jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Atefehsadat Navabi | Tahereh Momeni Isfahani | Majid Ramazani | Mohammad Alimoradi
Source: Journal of Essential Oil Bearing Plants (article, 2021-07-04)
Field: Chemistry — Spectroscopy

QSPR Models for Predicting Retention Indices of<i>Polygonum minus</i>Huds. Essential Oil Composition Using GA-BWMLR and GA-BPANN Methods In the present study, quantitative structure-property relationship (QSPR) models were used for modeling and predicting retention index (RI) values of chemical composition in Polygonum minus essential oil. In gas chromatography, RIs are used to convert retention times into system-independent constants. The dataset of 40 molecules was divided into training and external validation sets. A set of molecular descriptors were calculated from the optimized structures of the molecules using Gaussian 09 and Dragon software. The genetic algorithm (GA), backward stepwise multiple linear regression (BW-MLR), and back-propagation artificial neural networks (BPANN) were used to obtain suitable QSPR models. The predictive power of the QSPR model was discussed using a coefficient of determination (R2), average absolute deviation (AAD), mean squared error (MSE), and leave-one-out cross-validation (Q2cv). According to our findings, it was concluded that the QSPR model with three descriptors (ATS5m, BEHm4, and G1) established by the GA-BPANN method could be efficiently used for predicting RI of chemical components in P. minus essential oil and may be helpful for modeling and designing of new molecules of the essential oils. Quantitative structure–activity relationship ; Molecular descriptor ; Linear regression ; Artificial neural network ; Mean squared error ; Coefficient of determination ; Mathematics ; Chemistry ; Backpropagation ; Stepwise regression ; Biological system ; Essential oil ; Cross-validation ; Training set ; Artificial intelligence ; Statistics ; Computer science ; Chromatography ; Stereochemistry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Seong‐Heum Park | Hyeongseon Park | Hyunbok Lee | Heung‐Sik Kim
Source: Journal of the Korean Physical Society (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Iterative peak-fitting of frequency-domain data via deep convolution neural networks Computer science ; Convolution (computer science) ; Artificial neural network ; Algorithm ; Residual ; Convolutional neural network ; Frequency domain ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Neural Networks and Applications
Authors: Suzanne K. Wallace | Anton S. Bochkarev | Ambroise van Roekeghem | Javier Carrasco | Alexander Shapeev | Natalio Mingo
Source: Physical Review Materials (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Free energy of (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">Co</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:mrow><mml:msub><mml:mi mathvariant="normal">Mn</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math> mixed phases from machine-learning-enhanced <i>ab initio</i> calculations ${({\mathrm{Co}}_{x}{\mathrm{Mn}}_{1\ensuremath{-}x})}_{3}{\mathrm{O}}_{4}$ is a promising candidate material for solar thermochemical energy storage. A high-temperature model for this system would provide a valuable tool for evaluating its potential. However, predicting phase diagrams of complex systems with ab initio calculations is challenging due to the varied sources affecting the free energy, and with the prohibitive amount of configurations needed in the configurational entropy calculation. In this work we compare three different machine-learning (ML) approaches for sampling the configuration space of ${({\mathrm{Co}}_{x}{\mathrm{Mn}}_{1\ensuremath{-}x})}_{3}{\mathrm{O}}_{4}$, including a simpler ML approach, which would be suitable for application in high-throughput studies. We use experimental data for a feature of the phase diagram to assess the accuracy of model predictions. We find that with some methods, data pretreatment is needed to obtain accurate predictions due to inherently composition-imbalanced training data for a mixed phase. We highlight that the important entropy contributions depend on the physical regimes of the system under investigation and that energy predictions with ML models are more challenging at compositions where there are energetically competing ground state crystal structures. Similar methods to those outlined here can be used to screen other candidate materials for thermochemical energy storage. Phase diagram ; Experimental data ; Crystal structure prediction ; Ab initio ; Work (physics) ; Configuration entropy ; Entropy (arrow of time) ; Ground state ; Energy (signal processing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Chemical Looping and Thermochemical Processes
Authors: Sergi Vela | Alberto Fabrizio | Ksenia R. Briling | Clémence Corminbœuf
Source: The Journal of Physical Chemistry Letters (article, 2021-06-22)
Field: Materials Science — Materials Chemistry

Learning the Exciton Properties of Azo-dyes The <i>ab initio</i> determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine learning (ML) techniques have demonstrated their accuracy at retrieving ES properties of large molecular databases at a reduced computational cost. For these applications, nonlinear algorithms tend to be specialized in targeting individual properties. Learning fundamental quantum objects potentially represents a more efficient, yet complex, alternative as a variety of molecular properties could be extracted through postprocessing. Herein, we report a general framework able to learn three fundamental objects: the hole and particle densities, as well as the transition density. We demonstrate the advantages of targeting those outputs and apply our predictions to obtain properties, including the state character and the exciton topological descriptors, for the two bands (nπ* and ππ*) of 3427 azoheteroarene photoswitches. Variety (cybernetics) ; Exciton ; Excited state ; Ab initio ; Computer science ; Molecule ; Materials science ; Chemical physics ; Nanotechnology ; Computational chemistry ; Statistical physics ; Chemistry ; Physics ; Artificial intelligence ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Machine Learning in Materials Science
Authors: Eiki Suzuki | Kiyou Shibata | Teruyasu Mizoguchi
Source: Applied Physics Express (article, 2021-07-19)
Field: Materials Science — Materials Chemistry

Accurate prediction of bonding properties by a machine learning–based model using isolated states before bonding Abstract Bonding characters, such as length and strength, are of key importance for material structure and properties. Here, a machine learning (ML) model is used to predict the bonding properties from information pertaining to isolated systems before bonding. This model employs the density of states (DOS) before bond formation as the ML descriptor and accurately predicts the binding energy, bond distance, covalent electron amount, and Fermi energy even when only 20% of the whole dataset is used for training. The results show that the DOS of isolated systems before bonding is a powerful descriptor for the prediction of bonding and adsorption properties. Covalent bond ; Chemical bond ; Bonding strength ; Bond energy ; Energy (signal processing) ; Materials science ; Key (lock) ; Computer science ; Chemistry ; Molecule ; Mathematics ; Composite material ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Eun-Ah Kim | Jordan Venderley | Michael Matty | Krishnanand Mallayya | Matthew Krogstad | Jacob Ruff | Geoff Pleiss | Varsha Kishore | David Mandrus | Daniel Phelan | L. Poudel | Andrew Gordon Wilson | Kilian Q. Weinberger | Puspa Upreti | M. R. Norman | Stephan Rosenkranz | R. Osborn
Source: Research Square (preprint, 2021-03-17)
Field: Materials Science — Materials Chemistry

Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction Big data ; Unsupervised learning ; Artificial intelligence ; Data science ; Computer science ; Machine learning ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: S. Quondam Antonio | Francesco Riganti Fulginei | Antonio Faba | Francesco Chilosi | E. Cardelli
Source: Magnetochemistry (article, 2021-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Vector Hysteresis Processes for Innovative Fe-Si Magnetic Powder Cores: Experiments and Neural Network Modeling A thorough investigation of the 2-D hysteresis processes under arbitrary excitations was carried out for a specimen of innovative Fe-Si magnetic powder material. The vector experimental measurements were first performed via a single disk tester (SDT) apparatus under a controlled magnetic induction field, taking into account circular, elliptic, and scalar processes. The experimental data relative to the circular loops were utilized to identify a vector model of hysteresis based on feedforward neural networks (NNs), having as an input the magnetic induction vector B and as an output the magnetic field vector H. Then the model was validated by the simulation of the other experimental hysteresis processes. The comparison between calculated and measured loops evidenced the capability of the model in both the reconstruction of the magnetic field trajectory and the prediction of the power loss under various excitation waveforms. Finally, the computational efficiency of the model makes it suitable for future application in finite element analysis (FEA). Hysteresis ; Preisach model of hysteresis ; Finite element method ; Magnetic hysteresis ; Magnetic field ; Scalar (mathematics) ; Excitation ; Artificial neural network ; Vector field ; Waveform ; Electrical steel ; Trajectory ; Electromagnetic induction ; Magnetization ; Physics ; Computer science ; Materials science ; Mechanics ; Condensed matter physics ; Mathematics ; Voltage ; Electromagnetic coil ; Artificial intelligence ; Geometry ; Thermodynamics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Mohammad Shishesaz | Moslem Ghobadi | Najmeh Asadi | Alireza Zarezadeh | Ehsan Saebnoori | Hamed Amraei | Jan Schubert | Ondřej Chocholatý
Source: Molecules (article, 2021-12-07)
Field: Materials Science — Materials Chemistry

Surface Pretreatments of AA5083 Aluminum Alloy with Enhanced Corrosion Protection for Cerium-Based Conversion Coatings Application: Combined Experimental and Computational Analysis The effects of surface pretreatments on the cerium-based conversion coating applied on an AA5083 aluminum alloy were investigated using a combination of scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), polarization testing, and electrochemical impedance spectroscopy. Two steps of pretreatments containing acidic or alkaline solutions were applied to the surface to study the effects of surface pretreatments. Among the pretreated samples, the sample prepared by the pretreatment of the alkaline solution then acid washing presented higher corrosion protection (~3 orders of magnitude higher than the sample without pretreatment). This pretreatment provided a more active surface for the deposition of the cerium layer and provided a more suitable substrate for film formation, and made a more uniform film. The surface morphology of samples confirmed that the best surface coverage was presented by alkaline solution then acid washing pretreatment. The presence of cerium in the (EDS) analysis demonstrated that pretreatment with the alkaline solution then acid washing resulted in a higher deposition of the cerium layer on the aluminum surface. After selecting the best surface pretreatment, various deposition times of cerium baths were investigated. The best deposition time was achieved at 10 min, and after this critical time, a cracked film formed on the surface that could not be protective. The corrosion resistance of cerium-based conversion coatings obtained by electrochemical tests were used for training three computational techniques (artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine regression (SVMR)) based on Pretreatment-1 (acidic or alkaline cleaning: pH (1)), Pretreatment-2 (acidic or alkaline cleaning: pH (2)), and deposition time in the cerium bath as an input. Various statistical criteria showed that the ANFIS model (<i>R</i><sup>2</sup> = 0.99, MSE = 48.83, and MAE = 3.49) could forecast the corrosion behavior of a cerium-based conversion coating more accurately than other models. Finally, due to the robust performance of ANFIS in modeling, the effect of each parameter was studied. Cerium ; Materials science ; Dielectric spectroscopy ; Cerium nitrate ; Corrosion ; Scanning electron microscope ; Conversion coating ; Coating ; Chemical engineering ; Surface modification ; Nuclear chemistry ; Electrochemistry ; Metallurgy ; Chemistry ; Composite material Corrosion Behavior and Inhibition ; Electrodeposition and Electroless Coatings ; Anodic Oxide Films and Nanostructures
Authors: Cristian Bodnar | Fabrizio Frasca | Nina Otter | Yu Guang Wang | Píetro Lió | Guido Montúfar | Michael M. Bronstein
Source: arXiv (Cornell University) (preprint, 2021-06-23)
Field: Materials Science — Materials Chemistry

Weisfeiler and Lehman Go Cellular: CW Networks Graph Neural Networks (GNNs) are limited in their expressive power, struggle with long-range interactions and lack a principled way to model higher-order structures. These problems can be attributed to the strong coupling between the computational graph and the input graph structure. The recently proposed Message Passing Simplicial Networks naturally decouple these elements by performing message passing on the clique complex of the graph. Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs). In this work, we extend recent theoretical results on SCs to regular Cell Complexes, topological objects that flexibly subsume SCs and graphs. We show that this generalisation provides a powerful set of graph "lifting" transformations, each leading to a unique hierarchical message passing procedure. The resulting methods, which we collectively call CW Networks (CWNs), are strictly more powerful than the WL test and not less powerful than the 3-WL test. In particular, we demonstrate the effectiveness of one such scheme, based on rings, when applied to molecular graph problems. The proposed architecture benefits from provably larger expressivity than commonly used GNNs, principled modelling of higher-order signals and from compressing the distances between nodes. We demonstrate that our model achieves state-of-the-art results on a variety of molecular datasets. Computer science ; Message passing ; Theoretical computer science ; Graph ; Clique ; Set (abstract data type) ; Expressive power ; Undirected graph ; Mathematics ; Distributed computing ; Combinatorics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Christian Emeka Okafor | Christopher Chukwutoo Ihueze
Source: SHILAP Revista de lepidopterología (article, 2021-01-25)
Field: Materials Science — Polymers and Plastics

The Graft of ANN-FEM Technique in Macro-mechanics of Multi-oriented Natural Fiber/Polyester Laminates Low weight and high strength requirements are prime target design objectives in strength demanding applications. Skillful design of low density, low weight and eco-friendly natural fiber composites could provide an alternative material route to the actualization of lighter structures. The present study proposed ANN-FEM computational framework for the macro-mechanical analysis of multi-oriented Plantain Empty Fruit Bunch Fiber Laminate (PEFBFL) and Plantain Pseudo Stem Fiber Laminate (PPSFL). Control factors were numerically varied using Finite Element Method (FEM) and the resultant FEM models which encapsulated material properties of the laminate was streamlined into Artificial Neural Network (ANN) training scheme. A standard feed-forward backpropagation network was adopted and the ANN model consists of stacking sequence, laminate aspect ratio and fiber orientation as input variables while the selected network outputs variables include average stress and displacement. The laminate constitutive equation was developed which enabled the establishment of laminate load deformation affiliation and equivalent elastic constants. The damage onset for individual lamina was detected by the maximum principal stress theory and the overall laminate strength of 40.12 N/mm^2 was obtained for PEFBFL and 32.16N/mm^2 for PPSFL. On the whole, there was steady reduction in laminates elastic modulus which points to compromised stiffness in material principal axis arising from gradual failure of the plies, this trend continued until the last ply failure occurred in ply 3 and 4 at 90 degrees in tensile mode of transverse direction. Stresses and displacements observed using CLT agree very closely with predictions of ANN. Finite element method ; Materials science ; Composite material ; Ultimate tensile strength ; Stiffness ; Structural engineering ; Fiber ; Constitutive equation ; Reduction (mathematics) ; Mathematics ; Engineering ; Geometry Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research
Authors: Andrle, A. | Hönicke, P. | Gwalt, G. | Schneider, P.I. | Kayser, Y. | Siewert, F. | Soltwisch, V.
Source: HZB Repository (Helmholtz-Zentrum Berlin für Materialien und Energie GmbH (HZB)) (article, 2021-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Shape and element sensitive reconstruction of periodic nanostructures with grazing incidence x ray fluorescence analysis and machine learning The characterization of nanostructured surfaces with sensitivity in the sub nm range is of high importance for the development of current and next generation integrated electronic circuits. Modern transistor architectures for, e.g., FinFETs are realized by lithographic fabrication of complex, well ordered nanostructures. Recently, a novel characterization technique based on X ray fluorescence measurements in grazing incidence geometry was proposed for such applications. This technique uses the X ray standing wave field, arising from an interference between incident and the reflected radiation, as a nanoscale sensor for the dimensional and compositional parameters of the nanostructure. The element sensitivity of the X ray fluorescence technique allows for a reconstruction of the spatial element distribution using a finite element method. Due to a high computational time, intelligent optimization methods employing machine learning algorithms are essential for timely provision of results. Here, a sampling of the probability distributions by Bayesian optimization is not only fast, but it also provides an initial estimate of the parameter uncertainties and sensitivities. The high sensitivity of the method requires a precise knowledge of the material parameters in the modeling of the dimensional shape provided that some physical properties of the material are known or determined beforehand. The unknown optical constants were extracted from an unstructured but otherwise identical layer system by means of soft X ray reflectometry. The spatial distribution profiles of the different elements contained in the grating structure were compared to scanning electron and atomic force microscopy and the influence of carbon surface contamination on the modeling results were discussed. This novel approach enables the element sensitive and destruction free characterization of nanostructures made of silicon nitride and silicon oxide with sub nm resolution X-ray fluorescence ; Grazing ; Nanostructure ; Fluorescence ; X-ray ; Incidence (geometry) ; Materials science ; Optics ; Nanotechnology ; Physics ; Biology ; Botany Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Wesley K. Tatum | Diego Torrejon | Anton B. Resing | Jonathan W. Onorato | Christine K. Luscombe
Source: Computational Materials Science (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

Algorithmically extracted morphology descriptions for predicting device performance Computer science ; Organic solar cell ; Morphology (biology) ; Artificial intelligence ; Materials science ; Thin film ; Biological system ; Nanotechnology Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Thin-Film Transistor Technologies
Authors: Dipankar Mandal | Arpitam Chatterjee | Bipan Tudu
Source: Concurrency and Computation Practice and Experience (article, 2021-07-22)
Field: Chemistry — Analytical Chemistry

A deep neural network and random forests driven computer vision framework for identification and prediction of metanil yellow adulteration in turmeric powder Summary Turmeric ( Curcuma longa ) is a popular food ingredient which is widely used in powdered form. Despite different food and medicinal advantages it is often adulterated. Metanil yellow (MET) is one such synthetic chemical which can be easily mixed with turmeric powder and such mixing is difficult to detect. This paper presents a computer vision framework using the potential of deep neural network towards detection of MET adulteration in turmeric powder and random forests regressor to predict the possible amount of adulterant. An in‐house database consisting of features from turmeric images of five variants of pure and adulterated turmeric powder has been used for experimentations. A new frequency domain annular‐mean filter‐based feature extraction has been used. The results show the potential of the presented method that can perform with more than 98% accuracy in both identification and prediction tasks. The reported technique can be considered as a motivating step towards development of a non‐invasive and low‐cost mobile device towards food adulteration detection in future. Adulterant ; Curcuma ; Identification (biology) ; Artificial intelligence ; Artificial neural network ; Ingredient ; Pattern recognition (psychology) ; Computer science ; Random forest ; Food science ; Traditional medicine ; Chromatography ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Dye analysis and toxicity