jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gerfried Millner | Manfred Mücke | Lorenz Romaner | Daniel Scheiber
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Tensile strength prediction of steel sheets: an insight into data-driven models, dimensionality reduction, and feature importance Abstract In this work we apply data-driven models for predicting tensile strength of steel coils from chemical composition and process parameters. The data originates from steel production and includes a full chemical analysis, as well as many process parameters and the resulting strength properties from tensile tests. We establish a data pre-processing pipeline, where we apply data cleaning and feature engineering to create a machine-readable dataset suitable for various modeling tasks. We compare prediction quality, complexity and interpretability of pure machine learning (ML) models, either with the full feature set or a reduced one. Dimensionality reduction methods are used to reduce the number of features and therefore reduce complexity, either with a smart selection method or feature encoding, where features are combined and the included information is preserved. In order to determine key features of our models, we are investigating feature importance ratings, which can be used as a feature selection criteria. Furthermore, we are highlighting methods to explain predictions and determine the impact of every feature in every observation applicable for any ML model. Interpretability ; Feature selection ; Dimensionality reduction ; Feature (linguistics) ; Artificial intelligence ; Machine learning ; Pipeline (software) ; Curse of dimensionality ; Process (computing) ; Computer science ; Ultimate tensile strength ; Data set ; Set (abstract data type) ; Reduction (mathematics) ; Materials science ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Composite material Machine Learning in Materials Science
Authors: Kazuki Matsubara | Shusaku Takagi | Fan-Shun Meng | Shigenobu Ogata | Nobuyuki Ishikawa
Source: Proceedings of International Symposia on Steel Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Atomic-scale modelling of boundaries characterized lath martensite microstructure for hydrogen-induced crack initiation: A machine learning interatomic potentials study In this study, we attempted to elucidate the hydrogen embrittlement mechanism in lath martensite steels by multiscale approach across fracture test, microbeam analysis and atomic simulation. It was confirmed that the microcracks initiated not only along prior austenite grain boundaries but also partially along the boundaries existing within the prior austenite grain. To compare hydrogen embrittlement risk on various boundaries, the modelling method was developed to reflect the crystallography on hierarchical lath martensite microstructure. Hydrogen trapping on the boundaries was analysed using molecular dynamics calculations using neural network interatomic potential. Consequently, it was clarified that the hydrogen trapping energy of prior austenite grain boundaries is higher than that of the block and sub-block boundaries. Thus, the initiation risk due to hydrogen was lower in the block and sub-block boundaries which are the boundaries within prior austenite grain. It was suggested that elementary processes of hydrogen-induced isolated microcracks with length of approximately 30~50 μm are the crack initiation on intergranular boundaries with high risk and crack growth along intragranular boundaries with middle risk due to weaker hydrogen trapping. Lath ; Martensite ; Microstructure ; Materials science ; Atomic units ; Hydrogen ; Interatomic potential ; Scale (ratio) ; Metallurgy ; Molecular dynamics ; Chemistry ; Physics ; Computational chemistry ; Geography ; Cartography Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Rui Chen | Suresh K. Sitaraman
Source:  (article, 2024-05-28)
Field: Materials Science — Polymers and Plastics

Machine-Learning Guided Strain-Relief Patterns for Maximizing Stretchability of Printed Conductors There has been an explosion in the application of flexible and wearable electronics in a wide range of fields in the past few decades. When in use, flexible electronics are subjected to a variety of mechanical loadings, including stretching, bending, and twisting. These loadings will introduce damage in the printed materials and will eventually result in their failure. Therefore, it is beneficial to minimize the damage in the printed elements by reducing the mechanical strains without compromising the functionality of flexible electronics. Serpentine structures have been often used to mitigate the maximum strain during stretching, and in a prior publication, we have demonstrated that combining serpentine structure with strain-relief patterns will further reduce strain generated under the same stretching level. Strain-relief patterns represent a novel approach in the field of stretchable electronics. These patterns are cut-outs on the substrate that, when stretched, alter the strain distribution within the critical elements on the substrate. By strategically defining their geometry, dimensions, and locations, it is possible to significantly minimize the maximum strain experienced by these critical elements during stretching. As a result, the overall stretchability of the device is substantially improved. To achieve the optimal strain-relief pattern, machine-learning models have an effective way of identifying the effect of such parameters and help design and locate the strain-relief patterns. As a continuation of our previous paper, this paper explores a wider range of strain-relief patterns and examines their effect under stretching. Further, the circular strain-relief pattern is optimized to ensure the maximum reduction of the strain generated in the conductive ink. This optimization is achieved by conducting a parametric analysis of each strain-relief pattern and strain level via numerical simulation and developing the machine-learning models that correspond to these patterns. Other geometric designs of the strain-relief patterns are also considered as an initiation of future study. Electrical conductor ; 3d printed ; Strain (injury) ; Materials science ; Computer science ; Electrical engineering ; Composite material ; Engineering ; Biomedical engineering ; Medicine Textile materials and evaluations ; Mechanical stress and fatigue analysis ; Material Properties and Processing
Authors: Haoyu Wang | Ruizhi Wang | Yunbin Liu | Qing Gao | Lei Li | Hongxiang Cao | Liping He | Enling Tang
Source: Measurement (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Predicting the impact depolarization behavior of PZT-5H based on machine learning Depolarization ; Materials science ; Computer science ; Artificial intelligence ; Medicine Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Non-Destructive Testing Techniques
Authors: Olatunde Awotunde | Jin Cai | Christian Azar | Diane Medina | Samantha I. Eyolfson | Kathleen L. Hayes | Christèlle Waffo | Roland Marini Djang'Eing'A | Eric Ziémons | Pierre‐Yves Sacré | Marya Lieberman
Source: Journal of Pharmaceutical and Biomedical Analysis (article, 2024-04-30)
Field: Chemistry — Analytical Chemistry

Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets Chemistry ; Microcrystalline cellulose ; Active ingredient ; Dosage form ; Near-infrared spectroscopy ; Chromatography ; Calibration ; High-performance liquid chromatography ; Partial least squares regression ; Computer science ; Machine learning ; Statistics ; Mathematics ; Cellulose ; Optics Spectroscopy and Chemometric Analyses ; Pharmaceutical Quality and Counterfeiting ; Analytical Methods in Pharmaceuticals
Authors: Wentao Li | Zhijun Zhang | Xianghong Niu | Di He | Xing Wei | Yong Zhang
Source: The Journal of Physical Chemistry A (article, 2024-08-02)
Field: Chemistry — Spectroscopy

Diabatic Potential Energy Surfaces of SrH<sub>2</sub><sup>+</sup> and Dynamics Studies of the Sr<sup>+</sup>(5s<sup>2</sup>S) + H<sub>2</sub> Reaction Based on the ab initio energy points of both ground and excited states, a neural network fitting method combined with a specific function was successfully used to construct the diabatic potential energy surfaces (PESs) of the SrH<sub>2</sub><sup>+</sup> system. The topographical features of the diabatic PESs were examined in detail. The results indicate that the nonadiabatic transition characteristics between ground and excited states are accurately described by the newly constructed diabatic PESs. To verify the validity and applicability of the diabatic PESs, as well as the nonadiabatic effects during the reaction process, the quantum dynamics studies of the Sr<sup>+</sup>(5s<sup>2</sup>S) + H<sub>2</sub> reaction were performed based on both adiabatic and diabatic PESs. The dynamics results indicate that adiabatic dynamics results are dozens of times larger than those of nonadiabatic. This illustrates the significant effect of nonadiabaticity, indicating that adiabatic dynamics results often overestimate the actual values. The integral cross sections (ICSs) were calculated and compared with the experimental data. The diabatic ICSs are in good agreement with the experimental results. The reasonable dynamics results indicate that the newly constructed diabatic PESs are suitable for the relevant dynamics studies. Diabatic ; Physics ; Dynamics (music) ; Energy (signal processing) ; Reaction dynamics ; Atomic physics ; Thermodynamics ; Quantum mechanics ; Molecule Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Hind A Abdullatif | M. A. Abdelkawy | Shereen A. Boltia | Nesma M. Fahmy | Maha Kamal
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Novel enhanced detection and resolution of a nonfluorescent mixture in plasma at nanogram levels using sustainable fluorescent carbon dots and advanced chemometric models Chemistry ; Fluorescence ; Carbon fibers ; Environmental chemistry ; Resolution (logic) ; Plasma ; Nanotechnology ; Analytical Chemistry (journal) Carbon and Quantum Dots Applications ; Mercury impact and mitigation studies ; Advanced Nanomaterials in Catalysis
Authors: Matthew P. Rivera | Gianmarco Terrones | Tae Hoon Lee | Zachary P. Smith | Heather J. Kulik
Source: ChemRxiv (preprint, 2024-08-08)
Field: Chemistry — Inorganic Chemistry

Data-driven Screening and Discovery of Metal- organic Frameworks as C2 Adsorbents from Over 900 Experimental Isotherms The separation of ethylene from ethane accounts for almost 100 million tons of CO2 emissions annually and 0.3% of global primary energy usage. Replacing current cryogenic distillation units with adsorption separation units, especially for the minor component of ethane, would enable significant efficiency gains. Metal-organic frameworks (MOFs) are well-suited for adsorption separation due to their high surface areas and tunable chemical properties; exploring all possible MOFs is a daunting experimental challenge, motivating in silico screening with machine learning models. We present a database of 948 experimentally measured pure-component C2 isotherms from 192 MOFs gathered from the literature and use it to train machine learning models to predict MOF ethane and ethylene uptake across a range of temperature and pressure conditions. The models have high accuracy in interpolative tasks (mean absolute error ~0.05 mmol/g) when trained on only 20% of available data. Performance on unseen structures was also reasonably accurate with MAEs ~0.7 mmol/g. We apply the models to screen the CoRE ASR 2019 database and identify the most promising candidates. Several MOFs containing lanthanide metals were predicted to have high ethane selectivity, suggesting that this class of MOFs may be worth further investigation. Feature importance analysis suggests that MOF SBU design and optimizing the process conditions at which the sorbent will operate are critical for enabling ethane-selective separation. We synthesize a MOF predicted to exhibit high ethane selectivity and experimentally validate qualitative agreement of model predictions, highlighting the utility of both the dataset and model in discovering unexplored C2 adsorbents. Adsorption ; Metal-organic framework ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications
Authors: Tanja Lužaić | Katarina Nedić Grujin | Lato Pezo | Branislava Nikolovski | Zoran Maksimović | Ranko Romanić
Source: Foods (article, 2024-09-19)
Field: Materials Science — Biomaterials

Implementation of Cellulose-Based Filtration Aids in Industrial Sunflower Oil Dewaxing (Winterization): Process Monitoring, Prediction, and Optimization In the production of refined sunflower oil, waxes are removed during the winterization stage, and wax crystals are separated through filtration assisted by filtration aids. Commonly used filtration aids in oil refining include perlite and diatomaceous earth. After winterization, a significant amount of filter cake remains as a by-product and is treated as waste. Today, natural cellulose fibers are being promoted as filtration aids. Their advantages are numerous, both in the production process and from an environmental perspective. However, their only disadvantage is their higher cost. Therefore, in this study, 57 filtration cycles during the industrial sunflower oil winterization step using cellulose-based filtration aids were monitored. Different process parameters, including the pressure differential on the filter, the flow rate of filtered oil, constant pressure period, the quantity of filtered oil, filtration time, the quantity of pre-coating and dosing filtration aids, the volume of filtered oil, the concentration of dosing filtration aid, as well as the mass of separated waxes, were observed. Additionally, artificial neural networks were applied to predict process parameters, optimize the process, and, above all, determine the dosage of filtration aids, which will make the process more economical. The optimal filtration process is performed at a pressure differential of 3.3 bar, lasting a total of 39 h, with 32 h at constant pressure, resulting in 322,503 kg of filtered oil and 90.41 kg of waxes. The optimal quantity of cellulose-based filtration aids employed for pre-coat was 80 kg, and for dosing, 375 kg, with an optimal concentration of 0.12% <i>w/w</i>. Filtration (mathematics) ; Cross-flow filtration ; Wax ; Filter cake ; Sunflower oil ; Pulp and paper industry ; Materials science ; Chromatography ; Cellulose ; Chemistry ; Mathematics ; Composite material ; Engineering ; Food science Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: A K Nur Fadzeelah | Nur Alwani Ali Bashah | Fakhrony Sholahudin Rohman | Syahrul Fithry Senin | Ahmad Zuhairi Abdullah
Source: Desalination and Water Treatment (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Sonocatalytic degradation of caffeine using CeO2 nanorods: Modeling by artificial neural network This study has investigated the utilization of CeO 2 nanorods (NRs) in the sonocatalytic degradation of caffeine. The degradation performance was determined by examining the influence of three parametric conditions, namely, the initial pH of the solution (3.5 – 7.5), initial concentration of caffeine (5 – 30 mg/L), and dosage of CeO 2 NRs (0.5 – 2.0 g/L). All experiments were conducted in an ultrasonic bath (37 kHz, 150 W) that served as a sonocatalytic reactor. The mathematical modeling of the process with the catalyst was developed using Feedforward artificial neural networks (FFNN). The FFNN was employed to develop suitable modeling for determining the performance of the sonocatalytic degradation of caffeine (%) using CeO 2 NRs. A three-layer FFNN with [4−10-1] topology was successfully developed to predict the sonocatalytic degradation of caffeine using CeO 2 NRs. The FFNN was able to offer highly accurate predictions with the overall R 2 and MSE validation values of 0.991 and 0.00225, respectively. The ANN model has also provided excellent predictive performance by achieving the highest R 2 value. Thus, these results showed the promising finding of the sonocatalysis degradation of caffeine using CeO 2 NRs via experiments and the ANN model. Degradation (telecommunications) ; Artificial neural network ; Nanorod ; Caffeine ; Materials science ; Chemical engineering ; Computer science ; Artificial intelligence ; Nanotechnology ; Engineering ; Telecommunications ; Biology Catalytic Processes in Materials Science ; Advanced oxidation water treatment ; Gas Sensing Nanomaterials and Sensors
Authors: Md Arifur Rahman | Mohammad Jahangir Alam
Source:  (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization of Lead-Free Perovskite Solar Cell Architecture Using Machine Learning and SCAPS Simulations Significant advancements have been made in the study, research, and optimization of electrical devices and materials using machine learning (ML) models. Data regression and prediction provide for accurate estimates of device performance across a variety of factors by utilizing machine learning algorithms and input information. This study utilizes an ML algorithm to analyze the impact of 9 parameters on the output characteristics of methylammonium tin halide perovskite (CH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>NH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>SnX<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>) solar cells. The perovskite device structure proposed here is FTO/ETL/CH3NH3SnX3/HTL/Au. Distinct parameters like doping levels, defect densities, and thickness of the diverse materials are analyzed to understand their impacts on device performance. A dataset of 2565 points was created using validated numerical simulations for training and testing the model. The PCE variation across the two metal halides was investigated, yielding a maximum PCE of 24.31% for CH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>NH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>SnBr<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> and $27.37 \%$ for CH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>NH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>SnI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>. Using the SCAPS-1D simulation, the effects of several HTLs are examined. The device’s PCE rose from $27.37 \%$ to $39.68 \%$ by adjusting intrinsic material factors like absorber layer thickness, HTL thickness, doping, and defect concentration. A trained ML model further predicts PSC performance metrics with 91% accuracy. Consequently, the Al/FTO/PCBM/CH3NH3SnI3/V2O5/Au configuration is identified as the most suitable for manufacturing highly efficient photovoltaic devices. Lead (geology) ; Perovskite (structure) ; Solar cell ; Computer science ; Architecture ; Materials science ; Optoelectronics ; Engineering ; Geology ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Jianbo Hu
Source: Separation and Purification Technology (article, 2024-09-30)
Field: Chemistry — Inorganic Chemistry

Design principles for selective nitrogen adsorption via chelation of metals in metal organic frameworks Chelation ; Adsorption ; Metal-organic framework ; Nitrogen ; Metal ; Chemistry ; Inorganic chemistry ; Environmental chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Zeolite Catalysis and Synthesis
Authors: Anis Biswas | Ajay Kumar | Prashant Singh | Tyler Del Rose | Rajiv K. Chouhan | B. C. Margato | B.P. Alho | E.P. Nóbrega | P.J. von Ranke | P.O. Ribeiro | V.S.R. de Sousa | Yaroslav Mudryk
Source: arXiv (Cornell University) (preprint, 2024-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The influence of Ga doping on magnetic properties, magnetocaloric effect, and electronic structure of pseudo-binary GdZn1-xGax (x = 0-0.1) We explore the impact of introducing IIIA-group element Ga in place of IIB-group element Zn in binary intermetallic GdZn on its magnetic and magnetocaloric properties, as well as explicate the modified electronic band structure of the compound. The magnetic transition temperature of the compound decreases with the increase of Ga concentration in GdZn1-xGax (x = 0-0.1) while the crystal structure (CsCl-prototype) and lattice parameters remain unchanged. Our detailed analysis of magnetization and magnetocaloric data conclusively proves that long-ranged magnetic ordering exists in the sample, despite the magnetic interaction considerably weakening with the increase of Ga. The experimental data is rationalized using both theoretical machine learning model and first-principle density functional theory.The electronic band structure of GdZn is manifested with some unusual complex features which gradually diminish with Ga doping and conventional sinusoidal feature of Ruderman-Kittel-Kasuya- Yosida (RKKY)-type interactions also disappears. A mean-field theory model is developed and can successfully describe the overall magnetocaloric behavior of the GdZn1-xGax series of samples Magnetic refrigeration ; Doping ; Materials science ; Binary number ; Condensed matter physics ; Optoelectronics ; Magnetization ; Magnetic field ; Physics ; Mathematics Magnetic and transport properties of perovskites and related materials