jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yizhen Situ | Yanling Chen | Wenxuan Li | Zhiwei Qiao | Lifeng Ding | Qingyuan Yang
Source: Chemical Engineering Journal (article, 2025-10-05)
Field: Chemistry — Inorganic Chemistry

Efficient and explainable transformer-based language models for property prediction in metal-organic frameworks Scalability ; Physical property ; Property (philosophy) ; Test set ; Adsorption ; Training set ; Applicability domain ; Set (abstract data type) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Zihao Xie (4250887) | Samir I. Gadow (12708056) | Kakhramon Fayzullaev (22231761) | Usmonov Botir (22231764) | Rui Xiao (260235) | Zhicheng Luo (4574686)
Source: Figshare (article, 2025-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Feature EngineeringEnhanced Machine Learning Predictionof Pore Properties in Lignin-Derived Nanoporous Carbon for High-PerformanceSupercapacitor Applications Lignin-derived porous carbon (LDPC), prepared through catalytic pyrolysis, has attracted significant attention for its use in energy storage. However, optimizing its nanopore structure is often hindered by the trial-and-error nature of experiments and the limited quality of the available data. In this work, we propose a hybrid machine learning framework that enhances data quality through feature generation, thereby improving the predictive accuracy of pore-related properties. By systematically transforming and combining existing features, we generated 2025 candidate features and identified 30 high-correlation features that were integrated into the original data set. This enriched feature space significantly improved model performance: the final regressor achieved <i>R</i><sup>2</sup> values of 0.99 for specific surface area (SSA) and total pore volume (TPV) and demonstrated strong generalization capability (<i>R</i><sup>2</sup> > 0.85). Interpretability analysis further confirmed that the final regressor captures meaningful physical mechanisms in the generated feature space. Our approach provides an efficient pathway to enhance data set quality and accelerate the discovery of high-performance nanoporous carbon materials from lignin. By emphasizing nanoscale pore characteristics, this work highlights the potential of LDPC for high-performance supercapacitor applications. Interpretability ; Nanopore ; Feature (linguistics) ; Nanoporous ; Artificial neural network ; Generalization ; Quality (philosophy) ; Supercapacitor Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shufang Li | Tinglun Ao | Kunpeng Gao | Junqi Tang
Source: ChemPhysChem (article, 2025-11-08)
Field: Chemistry — Inorganic Chemistry

A Stacking Ensemble Model for Improving the Prediction Performance of Luminescent Color of Phosphor Determining the luminescent colors of phosphors used in display and lighting applications is a crucial step in discovering new functional luminescent materials. This study collects the experimental conditions for Ca<sub>11</sub>(SiO<sub>4</sub>)<sub>4</sub>(BO<sub>3</sub>)<sub>2</sub> (CSB) phosphor to produce different colors. Through 12 commonly used machine learning models and stacking ensemble model, the luminescence colors of CSB with different ion doping are accurately predicted, and the reliability of the results through experiments is verified. The findings demonstrate that the stacking ensemble model can effectively improve forecasting performance compared to a single optimal model, with overall accuracy, precision, recall score, and f1 score of 98.19%, 98.27%, 98.10%, and 98.18%, respectively. It is the best stacking ensemble model currently known. Compared with the single best classification model, the stacking ensemble model achieves relative improvements of ≈3.55%, 2.99%, 3.46%, and 3.89%, respectively. In addition, the Commission Internationale de L'Eclairage (CIE)-chromaticity diagram of the luminescence color of phosphors is successfully predicted by using a clustering method applied to the output of the stacking model; and experiments further verify the generalization performance of the model. The research results reveal that the stacking ensemble model has high precision and speed in predicting phosphor luminescence colors, and has great potential in optimizing luminescence properties. Stacking ; Phosphor ; Luminescence ; Ensemble learning ; Ensemble forecasting ; Generalization Pigment Synthesis and Properties ; Luminescence Properties of Advanced Materials ; Cultural Heritage Materials Analysis
Authors: Matthew Myers | Praveen Praveen | Simon Watt | Wesley Moss | Joanne Wisdom
Source: Sensing and Bio-Sensing Research (article, 2025-07-26)
Field: Chemistry — Electrochemistry

Toward the development of a square wave voltammetry technique for in situ soil organic carbon quantification Many strategies are being developed and implemented to increase the soil carbon stock; however, demonstrating efficacy is often limited by the time and costs associated with soil sampling/analysis and a lack of real-time data to understand soil carbon spatiotemporal dynamics. As such, the development of real time in-ground sensors for monitoring changes in soil organic carbon over time is necessary to validate (or invalidate) these strategies in the real world. In situ near-IR spectroscopic techniques have been developed for quantifying and characterizing organic carbon in soil. This strategy is strongly affected by soil moisture content, relies on complex statistical and machine learning techniques and is relatively expensive. In this work, we examine the feasibility of developing a low-cost electrochemical technique for the quantification of soluble soil carbon species in soil. Given that these species are very diverse, commercially available fulvic/humic acid is used as a pathway for sensor development. Using a composite film consisting of a poly(methyl methacrylate) resin (to attract fulvic/humic acid to the electrode surface) and a hydrogel material (to bring moisture to the electrode surface), a linear response over a fulvic acid concentration range 0 to 0.05 wt% has been demonstrated. Multi-day measurements in a varying temperature environment have shown that there is a strong correlation between response and temperature. By implementing these correlations and optimizing the coating characteristics, we show that the sensor has a lifetime of over one month in aqueous solution. • Fulvic acid is a good representative of the soluble electrochemically active species in soil. • A squarewave voltammetry based chemical sensor has been developed for fulvic acid. • Coating has been developed for the electrochemical detection of fulvic acid. • A hydrogel is used to improve robustness and accommodate operation in low moisture soils. In situ ; Soil carbon ; Voltammetry ; Environmental chemistry ; Total organic carbon ; Carbon fibers ; Square (algebra) ; Cyclic voltammetry ; Environmental science ; Materials science ; Soil science ; Chemistry ; Soil water ; Electrode ; Mathematics ; Electrochemistry ; Organic chemistry ; Composite material Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis
Authors: Rushik Desai (21090852) | Shubhanshu Agarwal (18855126) | Kiruba Catherine Vincent (13883022) | Alejandro Strachan (1435693) | Rakesh Agrawal (1619722) | Arun Mannodi-Kanakkithodi (1611505)
Source: Figshare (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Exploring the DefectLandscape and Dopability of ChalcogenidePerovskite BaZrS<sub>3</sub> BaZrS<sub>3</sub> is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities, which have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS<sub>3</sub>. In this work, we applied first-principles computations to comprehensively investigate the defect landscape of BaZrS<sub>3</sub>, including all intrinsic defects and a set of selected impurities and dopants. BaZrS<sub>3</sub> intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects which make BaZrS<sub>3</sub> even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS<sub>3</sub> owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS<sub>3</sub> paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys. Dopant ; Chalcogenide ; Perovskite (structure) ; Impurity ; Crystallographic defect ; Work (physics) ; Conductivity Machine Learning in Materials Science ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Xin Li (51274) | Shengqi Wu (22597675) | Shichao Ding (7520015) | Zhao Wang (145195) | Jing He (58446) | Mingxia Sun (129849) | Haitham Saad Al-Mashriqi (22597678) | Selene Tang (22597681) | Jia Chen (8203) | Hongdeng Qiu (1572232)
Source: Figshare (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Machine Learning-AssistedFe–N–C Single-AtomNanozyme Rapid Screening Platform for Acetylcholinesterase Inhibitors Traditional screening methods for acetylcholinesterase inhibitors (AChEIs) encounter significant challenges due to two primary factors: subjective errors in colorimetric analysis and reliance on laboratory instruments. To overcome these limitations, this study introduces a rapid screening platform, which integrates a Fe–N–C single-atom nanozyme (SAzyme) biosensor, an anti-interference portable hydrogel kit, and an intelligent analysis platform. The system capitalizes on the remarkable enzyme-like catalytic properties of SAzyme, which effectively transform changes in AChE activity into visible colorimetric signals. In addition, the encapsulation of SAzyme within an agarose acidic hydrogel, combined with the physical light-blocking features of a 3D printing setup, ensures high stability during detection. Moreover, the system is enhanced by a customized smartphone application equipped with machine learning algorithms. This application identifies color signals, processes data, corrects for interference, and provides quantification of AChE activity via the smartphone interface. In practical application, this platform was successfully utilized to screen 10 alkaloids for AChE inhibitory activity, of which only berberine chloride demonstrated a significant inhibitory effect (IC<sub>50</sub> 4.61 μM). This study developed a three-unit integrated system (enzyme-like catalytic material, hydrogel kit, and AI algorithm) that overcomes traditional limitations through synergistic innovation, enabling a portable, precise, instrument-free AChEIs screening platform for resource-limited regions. Acetylcholinesterase ; Aché ; Agarose ; Inhibitory postsynaptic potential Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Enzyme Catalysis and Immobilization
Authors: Alfonso Pedone (1487686) | Marco Bertani (9199964) | Matilde Benassi (21174565)
Source: Figshare (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Revisiting MachineLearning Potentials for SilicateGlasses: The Missing Role of Dispersion Interactions Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-ACE) model [D.P. Kovács et al., J. Chem. Phys. 159(2023), 044118] for silicate glasses, using sodium silicates as a test case. We compare its performance with a DeePMD-based MLIP specifically trained on sodium silicate compositions [M. Bertani et al., J. Chem. Theory Comput. 20(2024), 1358–1370] and assess their accuracy in reproducing structural and dynamical properties. Additionally, we investigate the role of dispersion interactions by incorporating the D3(BJ) correction in both models. Our results show that while MACE accurately reproduces neutron structure factors, pair distribution functions, and Si[Q<sup>n</sup>] speciation, it performs slightly worst for elastic properties calculations. However, it is suitable for the simulations of sodium silicate glasses. The inclusion of dispersion interactions significantly improves the reproduction of density and elastic properties for both MLIPs, highlighting their critical role in glass modeling. These findings provide insight into the transferability of general MLIPs to disordered systems and emphasize the need for dispersion-aware training data sets in developing accurate force fields for oxide glasses. Sodium silicate ; Dispersion (optics) ; Transferability ; Silicate ; Ab initio ; Density functional theory Machine Learning in Materials Science ; Material Dynamics and Properties ; Glass properties and applications
Authors: Tomoka Tsuya | Shinya Iwata | R. Kitani | Hiroaki Uehara | Tatsuki Okamoto | Tatsuo Takada
Source:  (article, 2025-09-14)
Field: Materials Science — Materials Chemistry

Design of Discharge Delay Time Estimator Based on Integral Equation Representing Partial Discharge The aging of power equipment and device miniaturization have heightened the need for insulation diagnostics. Partial discharge (PD) affects insulation lifespan but varies with material type and defect geometry. A previous study modeled PD occurrence using an integral equation with multiple parameters. This study focuses on discharge delay time. The objective is to estimate the discharge delay time from experimentally obtained PD distributions. To achieve this, a machine learning model is developed based on PD distributions derived from the integral equation. The integral equation was used for the case where a sinusoidal voltage is applied to symmetric electrodes and the discharge delay time varies according to an exponential distribution. Monte Carlo simulations were also performed to generate discharge distributions, which were used to evaluate the accuracy of the regression model. As a result, the Linear support vector machine (SVM) achieved the lowest root mean square error (RMSE) during training (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.76 \times 10^{-5}$</tex>), while validation using Monte Carlo data showed that the Gaussian SVM achieved the smallest RMSE (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1. 0 0} \times \mathbf{1 0}^{-\mathbf{5}}$</tex>). Partial discharge ; Monte Carlo method ; Estimator ; Mean squared error ; Integral equation ; Gaussian ; Support vector machine ; Exponential function High voltage insulation and dielectric phenomena ; Advanced Battery Technologies Research ; Thermal Analysis in Power Transmission
Authors: Jiashu Liang (12519822) | Martin Head-Gordon (588951)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Gold-Standard Chemical Database 137 (GSCDB137): ADiverse Set of Accurate Energy Differences for Assessing and DevelopingDensity Functionals We present GSCDB137, a rigorously curated benchmark library of 137 data sets (8377 entries) covering main-group and transition-metal reaction energies and barrier heights, (intra- and intermolecular) noncovalent interactions, dipole moments, polarizabilities, electric-field response energies, and vibrational frequencies. Legacy data from GMTKN55 and MGCDB84 have been updated to today’s best reference values; redundant or low-quality points were removed, and many new, property-focused sets were added. Testing 29 popular density functional approximations (DFAs) confirms the expected Jacob’s-ladder hierarchy overall but also reveals notable exceptions: functional performance for frequencies and electric-field properties correlates poorly with that for other ground-state energetics. ωB97M-V and ωB97X-V are the most balanced hybrid meta-GGA and hybrid GGA, respectively; B97M-V and revPBE-D4 lead the meta-GGA and GGA classes. Double hybrids lower mean errors by about 30% versus their hybrid analogues but demand careful frozen-core, basis set, and spin contamination treatment. GSCDB137 offers a comprehensive, openly documented platform for rigorous validation of DFA and universal machine learning potentials, and training of the next generation of exchange-correlation functionals. Set (abstract data type) ; Benchmark (surveying) ; Hierarchy ; Data set ; Basis (linear algebra) ; Energy (signal processing) ; Hybrid functional Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Feranmi
V. Olowookere (17144762) | C. Heath Turner (1272159)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Improving Aqueous Metal Salt Interactions Using Machine-LearnedInteratomic Potentials Accurate modeling of aqueous metal salt solutions is essential for understanding processes relevant to environmental safety, energy storage, and separation technologies. Trace metals such as As<sup>3+</sup> at low concentrations pose significant health and environmental risks. They are challenging to simulate due to limitations in both classical force fields (CFFs), which lack accuracy, and <i>ab initio</i> methods, which are restricted to short trajectories. In this study, we develop machine-learned interatomic potentials (MLIPs) to model aqueous AsCl<sub>3</sub> and MgCl<sub>2</sub> using the NequIP/Allegro equivariant graph neural network architecture trained on <i>ab initio</i> molecular dynamics (AIMD) and density functional theory data. Our MLIP models accurately reproduce <i>ab initio</i> energies and forces while capturing solvation structure, ion diffusion, and hydration dynamics more effectively than CFFs (AMBER and UFF models). Our MLIPs achieve energy MAEs < 1 meV/atom and force RMSEs < 40 meV/Å, while providing an O­(10<sup>4</sup>) speedup over AIMD. These MLIPs offer a reliable and efficient alternative for modeling trace metal speciation and transport, with implications for improved separation and environmental processes. Molecular dynamics ; Solvation ; Force field (fiction) ; Aqueous solution ; TRACE (psycholinguistics) ; Potential of mean force ; Metal ; Salt (chemistry) ; Potential energy Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electrocatalysts for Energy Conversion
Authors: Chibueze Ohia | J Ovri | Agha Ndukwe | Chukwudike Onuoha
Source: Acta periodica technologica (article, 2025-01-01)
Field: Materials Science — General Materials Science

Development of high-densitypolyethylene-matrix composites reinforced with silica and kaolin The mechanical properties of high-density polyethylene (HDPE) composites reinforced with silica and kaolin fillers were studied. Waste yogurt containers were cleaned and processed into polymer matrices and reinforced with varying concentrations of kaolin and silica (0 to 30 wt.%). Fillers were sieved through a mesh of 150 micrometer. Composites were produced using two-ball roll mills and compression moulding technique. Fourier Transform Infrared (FTIR) spectroscopy was used for fillers characterization. FTIR revealed presence of diverse functional groups for the fillers. The fillers' various functional groups, such as hydroxyl (O-H), carbon-carbon (C=C, C?C), carbon-nitrogen (C?N), and carbon-chlorine (C-Cl) groups, were identified by FTIR analysis. Uneven filler dispersion was seen in SEM micrographs; compositions including 6 wt.%, 12 wt.%, and 18 wt.% had better mechanical qualities and a more uniform distribution, but compositions containing 24 wt.% and 30% of filler showed notable agglomeration. Scanning Electron Microscopy (SEM) revealed morphology of the composites. Mechanical testing showed maximum tensile strength of 30.93 MPa and elastic modulus of 297.75 MPa with 12 wt.% filler content. Flexural strength peaked at 77.81 MPa for 18 wt.% filler loading. The maximum hardness value of 63.10Hv was recorded for 24 wt.% filler. Elongation at break and Impact strength gradually decreased with increasing filler content. SEM showed unequal distribution of filler within the HDPE matrix. 6 wt.%, 12 wt.% and 18 wt.% fillers reinforcement produced uniformly dispersed composites with improved properties. 24 wt.% and 30 wt.% filler composites showed better hardness values. Multiple Linear Regression was more accurate in predicting the impact strengths of the examined composites in comparison with predictions by the Artificial Neural Network (ANN). At 12 wt.% filler content, tensile strength increased 17.5% to 30.93 MPa, while flexural strength peaked at 77.81 MPa (an 11.7% improvement) at 18 wt.% loading. With an increase in filler content, however, impact strength dropped dramatically from 0.273 to 0.159 J/mm2, suggesting lower toughness. Filler agglomeration at greater concentrations was discovered by SEM analysis, which accounted for the strength decrease. These composites exhibit promise for uses such as roof ceilings and floor tiles, where high flexural strength is preferred above impact resistance. This research developed composites with improved mechanical properties produced using High density polyethylene waste yoghurt cans reinforced with silica and kaolin for possible use as floor tiles and roofing ceilings Composite material ; Matrix (chemical analysis) ; Materials science Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Polymer Science and Applications