jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Urban Tomc | Guilherme Fidelis Peixer | C.R.H. Bahl | K. Nielsen | Jaime A. Lozano | Jader R. Barbosa | Andrej Kitanovski
Source: Advanced Functional Materials (article, 2025-09-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of Layering and Curie Temperature Uncertainty on the Performance of Multilayer Active Magnetic Regenerators Abstract Magnetic refrigeration is a promising alternative to traditional vapor compression systems, with potential efficiency and environmental sustainability advantages. However, the narrow operational temperature range of magnetocaloric materials (MCMs) and their reliance on rare‐earth elements remain key challenges. Multilayer active magnetic regenerators (AMRs) address the temperature range limitations by combining multiple magnetocaloric layers, each with different Curie temperatures. This study investigates the impact of statistical deviations in Curie temperatures on the performance of multilayer AMRs, specifically using second‐order La‐Fe‐Co‐Si materials. A 1D multilayer AMR numerical model is developed to simulate the effects of Curie temperature variability, with radial basis function neural networks employed to efficiently predict performance. The results indicate that although increasing the number of MCM layers enhances cooling power and coefficient of performance (COP) , Curie temperature uncertainties significantly degrade the AMR performance. The likelihood of achieving cooling targets diminishes as the number of MCM layers increases, particularly for standard deviations exceeding 1 K. These findings emphasize the importance of accounting for Curie temperature uncertainties in AMR design. Moreover, enhancing the manufacturing precision of the Curie temperatures of MCMs is essential for improving the performance and commercialization of magnetocaloric technology. Curie temperature ; Magnetic refrigeration ; Curie ; Atmospheric temperature range ; Curie constant ; Refrigerant ; Vapor-compression refrigeration Magnetic and transport properties of perovskites and related materials
Authors: Simone Ritarossi | Riccardo Piombo | Fernando C. Giuliani | Daniele Dragoni | Marco Bernasconi | Riccardo Mazzarello
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Phase‐Change Heterostructures Based on Antimony Phase‐change materials (PCMs) are employed in optical and electronic storage devices and are interesting candidates for neuromorphic computing. However, they exhibit some drawbacks that limit their application in this field, including a temporal drift in resistance and a stochastic variability of the conductance. To eliminate these problems, it has been proposed to use phase‐change heterostructures made of alternating thin layers of PCMs and proper confinement materials, such as transition metal dichalcogenides. Herein, superlattice heterostructures consisting of TiTe 2 and antimony are investigated by ab initio methods and neural‐network interatomic potentials. The structural and kinetic properties of the relevant phases are characterized. A complete switching cycle is simulated and it is shown that very high quenching rates must be used to generate stable amorphous Sb layers. It is also shown that it is possible to switch the Sb layer without destroying the crystalline structure of TiTe 2 , which makes these superlattices potential candidates for neuro‐inspired applications that do not require long retention times, such as computing‐in‐memory tasks. Antimony ; Heterojunction ; Phase (matter) ; Phase change ; Materials science ; Optoelectronics ; Chemistry ; Engineering physics ; Physics ; Metallurgy Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Transition Metal Oxide Nanomaterials
Authors: Arpan Mukherjee | Deepesh Giri | Krishna Rajan
Source: Journal of Chemical Information and Modeling (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Uncertainty-Informed Screening for Safer Solvents Used in the Synthesis of Perovskites via Language Models Automated data curation for niche scientific topics, where data quality and contextual accuracy are paramount, poses significant challenges. Bidirectional contextual models such as BERT and ELMo excel in contextual understanding and determinism. However, they are constrained by their narrower training corpora and inability to synthesize information across fragmented or sparse contexts. Conversely, autoregressive generative models like GPT can synthesize dispersed information by leveraging broader contextual knowledge and yet often generate plausible but incorrect ("hallucinated") information. To address these complementary limitations, we propose an ensemble approach that combines the deterministic precision of BERT/ELMo with the contextual depth of GPT. We have developed a hierarchical knowledge extraction framework to identify perovskites and their associated solvents in perovskite synthesis, progressing from broad topics to narrower details using two complementary methods. The first method leverages deterministic models like BERT/ELMo for precise entity extraction, while the second employs GPT for broader contextual synthesis and generalization. Outputs from both methods are validated through structure-matching and entity normalization, ensuring consistency and traceability. In the absence of benchmark data sets for this domain, we hold out a subset of papers for manual verification to serve as a reference set for tuning the rules for entity normalization. This enables quantitative evaluation of model precision, recall, and structural adherence while also providing a grounded estimate of model confidence. By intersecting the outputs from both methods, we generate a list of solvents with maximum confidence, combining precision with contextual depth to ensure accuracy and reliability. This approach increases precision at the expense of recall─a trade-off we accept given that, in high-trust scientific applications, minimizing hallucinations is often more critical than achieving full coverage, especially when downstream reliability is paramount. As a case study, the curated data set is used to predict the endocrine-disrupting (ED) potential of solvents with a pretrained deep learning model. Recognizing that machine learning models may not be trained on niche data sets such as perovskite-related solvents, we have quantified epistemic uncertainty using Shannon entropy. This measure evaluates the confidence of the ML model predictions, independent of uncertainties in the NLP-based data curation process, and identifies high-risk solvents requiring further validation. Additionally, the manual verification pipeline addresses ethical considerations around trust, structure, and transparency in AI-curated data sets. SAFER ; Computer science ; Chemistry ; Computer security Machine Learning in Materials Science ; Multi-Criteria Decision Making
Authors: Ming Chen | Linfu Zhang | Kang Liu | Qiang Zhu | Jie Xu | Bin Guo | Debin Shan | Hushan Li | Guohua Fan | Tao Yang | Yinghao Zhou | Peng Zhang
Source: Acta Materialia (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Atomic-scale insights into the exceptional pinning effect of the K bubbles on W grain boundary Zener pinning ; Pinning force ; Grain boundary ; Tungsten ; Nanoscopic scale ; Recrystallization (geology) ; Grain growth ; Work (physics) ; Elongation Fusion materials and technologies ; Microstructure and mechanical properties ; Ion-surface interactions and analysis
Authors: Hyo Gyeom Kim | Byung-Moon Jun | Heewon Jeong | Yeomin Yoon | Kyung Hwa Cho
Source: Journal of Water Process Engineering (article, 2025-11-21)
Field: Chemistry — Inorganic Chemistry

Machine learning-based optimization and interpretation of the adsorption capacities of metal-organic frameworks for endocrine-disrupting compounds Interpretability ; Adsorption ; Interpretation (philosophy) ; Convolutional neural network ; Variable (mathematics) ; Process (computing) ; Sorting ; Artificial neural network Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Haoran Li | Pengcheng Wu | Shihong Ding | Tao Chen | Xiaobo Zou | Jisheng Dai
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-03-21)
Field: Chemistry — Analytical Chemistry

A knowledge-informed burst-sparsity learning (BSL) with non-uniform pattern-coupled prior for spectroscopic regression Regression ; Regression analysis ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Kuldeep Singh | Jeetendra Kumar Gupta | Shiv Narayan | Ketki Rani | Divya Jain | Prateek Porwal | Mukesh C. Sharma | Shivendra Kumar
Source: Current Pharmaceutical Biotechnology (article, 2025-02-20)
Field: Chemistry — Organic Chemistry

Advances in Molecular Docking Techniques for Targeting Protein Misfolding in Neurodegenerative Diseases Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's disease, represent a significant global health challenge with limited therapeutic options. Protein misfolding and aggregation, a common pathological hallmark in these disorders, have emerged as promising targets for therapeutic intervention. Molecular docking techniques have played a pivotal role in the identification and design of small molecules that can modulate protein misfolding, offering new hope for effective treatments. This review provides an overview of recent advancements in molecular docking techniques for targeting protein misfolding in neurodegenerative diseases. We discuss the principles and methodologies behind molecular docking, including various scoring functions and algorithms employed for accurate ligand-protein interactions. Additionally, we explore the use of molecular dynamics simulations and machine learning approaches to enhance the precision of docking studies. Furthermore, we highlight case studies and success stories where molecular docking has contributed to the discovery of potential drug candidates for neurodegenerative diseases. These include compounds that inhibit amyloid-β aggregation in Alzheimer's disease, α-synuclein oligomerisation in Parkinson's disease, and mutant huntingtin aggregation in Huntington's disease. We also discuss the problems and restrictions of molecular docking related to neurodegenerative diseases, such as how to accurately show the flexibility of proteins and why docking results need to be confirmed by experiments. We also discuss the structural biology methods, such as cryo-electron microscopy and X-ray crystallography, and how these techniques might help in improving molecular docking studies. Docking (animal) ; Computational biology ; Protein folding ; Chemistry ; Biology ; Medicine ; Biochemistry Click Chemistry and Applications ; Biotin and Related Studies ; Nanoparticle-Based Drug Delivery
Authors: Eman M. Fayyad | Adewale K. Ipadeola | Mostafa H. Sliem | Dana Abdeen | Noora Al‐Qahtani | Ashwin RajKumar | Joel Jeffrey | Phaneendra K. Yalavarthy | Aboubakr M. Abdullah
Source: Emergent Materials (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Interfacial robustness of commercial amine-based inhibitors mitigates under-deposit corrosion of carbon steel in simulated sour conditions: a merged electrochemical and machine learning study Abstract In oil and gas industry, under-deposit corrosion (UDC) of metallic pipelines is a major problem, especially in sour environments. Not much research has been done on the effectiveness of commercial inhibitors with potent interfacial qualities in reducing UDC. In order to prevent sand (SiO 2 )-induced UDC on CS in simulated sour conditions, two different commercial inhibitors, CRONOX-CRW85719 (CR1) and CRONOX-CRW85282 (CR2), were thoroughly tested over a concentration range (5–400 ppm). Performance was evaluated using electrochemical studies, physicochemical characterizations, and machine learning (ML) modeling. The results showed that, at optimal concentrations of 50 ppm and 200 ppm for CR1 and CR2, respectively, there were nearly total inhibition efficiencies (IE), outperforming the corrosion resistance of un-inhibited CS. Nevertheless, after 24 h, the IEs of CR1 and CR2 were reduced by 14.7% and 4.0%, respectively, due to the presence of fully covered SiO 2 deposits on the CS surface, suggesting that deposit coverage reduces inhibitor efficacy. Because of the reduced molecular bulk and improved ability of CR2 to penetrate SiO 2 deposits, it performs better and provides better access to the metal surface relative to CR1. The random forest technique was shown to be the most appropriate predictive ML model, with an optimized mean coefficient of determination ( R 2 = 0.85 ± 0.05), a root mean square error (RMSE = 3.6%), and a mean absolute error (MAE = 2.7%), amongst the various ML models. This study emphasizes how important inhibitor molecular properties are in preventing SiO 2 -induced UDC of CS in sour conditions, especially penetrating ability and strong interfacial contacts. Robustness (evolution) ; Electrochemistry ; Amine gas treating ; Corrosion ; Carbon steel ; Materials science ; Metallurgy ; Computer science ; Environmental science ; Chemistry ; Environmental engineering ; Electrode Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Archana Sasi | Chandrasekar Venkatachalam | R. Sathish Kumar | V. Jeevanantham
Source: Computers in Biology and Medicine (article, 2025-10-27)
Field: Materials Science — Biomaterials

Integrating optimal prediction model with hybrid deep learning for coated metal oxide nanoparticle drug delivery systems Deep learning ; Artificial neural network ; Performance enhancement ; Drug delivery ; Deep neural networks ; Targeted drug delivery ; Hybrid system ; Precision medicine ; Variety (cybernetics) Nanoparticle-Based Drug Delivery ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Peichen Zhong | Bowen Deng | Shashwat Anand | Tara P. Mishra | Gerbrand Ceder
Source: Physical Review Materials (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine-learning interatomic potentials Mn-rich disordered rocksalt (DRX) cathodes promise high energy density, but their performance depends on a structural evolution during electrochemical cycling. Using a fine-tuned machine learning interatomic potential, the authors performed large-scale molecular dynamics simulations to reveal this transformation. The simulations map the atomic-level transition from a disordered structure to a partially disordered, spinel-like phase. This transformed phase features enhanced Li-ion transport pathways, and the analysis further clarifies the role of Mn valence states and correlates cation ordering with intercalation voltage profiles. Molecular dynamics ; Cathode ; Interatomic potential ; Valence (chemistry) ; Phase (matter) ; Intercalation (chemistry) Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electrochemical Analysis and Applications
Authors: Ahmad Syauqi | Vijay Mohan Nagulapati | Yus Donald Chaniago | Juli Ayu Ningtyas | Riezqa Andika | Hankwon Lim
Source: Sustainable Energy Technologies and Assessments (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advancement in power-to-methanol integration with steel industry waste gas utilization through solid oxide electrolyzer cells: Surrogate model-based approach for optimization Power to gas ; Methanol ; Electrolysis ; Surrogate model ; Process engineering ; Waste management ; Oxide ; Municipal solid waste ; Materials science ; Environmental science ; Computer science ; Engineering ; Metallurgy ; Chemistry Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Molten salt chemistry and electrochemical processes
Authors: Qian Chen | Yikun Cai | Yuqin Zhu | Haodi Ji | Xiaobing Ma | Han Wang
Source: Materials (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

An Integrated Approach Using GA-XGBoost and GMM-RegGAN for Marine Corrosion Prediction Under Small Sample Size Corrosion is the predominant failure mechanism in marine steel, and accurate corrosion prediction is essential for effective maintenance and protection strategies. However, the limited availability of corrosion datasets poses significant challenges to the accuracy and generalization of prediction models. This study introduces a novel integrated model designed for predicting marine corrosion under small sample sizes. The model utilizes dynamic marine environmental factors and material properties as inputs, with the corrosion rate as the output. Initially, a genetic algorithm (GA)-optimized machine learning framework is employed to derive the optimal GA-XGBoost model. To further enhance model performance, a virtual sample generation method combining Gaussian Mixture Model and Regression Generative Adversarial Network (GMM-RegGAN) is proposed. By incorporating these generated virtual samples into the base model, the prediction accuracy is further improved. The proposed framework is validated using corrosion datasets from six types of marine steel. Results demonstrate that GA optimization substantially improves both the performance and stability of the model. Virtual sample generation further enhances predictive performance, with reductions of 14.94% in RMSE, 15.55% in MAE, and 14.04% in MAPE. The results indicate that the proposed method offers a robust and effective framework for corrosion prediction in scenarios with limited sample data. Corrosion ; Genetic algorithm ; Sample (material) ; Generalization ; Sample size determination ; Computer science ; Stability (learning theory) ; Predictive modelling ; Data mining ; Machine learning ; Artificial intelligence ; Materials science ; Statistics ; Mathematics ; Metallurgy Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Non-Destructive Testing Techniques
Authors: Zhengqing Zhang | Rongmei Han | Qi Han | Mengdi Zhao | Min Wang | Yuxiu Sun | Zhihua Qiao
Source: Separation and Purification Technology (article, 2025-05-30)
Field: Chemistry — Inorganic Chemistry

Rational design of hydrophobic MOFs for membrane-based C2H6-selective separation under high humidity condition Membrane ; Separation (statistics) ; Humidity ; Chemical engineering ; Rational design ; Membrane technology ; Materials science ; Chemistry ; Chromatography ; Nanotechnology ; Engineering ; Computer science ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Membrane Separation and Gas Transport
Authors: Yunfan Wang | Pengfei Dang | Yuehui Xian | Yumei Zhou | Xiangdong Ding | Jun Sun | Dezhen Xue
Source: Journal of Material Science and Technology (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Uncertainty-aware multi-objective optimization for high work output and low hysteresis in TiNiCuHfCo shape memory alloys Hysteresis ; Work (physics) ; Shape-memory alloy ; Computer science ; Reliability engineering ; Materials science ; Mechanical engineering ; Engineering ; Artificial intelligence ; Physics ; Condensed matter physics Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Magnetic and transport properties of perovskites and related materials
Authors: Shahla Alizadeh | Souvik Ta | Ajay K. Ray | S. Lakshminarayanan
Source: Industrial & Engineering Chemistry Research (article, 2025-09-26)
Field: Chemistry — Analytical Chemistry

Physics-Informed Neural Network with NSGA II and Levenberg–Marquardt Method for Kinetic Modeling in Heavy Oil Hydrocracking Accurately modeling reaction kinetics in heavy oil hydrocracking is essential for optimizing reactor performance and improving product distribution predictions. This study proposes a novel hybrid framework that integrates a physics-informed neural network (PINN) with nondominated sorting genetic algorithm II (NSGA-II) and Levenberg–Marquardt (LM) optimization method to achieve fast and accurate estimation of kinetic parameters. Unlike conventional approaches, the proposed method combines global and local search: NSGA-II generates high-quality initial parameter estimates, while LM efficiently refines them, ensuring convergence within 300 epochs. This hybrid framework leverages a neural network to model time-evolving behavior, while a Runge–Kutta–based solver enforces reaction kinetics, enabling robust kinetic parameter estimation under physical constraints. Four kinetic models previously proposed in hydrocracking research were implemented as physics constraints and systematically evaluated using the PINN framework. Among them, the most detailed, referred to here as model 4, emerged as the most comprehensive and accurate, capturing all major saturates, aromatics, resins, and asphaltenes (SARA) conversions and byproduct formation (gas and coke). Building upon this, a refined 10-parameter kinetic model was proposed by excluding three low-sensitivity reaction parameters. The simplified model preserved all dominant pathways and demonstrated excellent predictive accuracy across four temperatures (360–400 °C), total error (data + physics MSE on mass fractions) was on the order of 10–3 to 10–2 across training, validation, and testing, with R2 between 0.93 and 0.99. To prevent overfitting and improve generalization, early stopping and a 20% dropout strategy were employed. This study presents a novel application of a hybrid PINN framework that integrates a multiobjective evolutionary algorithm with numerical optimization for kinetic modeling in heavy oil hydrocracking. By embedding physical constraints into the learning process, the framework offers a scalable, interpretable, and accurate approach for estimating reaction parameters and capturing the dynamic behavior of the hydrocracking process. Overfitting ; Sorting ; Solver ; Artificial neural network ; Genetic algorithm ; Convergence (economics) ; Matching (statistics) ; Kinetic energy Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Hydraulic Fracturing and Reservoir Analysis
Authors: Ahmed M. Elgarahy | M.G. Eloffy | Priya Arunkumar | Malak Zirari | Mahmoud M. Ali | Laila A. Al-Khatib | Mashael Daghash Alqahtani | Khalid Z. Elwakeel
Source: International Journal of Biological Macromolecules (review, 2025-10-21)
Field: Materials Science — Polymers and Plastics

Sustainable biopolymer-based materials for oil spill remediation: artificial intelligence integration and technoeconomic insights - A review Sustainability ; Life-cycle assessment ; Oil spill ; Circular economy ; Environmental impact assessment ; Biomimetic materials ; Applications of artificial intelligence Polymer composites and self-healing ; Surface Modification and Superhydrophobicity ; Electrospun Nanofibers in Biomedical Applications