jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aydin Larestani | Behnam Amiri-Ramsheh | Saeid Atashrouz | Ali Abedi | Ahmad Mohaddespour | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2025-08-29)
Field: Chemistry — Inorganic Chemistry

Leveraging advanced ensemble learning techniques for methane uptake prediction in metal organic frameworks Energy and environmental policy agencies have been looking for suitable adsorbent materials to promote the use of adsorbed natural gas (ANG). Various candidate adsorbent materials have been developed and tested for methane adsorption. Metal-Organic Frameworks (MOFs) have shown a promising performance in methane adsorption and are of particular interest due to their power in adsorption and separation of gases, chemical tunability, ease of synthesis, and high surface area. Accurate calculation of the theoretical adsorption potential of methane in MOFs and its validation through experiments brings about significant challenges. A growing number of researchers are adopting soft-computing approaches, particularly machine-learning (ML) algorithms, to tackle these challenges. Although ML algorithms have been applied in assessing methane uptake capacity of MOFs, the majority of these efforts have primarily focused on feature selection or the criteria for MOF screening. This communication, however, mainly focuses on the implementation of ensemble-based ML paradigms, including gradient boosting (GBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost) in accurate estimation of methane uptake capacity of experimentally synthesized MOFs based on some readily available features including temperature, pressure, and MOF's pore volume and surface area, for the first time. To this end, a database containing almost 2600 datapoints was attained. The results indicated the high performance of the XGBoost algorithm in estimating the methane uptake capacity of MOFs with a correlation coefficient (R<sup>2</sup>) of 0.9955. Moreover, further analyses revealed that the developed predictive model can reliably estimate the physical trend of CH<sub>4</sub> capacity variations with changing pressure. Also, further analysis indicated the large impact of pressure value on the predicted values. The employed outlier detection technique showed that almost 95% of the collected data points were valid. Methane ; Adsorption ; Gradient boosting ; Metal-organic framework ; Computer science ; Boosting (machine learning) ; Categorical variable ; Ensemble learning ; Feature selection ; Machine learning ; Environmental science ; Materials science ; Artificial intelligence ; Chemistry ; Random forest Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Andrea García-Oseguera | Arizbeth Pérez | Mariel Alfaro-Ponce | Isaac Chairez | E. Reyes
Source: Water (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Kinetic Analysis of pH Effect on the Paracetamol Degradation by an Ozonation–Blast Furnace Slags Coupled System by Neural Network Approximation The presence of paracetamol (PCT) in aquatic environments has raised environmental concerns due to its incomplete removal in conventional wastewater treatment plants. This study evaluates the degradation kinetics of PCT using an ozonation system enhanced with blast furnace slags (BFSs) as a heterogeneous catalyst under acidic (pH 3), neutral (pH 7), and basic (pH 10) conditions. Experimental results show that a simple ozonation process achieves up to 85% PCT removal within 30 min, with the highest rates being observed at pH 10. The addition of BFSs increases the reaction rate constants by 20–30% across all pH levels, attributed to the catalytic activity of metallic oxides in BFSs, which promote radical-based degradation pathways. Biochemical oxygen demand (BOD5) and HPLC analyses confirm a significant reduction in PCT and its byproducts, while ozone consumption is optimized in the catalytic system. A hybrid kinetic modeling approach, integrating pseudo-first-order kinetics and a long short-term memory (LSTM) neural network, was developed and validated, demonstrating superior predictive accuracy (R2 &gt; 0.98) for PCT degradation dynamics compared with traditional models. Degradation (telecommunications) ; Blast furnace ; Kinetic energy ; Ground granulated blast-furnace slag ; Chemistry ; Artificial neural network ; Waste management ; Environmental science ; Chemical engineering ; Engineering ; Organic chemistry ; Computer science ; Physics ; Fly ash ; Electronic engineering Thermal and Kinetic Analysis ; Advanced oxidation water treatment ; Electrochemical Analysis and Applications
Authors: Yu Zhang | Tongle Xu | Luling He | MA Wei-bin | Xiong Li | Minjie Li | Guangrui Xu | Wei Lv | Peng Ding
Source: ACS Applied Materials & Interfaces (article, 2025-09-01)
Field: Materials Science — Polymers and Plastics

High-Performance Polyimide Design: Machine Learning–Accelerated Multiobjective Optimization Polyimides (PIs) with superior transparency, mechanical robustness, and thermal stability are essential for advanced applications such as flexible electronics, foldable displays, and aerospace components, but simultaneous optimization of these properties remains challenging. Here, we present a data-driven multiobjective optimization framework for systematic polyimide design, supported by high-quality data sets extracted with the assistance of large language models. Leveraging robust machine learning models validated by the experimental synthesis and evaluation of five PI films, our method precisely predicts material performance and effectively deciphers complex structure-property relationships influenced by charge transfer complexes. The demonstrated capability of rapidly screening over 20,000 potential PI formulations significantly accelerates material innovation. Delivers PI-a, PI-b, and PI-e films that exceed current commercial benchmarks, achieving glass transition temperature values of 336-376 °C, tensile strengths of 207-324 MPa, and transmittance above 89.6%. This framework establishes a broadly applicable, data-driven approach for designing multifunctional polymers tailored for next-generation flexible electronics and energy storage devices. Materials science ; Polyimide ; Multi-objective optimization ; Process engineering ; Composite material ; Machine learning ; Computer science ; Engineering ; Layer (electronics) Synthesis and properties of polymers ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Azita Etminan | Peter J. Holliman | Peyman Karimi | Mahshid Majd | Ian Mabbett | Mary Larimi | Ciarán Martin | Anna R. L. Carter
Source: Journal of Cleaner Production (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Hybrid pathways for methane production: Merging thermodynamic insights with machine learning A comprehensive study was conducted to simultaneously simulate thermodynamic behavior and predict catalyst performance for CH 4 production via CO and CO 2 methanation, using blast furnace gas (BFG) and basic oxygen furnace gas (BOFG) as feedstocks. Thermodynamic equilibrium simulations based on Gibbs free energy minimization identified optimal reaction conditions at moderate temperatures (150–250 °C) and elevated pressures, achieving over 98 % CO 2 conversion with less than 1 wt% carbon formation. In parallel, machine learning models were developed using an augmented dataset of 2777 experimental observations. Atomic-level structural and electronic descriptors were incorporated into the dataset, including unit cell density and formation energy for active metals, promoters, and supports. Feature selection through Pearson correlation and RFECV identified active phase weight, support density, and reduction conditions as the most influential variables. Among all tested algorithms, XGBoost and CatBoost demonstrated the highest accuracy, with R 2 values exceeding 0.93 for predicting CH 4 yield, selectivity, and CO 2 conversion. SHAP and partial dependence analyses showed that catalyst stability and textural properties govern overall performance. This integrated thermodynamic and machine learning approach defines the operating limits for high-efficiency methanation and provides a data-driven framework for catalyst optimization in industrial applications. Methanation ; Gibbs free energy ; Methane ; Feature selection ; Stability (learning theory) ; Support vector machine ; Extreme learning machine ; Catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Hydrocarbon exploration and reservoir analysis
Authors: Alla P. Toropova | Andrey A. Toropov | В. О. Кудышкин | Danuta Leszczyńska | Jerzy Leszczyński
Source: SAR and QSAR in environmental research (article, 2025-01-02)
Field: Chemistry — Spectroscopy

Application of monomer structures and fragments of local symmetry for simulation of glass transition temperatures of polymers A scheme for constructing models of the 'structure-glass transition temperature of a polymer' is proposed. It involves the representation of the molecular structure of a polymer through the architecture of monomer units represented through a simplified molecular input-line entry system (SMILES) and the fragments of local symmetry (FLS). The statistical quality of such models is quite good: the determination coefficient values for active training set, passive training set, calibration set, and validation set are 0.711, 0.715, 0.859, and 0.884, respectively. The reliability of the approach was assessed for three random distributions of experimental data in the training and validation sets. Machine learning technique was used for a structured training sample distributed in so-called active and passive learning, combined with a calibration set. The optimal descriptors for developed the models were calculated by the Monte Carlo technique. Monomer ; Glass transition ; Polymer ; Chemical physics ; Symmetry (geometry) ; Transition (genetics) ; Local symmetry ; Molecular dynamics ; Materials science ; Polymer science ; Chemistry ; Chemical engineering ; Statistical physics ; Computational chemistry ; Polymer chemistry ; Crystallography ; Organic chemistry ; Physics ; Mathematics ; Geometry ; Engineering ; Quantum mechanics Adsorption, diffusion, and thermodynamic properties of materials ; Material Properties and Applications ; Polymer crystallization and properties
Authors: Youngwoo Choi | Chris Wolverton | Seungbum Hong
Source: APL Machine Learning (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy We present the development of a machine-learning (ML) model for predicting the congruency of compound melts by utilizing a combination of density functional theory-calculated formation energies and a database of experimental melting reactions. Among the various ML models tested, the XGBoost model is found to be the most suitable. Therefore, the XGBoost model is trained with a labeled compound database to determine whether a compound melted congruently. Feature importance from the trained model is extracted and compared to identify descriptors crucial for predicting melting behavior. Notably, the change in slope of the DFT convex hull at the composition of the compound (i.e., the “convex hull sharpness”) exhibited significantly higher importance than the other features. The methodology employed in this study has the potential to enhance the design of alloy processing and can be applied to more complex higher-order alloys. Binary number ; Density functional theory ; Energy (signal processing) ; Materials science ; Artificial intelligence ; Computer science ; Thermodynamics ; Mathematics ; Chemistry ; Physics ; Computational chemistry ; Statistics ; Arithmetic Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Thermal and Kinetic Analysis
Authors: Eileen Tabrizi | Bingyun Li
Source: Materials Today Bio (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense–response–heal platforms Silver has re-emerged as a key material in biomedicine, functioning not only as an antimicrobial coating but also as a programmable nanoscale mediator that links infection control, tissue regeneration, and responsive therapeutics. At nanometer scales, silver's plasmonic, catalytic, and electronic properties differ significantly from those in bulk form. Advances in nanocomposite engineering have transitioned the field from simple antibacterial fillers to multifaceted platforms capable of sensing, signaling, and adapting to complex biological environments. By integrating silver with polymers, carbon allotropes, ceramics, metals, and bioactive scaffolds, researchers have developed silver nanocomposite-based architectures that promote angiogenesis, immunomodulation, electroactive regeneration, and staged therapeutic release. Additionally, the application of machine learning and multi-omics approaches provides predictive insights into how synthesis parameters, protein coronas, and cell responses may influence efficacy and toxicity, thus expediting the development of safe, biocompatible designs of silver nanocomposites. Realizing clinical potential requires reproducible fabrication, scalable manufacturing, and validation of dose parameters, oxidative stress responses, and immune compatibility. Clinically, silver nanocomposites have made significant progress, ranging from FDA-approved wound dressings and implant coatings to investigation platforms for cancer therapy, neural interfaces, and regenerative scaffolds. The future development of advanced silver nanocomposites will not be measured solely by their antimicrobial potency, but by integrating multiple orthogonal functions, including controlled ion release, mechanical reinforcement, electronic conductivity, and biological programming, within architectures verified through quantitative molecular readouts. Situated at the intersection of materials science, bioengineering, and regulatory science, silver nanocomposites offer a flexible platform poised to transform infection management, regenerative medicine, and precision therapeutics. Nanocomposite ; Nanometre ; Coating ; Bioelectronics ; Biocompatible material ; Regenerative medicine ; Antimicrobial ; Carbon nanotube Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Mason Hooten | Het Patel | Yueyue Shao | Rishabh Kumar Singh | Meenakshi Dutt
Source: Journal of Physics Condensed Matter (review, 2025-06-19)
Field: Materials Science — Biomaterials

Applications of enhanced sampling methods to biomolecular self-assembly: a review This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis for replica-exchange molecular dynamics, umbrella sampling, metadynamics, and machine learning based techniques. Applications of select methods towards peptides, proteins, polymers, and nucleic acids are discussed. Finally, a short discussion of the future directions of some of these methods is provided. Sampling (signal processing) ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Telecommunications Supramolecular Self-Assembly in Materials
Authors: Vijay A. Mane | Kartik M. Chavan | Shivaji G. Munde | D. V. Dake | Nita D. Raskar | R.B. Sonpir | Pravin V. Dhole | Ketan P. Gattu | Sandeep B. Somvanshi | Pavan R. Kayande | Jagruti Pawar | B. N. Dole
Source: Journal of Energy Storage (article, 2025-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi2O3 nanocomposites X-ray photoelectron spectroscopy ; Crystallite ; Capacitance ; Specific surface area ; Nanocomposite ; Mesoporous material ; Oxide ; Scanning electron microscope ; Graphene Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Qi Zhou | Minming Jiang | Jiang Xu | Zonghan Xie | Paul Munroe
Source: International Journal of Hydrogen Energy (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Accelerated discovery of hydrogen storage hydride perovskites: A combined machine learning and first-principles approach Hydrogen storage ; Reliability (semiconductor) ; Key (lock) ; Computer data storage ; Hydrogen ; Volume (thermodynamics) ; Perovskite (structure) Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Annalisa Cardellini | Cristina Caruso | Laura Rijns | Patricia Y. W. Dankers | Giovanni M. Pavan | Claudio Perego
Source: Journal of Materials Chemistry B (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Monomer exchange dynamics in ureido–pyrimidinone supramolecular polymers <i>via</i> molecular simulations The use of synthetic supramolecular polymers, built with monomers that self-assemble <i>via</i> non-covalent, reversible interactions, is rapidly growing in many fields, including energy, environmental, and bioengineering applications. Very recently, ureido-pyrimidinone (UPy)-based supramolecular polymers have been used to synthesize biocompatible hydrogels aiming to mimic the dynamic environment of extracellular matrices. Tuning the dynamics, stiffness, and bioactivity of UPy hydrogels effectively influences cellular behaviour and tissue development. However, a complete understanding of UPy-network dynamics over different length and time scales is still lacking, and even the most advanced experimental approaches are unable to capture the dynamics of monomer exchange with atomistic resolution. Here we present a computational study on UPy supramolecular assemblies in water that uncovers the mechanism of monomer exchange between the UPy supramolecular polymers and their surroundings. Our results, based on atomistic molecular dynamics (MD) simulations combined with enhanced sampling and machine-learning (ML) techniques, show that the fine interplay of solute-solvent interactions is the main engine of monomer motion, which makes UPy supramolecular polymer ends more dynamic as compared to the static backbone. This computational work complements the qualitative experimental evidence on supramolecular dynamics with the mechanism of monomer exchange, revealing the most favorable environment for supramolecular polymer damage as well as the underlying principle of self-healing. Supramolecular chemistry ; Monomer ; Supramolecular polymers ; Molecular dynamics ; Polymer ; Self-healing hydrogels ; Supramolecular assembly Synthesis and properties of polymers ; Advanced Polymer Synthesis and Characterization ; Dendrimers and Hyperbranched Polymers
Authors: Zhenan Chen | Haoqi Wu | Handong Gao | Xiaoming Xue | Guangyu Wang
Source: Forests (article, 2025-03-19)
Field: Chemistry — Analytical Chemistry

Monitoring and Discrimination of Salt Stress in Salix matsudana × alba Using Vis/NIR-HSI Technology (1) Background: Salt stress poses a significant challenge to plant productivity, particularly in forestry and agriculture. This research explored the physiological adaptations of Salix matsudana × alba to varying salt stress levels and assessed the utility of hyperspectral imaging (HSI) integrated with machine learning for stress detection; (2) Methods: Physiological metrics, such as photosynthesis, chlorophyll concentration, antioxidant enzyme activity, proline levels, membrane stability, and malondialdehyde (MDA) accumulation, were analyzed under controlled experimental conditions. Spectral data in the visible (Vis) and near-infrared (NIR) ranges were acquired, with preprocessing techniques enhancing data precision. The study established quantitative detection models for physiological indicators and developed a salt stress monitoring model; (3) Results: Photosynthetic efficiency and chlorophyll synthesis while elevating oxidative damage indicators, including enzyme activity, proline content, and membrane permeability. Strong correlations between spectral signatures and physiological changes highlighted HSI’s effectiveness for early stress detection. Among the machine learning models, the Convolutional Neural Network (CNN) trained on Vis+NIR data with standard normal variate (SNV) preprocessing achieved 100% classification accuracy; (4) Conclusions: The results demonstrated that HSI, coupled with modeling techniques, is a powerful non-invasive tool for real-time monitoring of salt stress, providing valuable insights for early intervention and contributing to sustainable agricultural and forestry practices. Stress (linguistics) ; Salt (chemistry) ; Horticulture ; Botany ; Biology ; Chemistry Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Leaf Properties and Growth Measurement
Authors: Lifei Wang | Yucheng Gu | Xiaoqing Tian | Jun Wang | Jia Yan | Junjie Xu | Zhen Zhang | Shiying Liu | Shuo Liu
Source: Photonics (article, 2025-05-23)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing Superhydrophobic surfaces have garnered significant attention due to their pivotal roles in various fields. Femtosecond laser technology provides a feasible means for inducing superhydrophobic microstructures on material surfaces. However, due to the unclear influence mechanisms of process parameters, as well as the high cost and time-consuming nature of experiments, identifying the optimal femtosecond laser processing parameters within the process space remains a significant challenge. To address this issue, a process optimization framework that couples machine learning and genetic algorithms was proposed and successfully applied to the optimization of femtosecond laser-induced groove structures on TC4 alloy surfaces. Firstly, based on 64 sets of experimental data, the effects of the power, scanning speed, and scanning interval on the micro-groove structures and their wetting properties were discussed in detail. Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. Three additional dimensional variables, i.e., the number of effective pulses, energy deposition rate, and roughness, were also added to the original dataset vectors as extra dimensions to participate in and guide the model training process. The prediction model was further coupled into a genetic algorithm to achieve the quantitative design of femtosecond laser processing. Compared to the best hydrophobicity in the original dataset, the contact angle of the designed process was improved by 5.5%. The proposed method provides an ideal solution for accurately predicting wetting properties and identifying optimal processes, thereby accelerating the development and application of femtosecond laser-induced superhydrophobic microstructures. Femtosecond ; Materials science ; Microstructure ; Laser ; Materials processing ; Optics ; Computer science ; Optoelectronics ; Composite material ; Process engineering ; Physics Surface Modification and Superhydrophobicity ; Laser Material Processing Techniques ; Fluid Dynamics and Heat Transfer