Magnesium Alloys: Properties and Applications ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Vladimír Krajňák | Shibabrat Naik | Stephen Wiggins
Source: Chemical Physics Letters (article, 2021-12-25)
Field: Materials Science — Materials Chemistry

Predicting trajectory behaviour via machine-learned invariant manifolds A priori and a posteriori ; Support vector machine ; Benchmark (surveying) ; Phase space ; Computer science ; Invariant (physics) ; Artificial intelligence ; Dynamical systems theory ; Trajectory ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Beáta Vranovičová | Roman Boča
Source: Journal of Molecular Modeling (article, 2021-11-18)
Field: Chemistry — Physical and Theoretical Chemistry

Ab initio study of the biogenic amino acids Mulliken population analysis ; Chemistry ; Polarizability ; Ab initio ; Electronic correlation ; HOMO/LUMO ; Ionization energy ; Electronegativity ; Computational chemistry ; Dipole ; Ab initio quantum chemistry methods ; Proton affinity ; Density functional theory ; Physical chemistry ; Molecule ; Ionization ; Organic chemistry Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Daniel Flam-Shepherd | Kevin Zhu | Alán Aspuru‐Guzik
Source: arXiv (Cornell University) (preprint, 2021-12-06)
Field: Materials Science — Materials Chemistry

Keeping it Simple: Language Models can learn Complex Molecular Distributions Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compounds depends on their ability to learn a training distribution of molecules. The most simple example is a language model that takes the form of a recurrent neural network and generates molecules using a string representation. More sophisticated are graph generative models, which sequentially construct molecular graphs and typically achieve state of the art results. However, recent work has shown that language models are more capable than once thought, particularly in the low data regime. In this work, we investigate the capacity of simple language models to learn distributions of molecules. For this purpose, we introduce several challenging generative modeling tasks by compiling especially complex distributions of molecules. On each task, we evaluate the ability of language models as compared with two widely used graph generative models. The results demonstrate that language models are powerful generative models, capable of adeptly learning complex molecular distributions -- and yield better performance than the graph models. Language models can accurately generate: distributions of the highest scoring penalized LogP molecules in ZINC15, multi-modal molecular distributions as well as the largest molecules in PubChem. Computer science ; Generative model ; Generative grammar ; Artificial intelligence ; Theoretical computer science ; Language model ; Molecular graph ; Simple (philosophy) ; Graph ; Representation (politics) ; Machine learning ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chady Ghnatios | George El Haber | Jean‐Louis Duval | Mustapha Ziane | Francisco Chinesta
Source: ESAFORM 2021 (article, 2021-04-12)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Based Space Reduction of Structural Models The need of solving industrial problems using faster and less computationally expensive techniques is becoming a requirement to cope with the present digital transformation of most industries. Recently, data is conquering the domain of engineering with different purposes: (i) defining data-driven models of materials, processes, structures and systems, whose physics-based models, when they exists, remain too inaccurate; (ii) enriching the existing physics-based models within the so-called hybrid paradigm; and (iii) using advanced machine learning and artificial intelligence techniques for scales bridging (upscaling), that is, for creating models that operating at the coarse-grained scale (cheaper in what respect the computational resources) enables integrating the fine-scale richness. The present work addresses the last item, aiming at enhancing standard structural models (defined in 2D shell geometries) for accounting all the fine-scale details (3D with rich through-the-thickness behaviors). For this purpose, two main strategies will be combined: (i) the in-plane-out-of-plane proper generalized decomposition -PGD- serving to provide the fine-scale richness; and (ii) advance machine learning techniques able to learn and extract the regression relating the input parameters with those high-resolution detailed descriptions. Computer science ; Scale (ratio) ; Artificial intelligence ; Transformation (genetics) ; Bridging (networking) ; Machine learning ; Industrial engineering ; Engineering Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Umer Sajjad | Arshad Ali | Jawad Ahmad | Sultan Shoaib
Source:  (article, 2021-01-26)
Field: Materials Science — Materials Chemistry

Application of Artificial Neural Network in Predicting Flashover Behaviour of Outdoor Insulators under Polluted Conditions Safe and reliable delivery of power through transmission lines mainly depends on the quality condition of the high voltage insulators. In the last few decades, demand in polymeric insulator has been dramatically increased due to their advanced performance in comparison to ceramic and glass insulators. This paper discusses the application of Artificial Neural Network (ANN) to predict the flashover parameters of polymeric insulators under the impact of weather and environment conditions. The training data for ANN were obtained from experimental tests executed in the climate chamber with the implementation of high voltage stress. The parameters predicted in this paper are arc-inception voltage, flashover voltage and surface resistance. A promising application of the ANN model proposed in this paper is the effective prediction of the flashover parameters of polymeric insulators affecting by extreme temperature, humidity and pollution level. These results will also enhance our understanding of the flashover process in outdoor polymeric insulators. Arc flash ; Insulator (electricity) ; Artificial neural network ; Voltage ; Environmental science ; Electric power transmission ; Materials science ; Humidity ; Automotive engineering ; Engineering ; Electrical engineering ; Computer science ; Composite material ; Meteorology ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Lea Fellner | Marian Kraus | Arne Walter | Frank Duschek | Thomas Bocklitz | Valentina Gabbarini | Riccardo Rossi | Alessandro Puleio | Andrea Malizia | P. Gaudio
Source: The European Physical Journal Plus (article, 2021-11-01)
Field: Chemistry — Analytical Chemistry

Determination of composition of mixed biological samples using laser-induced fluorescence and combined classification/regression models Abstract Laser-induced fluorescence (LIF) provides the ability to distinguish organic materials by a fast and distant in situ analysis. When detecting the substances directly in the environment, e.g., in an aerosol cloud or on surfaces, additional fluorescence signals of other fluorophores occurring in the surrounding are expected to mix with the desired signal. We approached this problem with a simplified experimental design for an evaluation of classification algorithms. An upcoming question for enhanced identification capabilities is the case of mixed samples providing different signals from different fluorophores. For this work, mixtures of up to four common fluorophores (NADH, FAD, tryptophan and tyrosine) were measured by a dual-wavelength setup and spectrally analyzed. Classification and regression are conducted with neural networks and show an excellent performance in predicting the ratios of the selected ingredients. Fluorescence ; Biological system ; Laser-induced fluorescence ; Identification (biology) ; Artificial neural network ; Regression ; Fluorescence spectroscopy ; Wavelength ; SIGNAL (programming language) ; Tryptophan ; Regression analysis ; Laser ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Materials science ; Pattern recognition (psychology) ; Artificial intelligence ; Chromatography ; Machine learning ; Mathematics ; Optics ; Physics ; Statistics ; Optoelectronics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Shengli Jiang | Zhuo Xu | Medhavi Kamran | Stas Zinchik | Sidike Paheding | Armando G. McDonald | Ezra Bar‐Ziv | Ví­ctor M. Zavala
Source: ChemRxiv (preprint, 2021-05-02)
Field: Chemistry — Analytical Chemistry

Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified. Attenuated total reflection ; Fourier transform infrared spectroscopy ; Convolutional neural network ; Fourier transform ; Plastic waste ; Spectroscopy ; Infrared ; Infrared spectroscopy ; Reflection (computer programming) ; Materials science ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Biological system ; Environmental science ; Chemistry ; Mathematics ; Optics ; Physics ; Engineering ; Waste management ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses
Authors: Matthew Jones | Nigel Clarke
Source: Soft Matter (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning real space microstructure characteristics from scattering data Using tools from morphological image analysis, we characterise spinodal decomposition microstructures by their Minkowski functionals, and search for a correlation between them and data from scattering experiments. To do this, we employ machine learning in the form of Gaussian process regression on data derived from numerical simulations of spinodal decomposition in polymer blends. For a range of microstructures, we analyse the predictions of the Minkowski functionals achieved by four Gaussian process regression models using the scattering data. Our findings suggest that there is a strong correlation between the scattering data and the Minkowski functionals. Minkowski space ; Scattering ; Spinodal decomposition ; Spinodal ; Gaussian ; Statistical physics ; Gaussian process ; Microstructure ; Materials science ; Mathematics ; Physics ; Optics ; Phase (matter) ; Quantum mechanics ; Geometry ; Composite material Machine Learning in Materials Science ; Enzyme Structure and Function ; X-ray Diffraction in Crystallography
Authors: Mustafa Karhan | Musa Faruk ÇAKIR
Source: Tehnicki vjesnik - Technical Gazette (article, 2021-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

ANN (Artificial Neural Network) Controlled Virtual Laboratory Design for NdFeB Magnet Production Magnets have an important place in electrical and electronic systems and applications nowadays. The developments in the field of magnets have also greatly expanded their usage areas. NdFeB magnets play active and important role in this development. In this study, design of virtual laboratory to be used for the production of nanocomposite NdFeB magnets has been realized. Maximum energy product (BHmax) is an important value for permanent magnets. The high BHmax value in small volume for the magnets is a desired criterion. In the study, mathematical functions were created from the data related to Br (permanent magnetism), Hc (magnetic coercivity), BHmax, Tc (Curie temperature) and density obtained in the researches on different NdFeB alloys in the laboratory. Additionally, Br functions were obtained by adding different additives (Co,Ti, Zr, Hf, V, Ta, Nb, Cr, W, Mo, Mn, Ni, Sb, Sn, Ge, Al, Bi) to the NdFeB magnets. A virtual laboratory is prepared with the created functions. The obtained results from the operation of the virtual laboratory system and the results obtained from Matlab Simulink and ANN (Artificial Neural Network) systems are compared. The designed and performed virtual laboratory system can be used both for industrial purposes and for educational purposes. Neodymium magnet ; Magnet ; Artificial neural network ; Materials science ; Production (economics) ; Metallurgy ; Computer science ; Engineering ; Artificial intelligence ; Mechanical engineering Magnetic Properties of Alloys ; Magnetic Field Sensors Techniques ; Magnetic and transport properties of perovskites and related materials
Authors: Clarissa G.C. Maia | Bárbara Caroline Rodrigues de Araujo | Maria Betânia de Freitas Marques | Israel F. Costa | María Irene Yoshida | Wagner da Nova Mussel | Rita C. O. Sebastião | Júlio S. Rebouças
Source: Oxidative Medicine and Cellular Longevity (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Thermal Stability Kinetics and Shelf Life Estimation of the Redox‐Active Therapeutic and Mimic of Superoxide Dismutase Enzyme, Mn(III) <i>meso</i>‐Tetrakis(<i>N</i>‐ethylpyridinium‐2‐yl)porphyrin Chloride (MnTE‐2‐PyPCl<sub>5</sub>, BMX‐010) Mn(III) <i>meso</i>-tetrakis(<i>N</i>-ethylpyridinium-2-yl)porphyrin chloride (MnTE-2-PyPCl<sub>5</sub>, BMX-010, and AEOL10113) is among the most studied superoxide dismutase (SOD) mimics and redox-active therapeutics, being currently tested as a drug candidate in a phase II clinical trial on atopic dermatitis and itch. The thermal stability of active pharmaceutical ingredients (API) is useful for estimating the expiration date and shelf life of pharmaceutical products under various storage and handling conditions. The thermal decomposition and kinetic parameters of MnTE-2-PyPCl<sub>5</sub> were determined by thermogravimetry (TG) under nonisothermal and isothermal conditions. The first thermal degradation pathway affecting Mn-porphyrin structural integrity and, thus, activity and bioavailability was associated with loss of ethyl chloride via <i>N</i>-dealkylation reaction. The thermal stability kinetics of the <i>N</i>-dealkylation process leading to MnTE-2-PyPCl<sub>5</sub> decomposition was investigated by using isoconversional models and artificial neural network. The new multilayer perceptron (MLP) artificial neural network approach allowed the simultaneous study of ten solid-state kinetic models and showed that MnTE-2-PyPCl<sub>5</sub> degradation is better explained by a combination of various mechanisms, with major contributions from the contraction models R1 and R2. The calculated activation energy values from isothermal and nonisothermal data were about 90 kJ mol<sup>-1</sup> on average and agreed with one another. According to the R1 modelling of the isothermal decomposition data, the estimated shelf life value for 10% decomposition (<i>t</i> <sub>90%</sub>) of MnTE-2-PyPCl<sub>5</sub> at 25°C was approximately 17 years, which is consistent with the high solid-state stability of the compound. These results represent the first study on the solid-state decomposition kinetics of Mn(III) 2-<i>N</i>-alkylpyridylporphyrins, contributing to the development of this class of redox-active therapeutics and SOD mimics and providing supporting data to protocols on purification, handling, storage, formulation, expiration date, and general use of these compounds. Porphyrin ; Kinetics ; Superoxide dismutase ; Chemistry ; Enzyme ; Redox ; Thermal stability ; Chloride ; Superoxide ; Biochemistry ; Inorganic chemistry ; Organic chemistry Thermal and Kinetic Analysis ; Free Radicals and Antioxidants ; Inorganic and Organometallic Chemistry