jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Omotayo Sanni | Oluwatobi Adeleke | Samuel A. Iwarere | Tien-Chen Jen | Michael O. Daramola
Source: Materials Today Communications (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Application of machine learning in predicting corrosion inhibition capacity of Spinacia oleracea leaf extract on copper In many different industries, material corrosion is a major problem and it has a big financial impact. In this case, plant extracts employed as corrosion inhibitors, provide affordable way to prevent copper from corroding in acidic media, providing an alternative to the dangerous chemicals now in use. This study presents an integrated experimental and machine learning approach for investigating the corrosion inhibition performance of Spinacia oleracea leaf extract on copper in nitric acid medium. Experimental procedure involving gravimetric analysis under different concentrations, temperatures, and exposure durations, inhibitory efficiency was conducted. Different machine learning (ML) models, namely Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (tree) were developed for predicting the corrosion rate. To overcome the black-box limitation of the ML models, an interpretable feature analysis was carried using Shapley Additive ExPlanations (SHAP).The accuracy and validity of the models were evaluated using statistical tests like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Variance Accounted For (VAF).The best prediction accuracy was obtained with SVM, giving an averaged validation-based RMSE, MAE, MAD, MAPE, and VAF values of 0.386, 0.287, 0.192,0.08773, and 99.093, respectively, across 5 folds. SHAP interpretability identified inhibitor concentration as the most influential variable controlling corrosion inhibition. The data-driven framework that combines experimental gravimetric analysis with SHAP-enhanced ML in this study contributes to the broader development of transparent, eco-friendly, and data-driven corrosion prediction models. Corrosion ; Mean absolute percentage error ; Interpretability ; Random forest ; Artificial neural network ; Gravimetric analysis ; Copper ; Spinacia ; Mean squared error Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Chenyang Wei (1569127) | Yutong Shi (6006209) | Wenbo Mu (841096) | Hongyuan Zhang (2358817) | Rui Qin (533202) | Yijun Yin (274233) | Gangqiang Yu (3698149) | Tiancheng Mu (1319817)
Source: Figshare (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Large Language Models Assisted Materials Development:Case of Predictive Analytics for Oxygen Evolution Reaction Catalystsof (Oxy)hydroxides This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in materials science, leveraging large language models (LLMs) such as OpenAI’s ChatGPT. Focusing on (oxy)­hydroxides as oxygen evolution reaction (OER) catalysts, we demonstrate how LLMs streamline data extraction, significantly reducing reliance on traditional, time-intensive methods. Using few-shot training and strategic prompting, ChatGPT achieved an extraction accuracy of approximately 0.9. The curated data set was then used to predict OER performance via the PyCaret library to evaluate various ML algorithms and a high-accuracy XGBoost regression model with accuracies above 0.9 is subsequently established. Further analysis using SHAP and Python Symbolic Regression (PySR) identified key descriptors-electrochemical double-layer capacitance, transition metal composition, support material, and d-electron count-as critical factors, consistent with established electrochemical principles. Additionally, SHAP’s extreme values for Cu and Zn suggest unconventional catalytic roles, potentially linked to Cu<sub>2</sub>O-facilitated NiOOH formation and Zn-induced electronic modulation, demonstrating the power of data-driven analysis in uncovering hidden mechanisms. To enhance literature-based insights, Microsoft’s GraphRAG technology was employed for in-depth chemical information retrieval. Overall, this study introduces an innovative, end-to-end ML framework powered by ChatGPT, promoting broader AI adoption in scientific research and bridging computational intelligence with experimental sciences. Big data ; Python (programming language) ; Transformative learning ; Predictive power ; Analytics ; Bridging (networking) ; Predictive analytics ; Regression analysis ; Data analysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Balvinder Kaur | Pardeep Singh | Sonu Sonu | Archana Singh | Quyet Van Le | Van‐Huy Nguyen | Aftab Aslam Parwaz Khan | Mohammad Asad | Pankaj Raizada | Khalid A. Alzahrani
Source: Journal of Physics and Chemistry of Solids (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Efficient surface defect engineering in boron nitride for advanced photocatalytic applications: DFT analysis, strategies, and catalytic performance Photocatalysis ; Boron nitride ; Heterojunction ; Charge carrier ; Band gap ; Semiconductor ; Boron ; Catalysis ; Surface engineering ; Surface charge Graphene research and applications ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Łukasz Lamch | Krzysztof Gawarecki | Dawid Szarpak | Kazimiera A. Wilk
Source: Chemical and Process Engineering New Frontiers (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

HSPiP software as a powerful tool for optimization studies in chemical nanoengineering The prediction of physicochemical parameters of novel compounds and their blends is an emerging problem in chemical nanoengineering, especially in the fields of fine chemicals and specialty polymers. Hansen solubility parameters in practice (HSPiP) software enables one to get valuable data from the known chemical structure on the basis of appropriate, standardized experiment, e.g. dissolution or swelling in a particular set of solvents. Herein we present a brief description of the recent applications of HSPiP software, supported by experimental and theoretical methods, including machine learning clusterization, aimed to solve technological and engineering issues. The present contribution aims to show the combined way of solvent clusterization for HSPiP studies for the determination of solubility parameters for novel surfactants, polymer blending toward drug nanocarriers as well as design of silicone-based polymer nanomatrix. The examples comprise the recent contribution of the groups’ research, previously published or studied in current projects of our groups. Nanoengineering ; Software ; Computer science ; Systems engineering ; Biochemical engineering ; Computational biology ; Engineering ; Nanotechnology ; Biology ; Materials science ; Programming language Machine Learning in Materials Science
Authors: José M. Fernandez | María López‐Abelairas | Arturo Sánchez-Ramos | Lila Otero-González
Source: Open Research Europe (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Development and comparative analysis of machine learning algorithms for predictive atmospheric corrosion modeling <ns3:p>Background Industrial content and infrastructure are in constant danger from atmospheric corrosion, which affects economies globally. However, there is a lack of a consistent set of comprehensive data that completely surrounds the range of this problem in diverse climate and locations. The purpose of the research is to evaluate the factors that contribute to atmospheric corrosion and its diverse effects on materials in various environments. Methods By creating a comprehensive dataset by collecting and standardizing corrosion data from diverse environments and geographic regions and initially analyzing the data, it helped indicate the main parameters affecting corrosion. This guided the selection of future features for further modeling. Several machine learning algorithms were tested, such as linear regression, decisions tree, neural network, and, most especially, attempting methods, for their corrosion rate prediction capabilities. These models were assessed based on their prediction’s accuracy, and computational efficiency, with special attention to refining their performance through detailed feature engineering and hyperparameter adjustment. Results Upon evaluating the performance of conventional predictive models, the research indicated that the machine learning approaches, especially with random forests methods of dress, were excellent in predicting corrosion rates, significantly improved upon these capabilities. By analyzing various machine learning approaches, it became clear that it was important to enhance their accuracy by selecting the best features and customizing them. Conclusions This work represents a significant advancement in the predictive modeling of atmospheric corrosion. It highlights the invaluable role of machine learning in this field. By integrating varied data sets and applying sophisticated machine learning techniques, it has established a foundation for ongoing research and the practical application of corrosion management strategies. The exceptional performance of ensemble methods, like random forests, signals their potential to improve prediction capabilities and guide more effective corrosion prevention measures.</ns3:p> Computer science ; Machine learning ; Artificial intelligence ; Algorithm Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: <p>Jian Lan</p>
Source: Academic Journal of Materials & Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Performance Prediction of Magnetocaloric Materials Using CNN and SVR In this paper, a hybrid model CvBlock-SVR combining convolutional neural network (CNN) and support vector regression (SVR) is proposed for predicting the properties of magneto-thermal effect materials. The chemical composition descriptors are constructed by Magpie and the material features are augmented using a convolutional block (CvBlock), and the augmented features are subsequently modelled with SVR to accurately predict the transition temperature (T<sub>tr</sub>) of the material. Training the pervasive CvBlock-SVR model on a dataset of all magneto-thermal effect materials achieved R² = 0.871 and MAE = 24.937 K for Ttr prediction.The model was also successfully applied to the prediction of materials space such as Gd-Al, Gd-Co-Al, and Fe<sub>93-x-y</sub>Zr<sub>7</sub>B<sub>x</sub>Cu<sub>y</sub>, which verified its high accuracy and wide applicability. It is shown that the CvBlock-SVR model not only provides accurate prediction results, but also has wide applicability and is suitable to be applied to the prediction of the properties of different types of magneto-thermal effect materials. Magnetic refrigeration ; Materials science ; Computer science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Neural Networks and Applications ; Magnetic and transport properties of perovskites and related materials
Authors: Ana María González Pérez | John M. Findley | Evan Granite | Eric Grol | Janice A. Steckel
Source: AIChE Journal (article, 2025-11-17)
Field: Chemistry — Inorganic Chemistry

Machine learning for optimization of zeolites for boric acid sorption Abstract Machine learning can effectively accelerate materials development in real‐world sorbent applications including clean‐up of polluted impoundment sites. Zeolites synthesized from coal fly ash can adsorb contaminants, such as boric acid, from water. Machine learning models were trained on molecular simulation data to predict boric acid uptake based on zeolite structure, aluminum content, extra‐framework cation species, and boron concentration in solution. Overall, eXtreme Gradient Boosting models yielded the highest speed and accuracy. The models were used with a genetic algorithm to enable concentration‐specific zeolite optimization for coal ash impoundment sites. Results indicate small pore zeolite frameworks such as PHI, CHA, AVE, ERI with low Si/Al ratios and a mix of Na and Ca metal cations are most effective for boric acid removal. Our use of machine learning models with a genetic algorithm has broad implications for machine learning‐aided materials design. Boric acid ; Zeolite ; Adsorption ; Boron ; Sorbent ; Fly ash ; Aluminosilicate Zeolite Catalysis and Synthesis ; Adsorption and biosorption for pollutant removal ; Phosphorus and nutrient management
Authors: Carolin M. Sutter-Fella | Shijing Sun
Source: PRX Energy (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Designing and Utilizing Material Acceleration Platforms: Need for Workforce Development In the quest to accelerate scientific discovery, the materials science field is rapidly moving toward the implementation of robotics and artificial intelligence driven workflows. Our recent summer school “Future Labs: Robotic Synthesis Coupled with Machine Learning for Energy Materials” provided learning opportunities for students, researchers, and educators in the materials science community. We describe this experience and provide our perspective on which new directions could be pursued to enable the future workforce to acquire cross-disciplinary skills. Workforce ; Workforce development ; Field (mathematics) ; Robotics ; Perspective (graphical) ; Robot ; Energy (signal processing) Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Inorganic Chemistry and Materials
Authors: A. D. Egorov | Lei Zhang | E. van der Giessen | Francesco Maresca
Source: ArXiv.org (preprint, 2025-12-14)
Field: Materials Science — Metals and Alloys

To crack, or not to crack: How hydrogen favors crack propagation in iron at the atomic scale Steel is a key structural material because of its considerable strength and ductility. However, when exposed to hydrogen, it is prone to embrittlement. Mechanistic understanding of the origin of hydrogen embrittlement is hampered by the lack of reliable interatomic potentials. Here, we perform large-scale molecular dynamics simulations of crack propagation after having developed and validated an efficient yet density-functional-theory-accurate machine-learning potential for hydrogen in iron. Simulations based on our potential reveal that in the absence of H, iron is intrinsically ductile at finite temperatures with crack-tip blunting assisted by dislocation emission. By contrast, minute (part-per-million) hydrogen concentrations can switch the crack-tip behavior from ductile blunting to brittle propagation. Detailed analysis of our molecular dynamics results reveals that the combination of fast hydrogen diffusion and diminished surface energy is at the origin of embrittlement. Our results set the stage for a modified Griffith's criterion for hydrogen-induced brittle fracture, which closely captures the simulations and that can be used to assess embrittlement in iron-based alloys. Hydrogen embrittlement ; Embrittlement ; Hydrogen ; Brittleness ; Molecular dynamics ; Atomic units ; Dislocation ; Diffusion Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Nuclear Materials and Properties
Authors: Sonali Mehta Patel | Akanksha Dwivedi | G. N. Darwhekar
Source: Premier journal of science. (review, 2025-07-26)
Field: Materials Science — Materials Chemistry

AI-Driven Advancements in Biomaterials Science: A Narrative Review INTRODUCTION Biomaterials have emerged as a key component of contemporary medicine, propelling advancements in drug delivery, implants, and regenerative medicine. But conventional trial-and-error methods of finding new materials are sometimes cumbersome, resource-intensive, and ill-equipped to meet the demands of individual patients. AI IN HEALTH CARE By facilitating intelligent data analysis, diagnosis, and individualized therapy, artificial intelligence (AI), in particular, machine learning, deep learning, and data mining, has become a disruptive force in the health care industry. Its incorporation into biomaterials research opens up new avenues for clinical translation and innovation. PREDICTIVE MODELING AI systems are able to analyze sizable and intricate biological and material information in order to forecast attributes like mechanical strength, toxicity, biocompatibility, and in vivo response. These predictive skills enhance preclinical research ethics while speeding up the identification of biomaterials. DESIGN AND DEVELOPMENT AI makes it possible to create and modify biomaterials that are suited to a certain illness or a patient’s unique circumstances. Targeted medication delivery systems, customized implants, and physiologically sensitive smart materials are a few examples of applications. Materials informatics and high-throughput screening drastically cut down on development time and expense. FUTURE PROSPECTS In spite of its potential, integrating AI into biomaterials presents difficulties, including the requirement for reliable data privacy frameworks, transparent algorithms, and standardized, high-quality datasets. To get over these obstacles, multidisciplinary cooperation between data scientists, physicians, materials experts, and regulators is crucial. CONCLUSION AI is changing the biomaterials industry by improving the accuracy and efficiency of material design, selection, and testing. AI will continue to play a key role in developing next-generation biomaterials for predictive and individualized health care with sustained improvements and cooperative efforts. Narrative review ; Narrative ; Engineering ethics ; Nanotechnology ; Data science ; Computer science ; Materials science ; Engineering ; Psychology ; Art ; Psychotherapist ; Literature Machine Learning in Materials Science ; 3D Printing in Biomedical Research ; Artificial Intelligence in Healthcare and Education
Authors: Abhinav S. Raman
Source: Journal of the Indian Institute of Science (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Computational Modeling of Solid–Liquid Interfaces: From Ab Initio Methods to Machine-Learning Potentials Ab initio ; Computer science ; Computational chemistry ; Artificial intelligence ; Materials science ; Computational science ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Radioactive element chemistry and processing
Authors: Christophe Chipot | Sergio Contreras Arredondo | Chenyu Tang | Radu A. Talmazan | Alberto Megías | Cheng Giuseppe Chen
Source: Research Square (preprint, 2025-08-26)
Field: Materials Science — Materials Chemistry

From Atoms to Dynamics: Learning the Committor Without Collective Variables Dynamics (music) ; Psychology ; Statistical physics ; Physics ; Pedagogy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Physical and Chemical Molecular Interactions
Authors: Nguyen
Thien Phuc Tu (19459663) | Alexander L. M. Knoll (22661967) | Jörg Behler (1304340) | Christopher N. Rowley (160030)
Source: Figshare (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Long-Range Interactionsin High-Dimensional NeuralNetwork Potentials: A Benchmark Study for Small Organic Molecules Many machine learning potentials (MLPs) rely on representations of the total energy in terms of the positions of the atoms in their local environment, using either a cutoff radius or a limited number of message-passing layers. This restricts their ability to model long-range intermolecular interactions accurately. This limitation can be addressed by explicitly incorporating long-range electrostatic and dispersion interactions into the MLP framework. In this paper, we investigate the impact of augmenting high-dimensional neural network potentials (HDNNPs) with both electrostatic and dispersion corrections on the prediction of gas-phase intermolecular interactions between small organic molecules. We employ a machine learning-based charge equilibration (QEq) scheme to model electrostatics and the Machine-Learning eXchange-hole Dipole-Moment (MLXDM) model to account for dispersion. The resulting model, CombineNet, integrates these long-range terms with short-range atomic energies trained on density functional theory (DFT) data and achieves a low mean absolute error (MAE) of 0.59 kcal/mol (root-mean-square error (RMSE) of 3.38 meV/atom) against CCSD(T)/CBS benchmarks on the DES370K test set. Notably, electrostatic interactions derived from Hirshfeld charges tend to underestimate long-range effects, whereas the minimal basis iterative stockholder (MBIS) charges yield more accurate interaction trends. For reliable modeling of molecular dimers, the training set must capture both the dissociation limit and the transition region near the cutoff radius. Cutoff ; Artificial neural network ; Electrostatics ; Intermolecular force ; Dispersion (optics) ; Limit (mathematics) ; Dissociation (chemistry) ; Charge (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: J.M. Moree | Juba Bouaziz | Ryotaro Arita
Source: ArXiv.org (preprint, 2025-10-21)
Field: Materials Science — Materials Chemistry

Uncovering critical temperature dependence in Heusler magnets via explicit machine learning We employ interpretable explicit machine learning to analyze the material dependence of the magnetic transition temperature $T_c$ in ferromagnetic and ferrimagnetic Heusler compounds. For around 200 compounds, we consider both experimental $T_c$ and calculated $T_c$ using \textit{ab initio} determination of magnetic interactions together with a Monte-Carlo solution. We use the hierarchical dependence extraction (HDE) procedure [Morée and Arita, Phys. Rev. B 110, 014502 (2024)] to extract the dependencies of $T_c$ on chemical proportions and magnetic moments from the main order to the higher order, and construct an explicit expression of $T_c$ from these dependencies. The main results are: (a) $T_c$ is mainly controlled by the proportions of Fe, Co, and Mn, and increases with these proportions, consistent with previous machine learning analyses of ferromagnetic materials. (b) The HDE describes $T_c$ with an accuracy that is comparable to that of other machine learning procedures. (c) The HDE expression of $T_c$ can be interpreted as a generalized order parameter that increases with increasing magnetization amplitude, in qualitative agreement with various theories of phase transitions. These results strengthen our understanding of the material dependence of $T_c$ in collinear Heusler magnets and motivate the further use of HDE in material design. Ferrimagnetism ; Ferromagnetism ; Magnet ; Magnetization ; Magnetic moment ; Phase transition ; Extraction (chemistry) Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity