jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. Alias | Mohd Firdaus Hassan | Nasrul Azuan Alang
Source: Corrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Corrosion performance prediction of self-healing smart coatings on AZ31 magnesium alloys using feedforward neural network Investigating the self-healing behaviors of smart coatings incorporating microencapsulated linseed oil and honey as a corrosion protection method for AZ31 magnesium alloy is both time-consuming and expensive. To address these limitations, this paper proposes a feedforward neural network (FNN) to predict corrosion behavior using experimental data from the smart-coated alloy. The neural network was trained using corrosion data at a single stirring speed of 800 rpm and validated against three distinct datasets, including the original training data at 800 rpm, a separate dataset at the same speed, and additional experimental data obtained at 1100 rpm. The results show that FNN achieved strong predictive accuracy (less than 5% error) for corrosion potentials under matching 800 rpm conditions, though performance declined at 0% (10.09% error) and 40% honey concentrations (5.82% error). While effectively modeling fundamental potential–current density relationships at trained conditions (800 rpm), the accuracy of FNN diminished when tested at 1100 rpm, revealing sensitivity to stirring speed variations. However, the application of neural networks to predict corrosion behavior in smart-coated magnesium alloys shows significant promise. This approach improves the accuracy of understanding complex corrosion mechanisms in magnesium alloys, enabling the development. Corrosion ; Magnesium ; Materials science ; Self-healing ; Feedforward neural network ; Magnesium alloy ; Artificial neural network ; Feed forward ; Metallurgy ; Computer science ; Artificial intelligence ; Engineering ; Medicine ; Control engineering Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; High-Temperature Coating Behaviors
Authors: Mohammad Amin Adoul | Balaji Subramanian | Naveen Venkatesh Sridharan | Ramin Karim | Ravdeep Kour
Source: Fuel Processing Technology (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Experimental investigation and machine learning-based estimation of oxyhydrogen (HHO) gas production using KOH electrolyte in a flat plate electrolyser Hydrogen gas has gained significant attention as a cleaner alternative to fossil fuels offering a sustainable energy solution. This study explores the production efficiency of oxyhydrogen (HHO) gas using a flat plate electrolyser with potassium hydroxide (KOH) as the electrolyte. Machine learning regression models were employed to estimate hydrogen generation rates and system efficiency based on key operational parameters that includes voltage, current and electrolyte concentration. A set of gradient-boosting algorithms was evaluated utilizing raw experimental data to predict (i) hydrogen output in liters per minute (LPM) and (ii) system efficiency. The results indicate that Categorical Boosting (CatBoost) excelled in forecasting system efficiency (R 2 = 0.9748, RMSE = 1.6567 on testing data) and predicting HHO gas generation rate (R 2 = 0.9936, RMSE = 0.0090). The experimental results show that with the increase in KOH concentration there is increase in production of Hydrogen. Maximum efficiency was noted with 0.5 N of KOH with the peak efficiency of 99.8 % because of its optimal conductivity and power consumption. It can also be absorbed that higher concentration such 0.75 N and 1 N have shown significant improvement in hydrogen production. Experimental findings further revealed that moderate operating conditions maximize hydrogen production with efficiency varying as a function of applied current and electrolyte concentration. This study highlights the advantages of integrating machine learning models with electrolysis-based hydrogen production offering a scalable and data-driven approach to optimizing energy efficiency. The results underscore the potential of KOH-based electrolysis for sustainable hydrogen generation and reinforce the role of predictive modeling in enhancing system performance. • A wet-type flat-plate electrolyser is analyzed for efficiency and gas output. • 4 concentration of KOH electrolyte were considered. • Experimental results were evaluated with ML algorithms. • Five gradient boosting ML algorithms were assessed. • CatBoost achieves R 2 of 0.9936 for gas output and 0.9748 for efficiency. Hydrogen production ; Electrolyte ; Hydrogen ; Electrolysis ; Power to gas ; High-temperature electrolysis ; Potassium hydroxide ; Fossil fuel ; Hydrogen fuel Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Fuel Cells and Related Materials
Authors: Shoeb Athar | Adrien Mecibah | Philippe Jund
Source: Materials Today Physics (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials Machine Learning (ML) driven discovery of novel and efficient thermoelectric (TE) materials warrants experimental TE datasets of high volume, diversity, and quality. While the largest publicly available dataset, Starrydata2, has a high data volume, it contains inaccurate data due to the inherent limitations of Large Language Model (LLM)-assisted data curation, ambiguous nomenclature and complex formulas of materials in the literature. Another unaddressed issue is the inclusion of multi-source experimental data, with high standard deviations and without synthesis information. Using half-Heusler (hH) materials as an example, this work is aimed at first highlighting these errors and inconsistencies which cannot be filtered with conventional dataset curation workflows. We then propose a statistical round-robin error-based data filtering method to address these issues, a method that can be applied to filter any other material property. Lastly, a hybrid dataset creation workflow, involving data from Starrydata2 and manual extraction, is proposed and the resulting dataset is analyzed and compared against Starrydata2. Filter (signal processing) ; Data curation ; Thermoelectric materials ; Work (physics) ; Experimental data ; Inclusion (mineral) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Hao Qu | Tao Hu | Mingjun Li | Jiangyu Yang | Yan Zhou | Shichang Li | Dengfeng Li | Gang Tang | Chunbao Feng
Source: Chinese Physics B (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Unveiling stable and efficient antiperovskite semiconductors via high-throughput computation and interpretable machine learning Abstract Nitride antiperovskites have recently been theoretically identified as promising optoelectronic materials, yet their chemical space remains largely unexplored. Here, we employ a high-throughput first-principles screening workflow to systematically investigate the X 3 BA antiperovskite family. Six candidates that exhibit both structural and dynamical stability together with desirable bandgaps are identified. Electronic-structure calculations reveal that the alkaline-earth-based compounds (e.g., Ca 3 AsSb, Sr 3 AsSb, Ba 3 AsSb) not only possess suitable direct bandgaps and strong optical absorption, but also exhibit favorable ambipolar carrier mobilities and low exciton binding energies (&lt; 45 meV). Notably, Sr 3 AsSb and Ba 3 AsSb are predicted to achieve theoretical maximum power-conversion efficiencies of 28.1% and 29.4%, respectively. Finally, an interpretable machine-learning model demonstrates that the electronegativity of the A-site anion is the single most influential descriptor governing bandgap trends across the chemical space. This work establishes a data-driven design heuristic and provides a predictive framework for the accelerated discovery of efficient and stable antiperovskite-based optoelectronic materials. Electronegativity ; Antiperovskite ; Stability (learning theory) ; Semiconductor ; Band gap ; Ambipolar diffusion ; Workflow ; Exciton ; Computation Thermal Expansion and Ionic Conductivity ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Beriham Basha | Mudassir Hussain Tahir | Tagir Kadyrov | Norah Salem Alsaiari | M.S. Al‐Buriahi
Source: Chemical Engineering Science (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction of melting points of non-fullerene electron acceptors, chemical space generation and visualization Fullerene ; Visualization ; Chemical space ; Space (punctuation) ; Electron ; Materials science ; Melting point ; Computer science ; Chemical physics ; Artificial intelligence ; Chemistry ; Nanotechnology ; Biological system ; Physics ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Carbon Nanotubes in Composites
Authors: Vipin Kurian Elavunkel | Prahallad Padhan
Source: ACS Applied Energy Materials (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half-Heusler Alloys The thermoelectric properties of half-Heusler (HH) alloys are predicted using an ensemble modeling approach, specifically a stacking model that integrates random forest and XGBoost algorithms. Leveraging a diverse data set encompassing thermal conductivity (κ), electrical conductivity (σ), Seebeck coefficient (S), and the figure of merit (ZT), the study demonstrates superior accuracy with R2 scores of 0.93 for κ, 0.96 for σ, 0.99 for S, and 0.92 for ZT. Feature importance analysis revealed that temperature, mean covalent radius, and atomic volume deviations critically influence thermoelectric behavior. The stacking model demonstrates a clear advantage by substantially reducing both the mean absolute error and root-mean-square error across all predicted thermoelectric properties, indicating enhanced prediction accuracy and reliability compared to individual base models. The model was validated against first-principles density functional theory calculations for representative compounds ErNiBi, YPtBi, and YPdSb, showing strong agreement and capturing real-world experimental effects such as nonmonotonic variations and peak values prior to high-temperature decline─phenomena that simplified density functional theory struggles to encapsulate. This comprehensive approach highlights the effectiveness of combining machine learning with physical descriptors to predict complex thermoelectric properties, offering valuable insights for the strategic design of high-performance HH thermoelectric materials. The ensemble modeling framework, coupled with thoughtful feature selection and meticulous engineering, establishes a robust foundation for future research aimed at developing high-performance thermoelectric materials. Thermoelectric effect ; Stacking ; Thermal conductivity ; Thermoelectric materials ; Random forest ; Seebeck coefficient ; Ensemble learning Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Alice E. A. Allen | Rui Li | Sakib Matin | Xing Zhang | Benjamin Nebgen | Nicholas Lubbers | Justin S. Smith | Richard A. Messerly | Sergei Tretiak | Garnet Kin‐Lic Chan | Kipton Barros
Source: ArXiv.org (preprint, 2025-09-13)
Field: Materials Science — Materials Chemistry

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly describe bond breaking and making energetics. Commonly used approaches such as Density Functional Theory (DFT) frequently fail for this task due to deficiencies that are well recognized. However, for high-fidelity approaches, creating large datasets of energies and forces for reactive processes to train machine learning interatomic potentials or force fields is daunting. For example, the use of the unrestricted coupled cluster level of theory has previously been seen as unfeasible due to high computational costs, the lack of analytical gradients in many computational codes, and additional challenges such as constructing suitable basis set corrections for forces. In this work, we develop new methods and workflows to overcome the challenges inherent to automating unrestricted coupled cluster calculations. Using these advancements, we create a dataset of gas-phase reactions containing energies and forces for 3119 different organic molecules configurations calculated at the gold-standard level of unrestricted CCSD(T) (coupled cluster singles doubles and perturbative triples). With this dataset, we provide an analysis of the differences between the density functional and unrestricted CCSD(T) descriptions. We develop a transferable machine learning interatomic potential for gas-phase reactions, trained on unrestricted CCSD(T) data, and demonstrate the advantages of transitioning away from DFT data. Transitioning from training to DFT to training to UCCSD(T) datasets yields an improvement of more than 0.1 eV/Å in force accuracy and over 0.1 eV in activation energy reproduction. Density functional theory ; Coupled cluster ; Cluster (spacecraft) ; Basis (linear algebra) ; Workflow ; Task (project management) ; Set (abstract data type) ; Cluster analysis ; Potential energy Machine Learning in Materials Science
Authors: Luis Itza Vazquez-Salazar | Markus Meuwly
Source: Journal of Chemical Information and Modeling (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space Machine learning (ML) has become a standard tool for the exploration of the chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for "chemical tasks" including the prediction of hybridization, oxidation, substituent effects, and aromaticity, starting from an initial "restricted" database (iRD). Choosing molecules for augmenting this iRD, including increasing numbers of conformations generated at different temperatures, and retraining the models can improve predictions of the models on the selected "tasks". Addition of a small percentage of conformations (1%) obtained at 300 K improves the performance in almost all cases. On the other hand, and in line with previous studies, redundancy and highly deformed structures in the augmentation set compromise prediction quality. Energy and bond distributions were evaluated by means of Kullback-Leibler (<i>D</i><sub>KL</sub>) and Jensen-Shannon (<i>D</i><sub>JS</sub>) divergence and Wasserstein distance (<i>W</i><sub>1</sub>). The findings of this work provide a baseline for the rational augmentation of chemical databases or the creation of synthetic databases. Chemical space ; Computer science ; Divergence (linguistics) ; Database ; Chemical database ; Space (punctuation) ; Redundancy (engineering) ; Machine learning ; Artificial intelligence ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Florian Kiehas | Martin Reiter | F. Rueda | Juan Pablo Torres | Michael Jerabek | Zoltán Major
Source: Journal of Dynamic Behavior of Materials (article, 2025-06-04)
Field: Materials Science — Polymers and Plastics

Modelling Polymers Under Impact Loading: A Fast Calibration Strategy Using Machine Learning Abstract For polymers, inelastic deformation and failure under dynamic loading is largely dependent on temperature, strain rate and stress multiaxiality. Consequently, modelling their impact behavior requires several experiments and time-consuming determination of material parameters. In this work, a calibration strategy to reduce calibration times and experimental workloads based on neural networks and genetic algorithms is presented. The method is demonstrated on Charpy- and puncture experiments at 23 $$ ^{\circ }\text {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> and 50 $$ ^{\circ }\text {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> with two additional tensile tests at $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 30 $$ ^{\circ }\text {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> and 23 $$ ^{\circ }\text {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> . In many cases, these experiments are conducted for datasheet specifications and are readily available. To achieve a balance between precision, ability to generalize and numerical simplicity, the strain-rate and temperature dependence was mapped via a Johnson–Cook model that creates tabular inputs for a general J2 plasticity model in Abaqus. Similarly, failure behavior was modelled using ductile-damage in Abaqus. The traditional fitting methodology based on inverse analysis using finite element simulations was replaced by the use of neural network surrogate models which allow for calibration in a matter of minutes. This approach was validated against four-point bending experiments with geometry, velocity and temperatures different to those used to calibrate the model on. Here, simulated toughness and limit loads on average differed by 19% and 5% from experimental results. Calibration ; Solid mechanics ; Materials science ; Polymer ; Composite material ; Computer science ; Process engineering ; Mechanical engineering ; Engineering ; Mathematics ; Statistics Polymer crystallization and properties ; Probabilistic and Robust Engineering Design ; Force Microscopy Techniques and Applications
Authors: Hui-Qi Mo | Cheng Hou
Source: Organic Chemistry Frontiers (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Mechanistic and machine learning insights into borrowing hydrogen reactions catalyzed by transition metal complexes with N-heterocyclic ligands DFT and machine learning uncover the N2-assisted outer-sphere pathway as the most favorable in pyrazole-based BH catalysis, with the HOMO level, dipole moment, and steric effects identified as key performance-determining factors. Steric effects ; Catalysis ; Transition metal ; Key (lock) ; Transition (genetics) ; Hydrogen Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Ailar Baghervand | S. Farhad Masoudi | Hassan Yousefnia | Fatemeh S. Rasouli
Source: Applied Radiation and Isotopes (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Machine learning predictions of cross-sections for the 232Th(p,x)225Ac reaction Materials science ; Radiochemistry ; Chemistry Nuclear Materials and Properties ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Yuqing Qiu | Zhiyuan Zhang | Zhen‐Wu Shao | Yue Dong | Chaozhi Xiong | Li Xiong | Dongsheng Yang | Y. K. Que | Shiyi Jiang | Chong Liu
Source: ACS Applied Materials & Interfaces (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Virtual Database Construction and Machine-Learning-Assisted High-Throughput Evaluation of Amorphous Porous Carbon Materials as Iodine Sorbents We present a comprehensive approach to enable the high-throughput screening and analysis of amorphous porous carbon (APC) materials as effective I<sub>2</sub> sorbents for the nuclear industry. A diverse virtual database of 19,599 APC models was established from scratch through liquid quenching molecular dynamics simulations. Large-scale grand canonical Monte Carlo simulation at a series of I<sub>2</sub> concentrations was carried out for sampled APCs to generate an array of I<sub>2</sub> adsorption capacities. Machine learning and SHapley Additive exPlanations (SHAP) analysis were employed to investigate the impact of various extracted (structural and chemical) features of the APC materials on their respective I<sub>2</sub> adsorption behavior, revealing influential factors (surface area, pore size ranges, etc.) for APC development that varied with I<sub>2</sub> concentrations. This work attempts to provide both fundamental databases and research frameworks to accelerate the development and enhance the understanding of APC materials. Materials science ; Throughput ; Porosity ; Amorphous solid ; Carbon fibers ; Amorphous carbon ; Nanotechnology ; Chemical engineering ; Computer science ; Composite material ; Organic chemistry ; Engineering ; Operating system Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors ; Radioactive element chemistry and processing
Authors: Hongwei Yan | Qingshan Yang | Dan Zhang | Guanglin Liu | Xianzheng Li | Fei Ren | Liyang Yue | Bin Jiang
Source: Materials Today Communications (article, 2025-03-01)
Field: Materials Science — Biomaterials

Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network Materials science ; Artificial neural network ; Texture (cosmology) ; Magnesium ; Magnesium alloy ; Alloy ; Metallurgy ; Artificial intelligence ; Computer science ; Image (mathematics) Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Jinpeng Wang | Wei Deng | Ming Hu | Yuehua Zhao | Bo Peng | Dapeng Wang
Source: Macromolecular Rapid Communications (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

In Situ Probing of Polymer Residence on the Nanoparticle Surface by Combining Deep Learning and Single‐Molecule Fluorescence Tracking This study presents a method for in situ analysis of the adsorption and desorption of polymers on the nanoparticle surface within entangled polymer solutions. This method is based on the principle that when a polymer adsorbs onto the nanoparticle surface, the diffusion coefficient of the polymer becomes equivalent to that of the nanoparticle. Consequently, adsorption events can be identified by properly detecting diffusion-state transitions in trajectories acquired through single-molecule fluorescence tracking experiments. This method, which involves numerically generated trajectories, training of a 1D convolutional neural network (1D-CNN), and state prediction, was validated experimentally. When the nanoparticle-to-polymer diffusion coefficient ratio is as high as 0.5, the trained 1D-CNN model can still achieve 85% accuracy and an F1 score of 0.86 in adsorption-state identification. These results significantly outperform those of the hidden Markov model and the threshold-based method. Using the proposed method, we found that in aqueous solutions containing entangled poly(ethylene glycol) (PEG) chains and silica nanoparticles of varying sizes, the adsorption probability of PEGs onto the nanoparticle surface decreases, while the adsorption duration increases as the nanoparticle size decreases. Nanoparticle ; Adsorption ; Polymer ; Materials science ; Diffusion ; Ethylene glycol ; Desorption ; Tracking (education) ; Molecule ; Fluorescence ; Chemical engineering ; Nanotechnology ; Chemistry ; Physical chemistry ; Thermodynamics ; Optics ; Physics ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods