jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefanos Giaremis | Georgios Katsikas | G. Sempros | M. Gjoka | C. Sarafidis | Joseph Kioseoglou
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

<i>Ab initio</i>, artificial neural network predictions and experimental synthesis of mischmetal alloying in Sm–Co permanent magnets The use of the mischmetal alloy, comprised of La and Ce in 1 : 3 ratio, as a partial substitute for Sm in the CaCu<sub>5</sub>-type structure is explored, as a means for the search of viable alternatives for permanent magnets that require fewer steps in the rare earth separation processing. The structural and magnetic properties of the introduced stoichiometry, containing 50% less Sm, are compared to the ones of the SmCo<sub>5</sub>, LaCo<sub>5</sub> and CeCo<sub>5</sub> binary compounds by means of <i>ab initio</i> simulations. The capability of artificial neural networks to accurately predict the relationship between structure and total magnetization from DFT calculations in the supercell approach that was employed, is also demonstrated. Experimental fabrication and structural and magnetic characterization of the proposed stoichiometry verifies the structural configuration and provides insight for the macroscopic hard magnetic properties of the material. The reduction of magnetic properties was found to be favorable compared to the respective reduction of the raw materials cost, while measurements of the Cure temperature verify that the proposed compound is still suitable for high temperature applications. Mischmetal ; Materials science ; Magnet ; Ab initio ; Stoichiometry ; Alloy ; Ab initio quantum chemistry methods ; Metallurgy ; Physical chemistry ; Chemistry ; Physics ; Molecule ; Organic chemistry Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; Magnetic Properties and Applications
Authors: Chenyang Gan | Liangyong Wang | Shunkai Xiao | Yaolong Zhu
Source: Crystals (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

Feedback Control of Crystal Size Distribution for Cooling Batch Crystallization Using Deep Learning-Based Image Analysis The shape of the crystal size distribution directly determines the quality of crystal products. It is often assumed that distributional properties of crystal size conform to the Gaussian distribution or the log normal distribution. The mean and variance or relative crystal number are widely adopted to describe the crystal size distribution and taken as the control objectives. Therefore, the resulting control methods have difficulties in controlling the crystal size distribution with a general shape. In this article, a novel feedback control system of crystal size distribution based on image analysis is designed for the effective control of crystal size distribution with a general shape. First, a deep learning network-based image analysis method is adopted and implemented to extract the crystal size distribution. Second, the crystal size distribution is approximated by a radial basis function neural network. Consequently, a feedback controller is designed and the tracking control of the target crystal size distribution is finally realized. The results of crystallization experiments demonstrate the effectiveness of the proposed method. Crystal (programming language) ; Crystallization ; Distribution (mathematics) ; Gaussian ; Materials science ; Computer science ; Biological system ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis ; Thermodynamics Crystallization and Solubility Studies ; Chromatography in Natural Products
Authors: Farid Amirkhani | Amir Dashti | Hossein Abedsoltan | Amir H. Mohammadi | Abdoulmohammad Gholamzadeh Chofreh | Feybi Ariani Goni | Jiří Jaromír Klemeš
Source: Fuel (article, 2022-05-05)
Field: Chemistry — Analytical Chemistry

Estimating flashpoints of fuels and chemical compounds using hybrid machine-learning techniques Computer science ; Adaptive neuro fuzzy inference system ; Artificial neural network ; Artificial intelligence ; Range (aeronautics) ; Support vector machine ; Machine learning ; Fuzzy logic ; Data mining ; Biochemical engineering ; Fuzzy control system ; Engineering Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Noora Hyttinen | Antti Pihlajamäki | Hannu Häkkinen
Source: The Journal of Physical Chemistry Letters (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model is able to predict chemical potentials of molecules that are in the size range of the training data with a root-mean-square error (RMSE) of 0.5 kcal/mol. There is also a linear correlation between calculated and predicted chemical potentials of molecules that are larger than those included in the training set. Finding the lowest chemical potential conformers is useful in condensed phase thermodynamic property calculations, in order to reduce the number of computationally demanding density functional theory calculations. Conformational isomerism ; Molecule ; Density functional theory ; Training set ; Mean squared error ; Range (aeronautics) ; Chemistry ; Root mean square ; Hydrogen ; Biological system ; Thermodynamics ; Computational chemistry ; Mathematics ; Artificial intelligence ; Materials science ; Computer science ; Statistics ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Md. Imran H. Khan | Duval Longa | Shyam S. Sablani | Yuantong Gu
Source: Food and Bioprocess Technology (article, 2022-11-21)
Field: Materials Science — Polymers and Plastics

A Novel Machine Learning–Based Approach for Characterising the Micromechanical Properties of Food Material During Drying Artificial neural network ; Backpropagation ; Artificial intelligence ; Machine learning ; Computer science ; Biological system ; Experimental data ; Nonlinear system ; Stiffness ; Genetic algorithm ; Mathematics ; Engineering ; Statistics ; Structural engineering ; Biology Textile materials and evaluations ; Food Drying and Modeling ; Advanced machining processes and optimization
Authors: Shai Kendler | Ziv Mano | Ran Aharoni | Raviv Raich | Barak Fishbain
Source: Scientific Reports (article, 2022-10-20)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for chemicals identification: a human-inspired machine learning approach Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature of the target materials. The inputs for educating and testing the machine were a nonlinear mixing model, the spectra of the pure target materials (which are problem invariant), and the unlabeled HSI data. The educated machine is accurate, and its generalization capabilities outperform classical machines. When using the educated machine, the number of falsely identified samples is ~ 100 times lower than the classical machine. The probability for detection with the educated machine is 96% compared to 90% with the classical machine. Hyperspectral imaging ; Machine learning ; Artificial intelligence ; Computer science ; Support vector machine ; Identification (biology) ; Generalization ; Flexibility (engineering) ; Nonlinear system ; Mixing (physics) ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Ranjit Singh | Ravi Pratap Singh | Rajeev Trehan
Source: Sensors International (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning algorithms based advanced optimization of EDM parameters: An experimental investigation into shape memory alloys In the era of advancement and progressive fourth industrial revolution culture, the demand of advanced and smart engineering materials has been increased. In this way, shape memory alloys are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft component components, military instruments, fabricating elements, and bio-medical devices, among others. This paper has been aimed to attempt the machine learning (ML) algorithms-based optimization of the different process inputs in electrical discharge machining of Cu-based shape memory alloy. The current study focused on study the behavior of response parameters along with the variation in machining input parameters The considered process input factors are namely as; pulse on time (Ton), pulse off time (Toff), peak current (Ip), and gap voltage (GV) and their effects were studied on dimensional deviation (DD) and tool wear rate (TWR). The central composite design matrix has been employed for planning the main runs. The 2-D and 3-D graphs represents the behavior of the response parameters along with variations in the machining inputs. The novelty of the work is machining of Cu-based Shape Memory Alloy (SMA) in EDM operations and optimization of parameters using Machine Learning techniques. Furthermore, machine learning based, single and multi-objective optimization of investigated responses were conducted using the desirability approach, Genetic Algorithm (GA) and Teacher Learning based Optimization (TLBO) techniques. The parametric combination attained for optimization of multiple responses (TWR and DD) is: Ton ​= ​90.10 ​μs, Toff ​= ​149.69 ​μs, Ip ​= ​24.59 A & GV ​= ​60 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V, using desirability approach, GA method and TLBO method, respectively. Electrical discharge machining ; Taguchi methods ; Machining ; Shape-memory alloy ; Mechanical engineering ; Genetic algorithm ; Voltage ; Computer science ; Algorithm ; Parametric statistics ; Actuator ; Machine learning ; Engineering ; Artificial intelligence ; Mathematics ; Electrical engineering Shape Memory Alloy Transformations ; Advanced Machining and Optimization Techniques
Authors: Abbasali Abouei Mehrizi | Hamed Jafarzadeh | Mohammad Soleimani Lashkenari | Mastoureh Naddafi | Van Thuan Le | Vy Anh Tran | Elnea-Niculina Dragoi | Yasser Vasseghian
Source: Process Safety and Environmental Protection (article, 2022-06-02)
Field: Materials Science — Polymers and Plastics

Artificial neural networks modeling ethanol oxidation reaction kinetics catalyzed by polyaniline-manganese ferrite supported platinum-ruthenium nanohybrid electrocatalyst Tafel equation ; Electrocatalyst ; Catalysis ; Electrochemistry ; Polyaniline ; Anode ; Materials science ; Proton exchange membrane fuel cell ; Chemical engineering ; Chemistry ; Electrode ; Physical chemistry ; Composite material ; Organic chemistry Conducting polymers and applications ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Ruoting Zhao | Bangyu Xing | Huimin Mu | Yuhao Fu | Lijun Zhang
Source: Chinese Physics B (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials With the rapid development of artificial intelligence and machine learning (ML) methods, materials science is rapidly entering the era of data-driven materials informatics. ML models serve as the most crucial component, closely bridging material structure and material properties. There is a considerable difference in the prediction performance of different ML methods for material systems. Herein, we evaluated three categories (linear, kernel, and nonlinear methods) of models, with twelve ML algorithms commonly used in the materials field. In addition, halide perovskite was chosen as an example to evaluate the fitting performance of different models. We constructed a total dataset of 540 halide perovskites and 72 features, with formation energy and bandgap as target properties. We found that different categories of ML models show similar trends for different target properties. Among them, the difference between the models is enormous for the formation energy, with the coefficient of determination ( R 2 ) range 0.69–0.953. The fitting performance between the models is closer for bandgap, with the R 2 range 0.941–0.997. The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap. It shows that the nonlinear-ensemble model, constructed by combining multiple weak learners, effectively describes the nonlinear relationship between material features and target property. In addition, the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and bandgap. Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems. The dataset that supported the findings of this study is available in Science Data Bank, with the link https://www.doi.org/10.11922/sciencedb.01611 . Band gap ; Computer science ; Perovskite (structure) ; Nonlinear system ; Artificial intelligence ; Materials science ; Machine learning ; Boosting (machine learning) ; Halide ; Decision tree ; Gradient boosting ; Algorithm ; Physics ; Optoelectronics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Gogulan Karunanithy | Tairan Yuwen | Lewis E. Kay | D. Flemming Hansen
Source: Journal of Biomolecular NMR (article, 2022-05-27)
Field: Chemistry — Spectroscopy

Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3-60 ms, the range most frequently observed via experiment. The work presented here focuses on the <sup>1</sup>H CEST class of methods that are further complicated, in relation to applications to other nuclei, by anti-phase features. The developed DNNs accurately predict the chemical shifts of nuclei in the exchanging species directly from anti-phase <sup>1</sup>H<sup>N</sup> CEST profiles, along with an uncertainty associated with the predictions. The performance of the DNN was quantitatively assessed using both synthetic and experimental anti-phase CEST profiles. The assessments show that the DNN accurately determines chemical shifts and their associated uncertainties. The DNNs developed here do not contain any parameters for the end-user to adjust and the method therefore allows for autonomous analysis of complex NMR data that report on conformational exchange. Chemistry ; Biological system ; Artificial neural network ; Chemical shift ; Decoupling (probability) ; Artificial intelligence ; Magnetization transfer ; Computer science ; Statistical physics ; Machine learning ; Physics ; Physical chemistry Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies ; NMR spectroscopy and applications
Authors: Jesus Sanchez-Juarez | Marissa Granados-Baez | Alberto A. Aguilar‐Lasserre | Jaime Cárdenas
Source: Optical Materials Express (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Automated system for the detection of 2D materials using digital image processing and deep learning The unique properties of two-dimensional materials for light emission, detection, and modulation make them ideal for integrated photonic devices. However, identifying if the films are indeed monolayers is a time-consuming process even for well-trained operators. We develop an intelligent algorithm to detect monolayers of WSe 2 , MoS 2 and h-BN autonomously using Digital Image Processing and Deep Learning with high accuracy rate, avoiding human interaction and any additional characterization tests. We demonstrate an autonomous detection algorithm for TMDC’s and h-BN monolayers with high accuracy of 99.9% with a total processing time of 9 minutes per 1 cm 2 . Monolayer ; Materials science ; Image processing ; Digital image processing ; Characterization (materials science) ; Signal processing ; Computer science ; Process (computing) ; Photonics ; Modulation (music) ; Artificial intelligence ; Digital signal processing ; Computer vision ; Optoelectronics ; Optics ; Nanotechnology ; Image (mathematics) ; Computer hardware ; Acoustics ; Physics 2D Materials and Applications ; Nanowire Synthesis and Applications ; Machine Learning in Materials Science
Authors: Chuang Yao | Xin Li | Yezi Yang | Lei Li | Maolin Bo | Cheng Peng | Jinshan Wang
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning with quantum chemistry descriptors: predicting the solubility of small-molecule optoelectronic materials for organic solar cells A general solution prediction model was developed by using the smallest set of quantum chemistry descriptors. Solubility ; Organic solar cell ; Molecule ; Organic molecules ; Materials science ; Chemistry ; Nanotechnology ; Optoelectronics ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Shun Nishimura | Son Dinh Le | Itsuki Miyazato | Jun Fujima | Toshiaki Taniike | Junya Ohyama | Keisuke Takahashi
Source: Catalysis Science & Technology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La<sub>2</sub>O<sub>3</sub>-based catalysts for the oxidative coupling of methane Unique 30 types of multi-component La 2 O 3 -based catalysts for oxidative coupling of methane were discovered in 75 types of selected catalysts based on high-throughput screening and literature datasets with multi-output machine learning approaches. Catalysis ; Component (thermodynamics) ; Oxidative coupling of methane ; Throughput ; Coupling (piping) ; Methane ; Computer science ; Chemistry ; Materials science ; Physics ; Organic chemistry ; Thermodynamics ; Telecommunications Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Amir Abbas Kazemzadeh Farizhandi | Mahmood Mamivand
Source: Materials & Design (article, 2022-06-04)
Field: Materials Science — Materials Chemistry

Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There are several paths for an identical microstructure, 2) Microstructures reach steady-state morphologies after a long time of aging. The error analysis shows that the majority of the wrong predictions are indeed not wrong, but the other right answers. We validated the model successfully with an experimental Fe-Cr-Co transmission electron microscopy micrograph. Microstructure ; Materials science ; Chemical composition ; Artificial neural network ; Biological system ; Work (physics) ; Inverse ; Algorithm ; Artificial intelligence ; Metallurgy ; Thermodynamics ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and Mechanical Properties of Steels
Authors: Aaron Döring | Andrey L. Rogach
Source: ACS Applied Nano Materials (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

Utilizing Deep Learning to Enhance Optical Sensing of Ethanol Content Based on Luminescent Carbon Dots Optical sensing methods have shown their potential in measuring environmental properties such as temperatures and sample concentrations, increasing the convenience and speed of such measurements. Carbon dots (CDs) are readily available, easy-to-synthesize, and non-toxic luminescent colloidal nanoparticles that have gained prominence as optical probes in recent years. A disadvantage of CDs used for optical sensing is their broad emission profile, leading to unspecific sensing and a potential overlap of their luminescence with the autofluorescence of the samples. Machine learning approaches can address these shortcomings and greatly enhance sensing accuracy. In this study, CDs were employed as optical probes, while machine learning methods were applied to optimize ethanol content determination in ethanol/water mixtures as well as in alcohol-containing beverages. A simple neural network was used to understand the importance of different optical parameters on sensing, while a modular deep learning model was developed for increased generalizability towards samples with strong autofluorescence. Drawing from multiple input channels, the deep learning model was able to predict ethanol concentrations with a mean absolute error of 0.4 vol % in pure solvents and 6.7 vol % in beverages (beers, wines, and spirits). While CDs are excellent candidates to demonstrate deep learning for optical sensing, the methods discussed in this work are promising for improving chemical sensing using various luminescent materials. Luminescence ; Autofluorescence ; Materials science ; Generalizability theory ; Nanotechnology ; Deep learning ; Artificial intelligence ; Computer science ; Fluorescence ; Optoelectronics ; Optics ; Mathematics Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Biosensors and Analytical Detection