jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bader Huwaimel | Saad Alqarni
Source: Scientific Reports (article, 2025-08-13)
Field: Chemistry — Inorganic Chemistry

Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability Metal organic frameworks (MOFs) have indicated great capacity and applications in drug delivery owing to their porous structures. Analysis of their drug loading capacity as well as cytotoxicity was carried out in this study via machine learning. The study employs a stacking regression approach to predict two critical outputs: Cell Viability (%) and Drug Loading Capacity (g/g) in MOFs. The proposed framework combines base models, including Multilayer Perceptron (MLP), Random Forest (RF), and Quantile Regression (QR), with a meta-model for enhanced accuracy and robustness. Principal Component Analysis (PCA) was applied to reduce dimensionality, and the Water Cycle Algorithm was used to optimize hyperparameters. Evaluation metrics, including R<sup>2</sup>, Root Mean Squared Error (RMSE), and maximum error, indicated that the QR-MLP model outperformed the other models, achieving test R<sup>2</sup> scores of 0.99917 for Drug Loading Capacity and 0.99111 for Cell Viability. These findings provide a new perspective of their chemical and biological uses since they show the efficiency of stacking ensemble approaches in handling challenging datasets and optimizing MOF-based drug delivery systems. Future work will focus on enhancing biocompatibility and optimizing drug release profiles to further improve the clinical applicability of MOF-based delivery systems. Computer science ; Principal component analysis ; Machine learning ; Random forest ; Mean squared error ; Artificial intelligence ; Data mining ; Mathematics ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Nanoparticle-Based Drug Delivery
Authors: Emad Al Ibrahim | Nathan Morgan | Simon Müller | Saikiran Motati | William H. Green
Source: Journal of the American Chemical Society (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Accurately Predicting Solubility Curves via a Thermodynamic Cycle, Machine Learning, and Solvent Ensembles Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essential for drug formulation, synthesis, purification, and crystallization. Hard-to-estimate solubility limits currently hinder the design of new processes, making innovation more expensive. We propose a fast and general method for predicting the solubilities of neutral organic molecules in a wide range of solvents and temperatures. Our method uses a thermodynamic fusion cycle to combine machine learning predictions of the activity coefficient, fusion enthalpy, and melting point temperature. This method was tested on a combined data set with more than 100,000 experimental solubility values, showing better or comparable performance to competing methods on many solubility benchmarks even at elevated temperatures. We also introduce reference ensembling to leverage all available experimental solubilities for a given solute in estimating its solubility in a different solvent. Reference ensembling is also shown to enhance the robustness of models trained directly on solubility data. Solubility ; Fusion ; Leverage (statistics) ; Solvent ; Melting point ; Molecule ; Hildebrand solubility parameter ; COSMO-RS Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: M. Alzate Banguero | Sayan Basak | Nicolas Raymond | Forrest Simmons | Pavel Salev | Iván K. Schuller | Lionel Aigouy | E. W. Carlson | A. Zimmers
Source: Condensed Matter (article, 2025-02-13)
Field: Materials Science — Polymers and Plastics

Optical Mapping and On-Demand Selection of Local Hysteresis Properties in VO2 Quantum materials have tremendous potential for disruptive applications. However, scaling devices down has been challenging due to electronic inhomogeneities in many of these materials. Understanding and controlling these electronic patterns on a local scale has thus become crucial to further new applications. To address this issue, we have developed a new optical microscopy method that allows for the precise quasi-continuous filming of the insulator-to-metal transition in VO­2 with fine temperature steps. This enables us to track metal and insulator domains over thousands of images and quantify, for the first time, the local hysteresis properties of VO­2 thin films. The analysis of the maps has allowed us to quantify cycle-to-cycle reproducibility of the local transitions and reveals a positive correlation between the local insulator–metal transition temperatures T­c and the local hysteresis widths ΔTc. These maps also enable the optical selection of regions of high or low transition temperature in combination with large or nearly absent local hysteresis. These maps pave the way to understand and use stochasticity to advantage in these materials by picking on-demand transition properties, allowing the scaling down of devices such as optical switches, infrared microbolometers and spiking neural networks. Hysteresis ; Selection (genetic algorithm) ; On demand ; Computer science ; Artificial intelligence ; Physics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; CCD and CMOS Imaging Sensors
Authors: Balaji Mohan | Fatema Alsaleh | Abdullah S. AlRamadan | Saud Almbdal | Alya Al-Ammari | Zainab Saihati | Alexander K. Voice | Jihad Badra
Source: Fuel (article, 2025-05-15)
Field: Chemistry — Analytical Chemistry

In silico estimation of density, vapor pressure, distillation curve, and octane numbers of gasoline fuels using machine learning Gasoline ; Distillation ; Octane rating ; Octane ; Vapor pressure ; Process engineering ; Environmental science ; Computer science ; Chemistry ; Chromatography ; Organic chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Qingfeng Tang | Zhengye Feng | Yanhui Wang | Xiaoyan Wei | Duoqiang Pan | Wangsuo Wu | Zhen Xu
Source: Journal of Hazardous Materials (article, 2025-08-05)
Field: Chemistry — Inorganic Chemistry

Bacillus subtilis morphology and bentonite colloids govern Eu(III) transport in quartz sand: Mechanisms and machine learning insights Bentonite ; Bacillus subtilis ; Colloid ; Quartz ; Chemical engineering ; Morphology (biology) ; Chemistry ; Environmental science ; Materials science ; Geology ; Engineering ; Bacteria ; Metallurgy Radioactive element chemistry and processing ; Groundwater flow and contamination studies ; Geophysical and Geoelectrical Methods
Authors: Ziyuan Liu | Haoyuan Ding | Sijia Zhao | Hongzhen Wang | Yiqing Xu
Source: Plants (article, 2025-10-11)
Field: Chemistry — Analytical Chemistry

Exploring the Hyperspectral Response of Quercetin in Anoectochilus roxburghii (Wall.) Lindl. Using Standard Fingerprints and Band-Specific Feature Analysis Quercetin, a key flavonoid in <i>Anoectochilus roxburghii</i> (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in <i>A. roxburghii</i> leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths-particularly the 923 nm band-showed strong theoretical and statistical relevance to quercetin's molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine. Hyperspectral imaging ; Quercetin ; Pattern recognition (psychology) ; Flavonoid ; Convolutional neural network ; Visualization Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Biological and pharmacological studies of plants
Authors: Federica Bragone | Kateryna Morozovska | Tomas Rosén | Tor Laneryd | Daniel Söderberg | Stefano Markidis
Source: Journal of Computational Science (article, 2025-02-01)
Field: Materials Science — Biomaterials

Automatic learning analysis of flow-induced birefringence in cellulose nanofibrils Cellulose Nanofibrils (CNFs), highly present in nature, can be used as building blocks for future sustainable materials, including strong and stiff filaments. A rheo-optical flow-stop technique is used to conduct experiments to characterize the CNFs by studying Brownian dynamics through the CNFs’ birefringence decay after stop. As the experiments produce large quantities of data, we reduce their dimensionality using Principal Component Analysis (PCA) and exploit the possibility of visualizing the reduced data in two ways. First, we plot the principal components (PCs) as time series, and by training LSTM networks assigned for each PC time series with the data before the flow stop, we predict the behavior after the flow stop (Bragone et al., 2024). Second, we plot the first PCs against each other to create clusters that give information about the different CNF materials and concentrations. Our approach aims at classifying the CNF materials to varying concentrations by applying unsupervised machine learning algorithms, such as k -means and Gaussian Mixture Models (GMMs). Finally, we analyze the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) of the first principal component, detecting seasonality in lower concentrations. • CNFs are building blocks for future sustainable materials. • PCA exploits information for different cellulose materials and concentrations. • LSTM accurately predicts CNFs’ Brownian dynamics after the flow stops. • Unsupervised learning classifies CNF materials at different concentrations. • Autocorrelation functions are used to discover hidden patterns for CNFs. Cellulose ; Birefringence ; Flow (mathematics) ; Materials science ; Flow birefringence ; Computer science ; Composite material ; Chemistry ; Polymer science ; Mathematics ; Optics ; Organic chemistry ; Physics ; Polymer Advanced Cellulose Research Studies ; Liquid Crystal Research Advancements ; Characterization and Applications of Magnetic Nanoparticles
Authors: Xiaohan Ma | Shiqing Deng | He Qi | Jun Chen
Source: Physical review. B./Physical review. B (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Machine learning accelerates composition design of lead-free high performance piezoceramics based on potassium sodium niobate Potassium sodium niobate (KNN) based lead-free piezoelectric ceramics have garnered significant attention as a new generation of environmentally friendly materials for energy storage and conversion. However, achieving the critical coexistence of different phases at room temperature for superior piezoelectric response typically requires complex multicomponent adjustments through a lengthy process of trial and error. The intricate elemental composition poses tremendous challenges for experimental design and high-throughput calculation. In this study, we adopt a stepwise machine learning (ML) method to develop models for KNN-based solid solutions with multiple element joint doping. On the one hand, a critical composition with multiple phase coexistence at room temperature can be obtained on the forecasted phase diagrams calibrated by the shift of polymorphic phase transition temperatures, experimentally, which exhibits rhombohedral-orthorhombic-tetragonal slushlike polar nanoregions on the atomic polarization mapping. On the other hand, optimal experimental piezoelectric properties for the critical composition further confirm the accuracy of the predicted piezoresponse in the ML model. This work offers a perspective for lead-free piezoelectric materials design that differs from the traditional artificial trial and error method and may provide insights for other materials fields. Potassium ; Lead (geology) ; Sodium ; Potassium niobate ; Materials science ; Composition (language) ; Metallurgy ; Optoelectronics ; Geology ; Ferroelectricity ; Art Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Microwave Dielectric Ceramics Synthesis
Authors: Hajime Kimizuka | Shigenobu Ogata | Bo Thomsen | Motoyuki Shiga
Source: Journal of Physics Condensed Matter (review, 2025-03-13)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> path-integral simulations of hydrogen-isotope diffusion in face-centred cubic metals Lighter isotopes typically diffuse faster than heavier isotopes; however, the case is not necessarily true for H. Predicting the kinetics of H isotope transport and reactions in substances remains a fundamental challenge in material and condensed matter physics. The peculiar experimentally observed isotope effect on H diffusivities in face-centred cubic (fcc) metals has long been an unresolved problem. Using an<i>ab initio</i>path-integral approach to explore the quantum mechanical nature of both electrons and nuclei, this study successfully predicts H isotope diffusivities in fcc Pd over a wide temperature range. The temperature dependence of the diffusivities follows an unusual 'reversed-S' shape on Arrhenius plots. This irregular behaviour, arising from the competition between different nuclear quantum effects (NQEs) with different temperature dependencies, reveals the mechanism of anomalous crossovers between normal and reversed isotope effects. The results illustrate that this phenomenon is common in other fcc metals (e.g. Cu and Ag), where H atoms prefer to occupy octahedral (O) sites. Conversely, in Al, where H atoms prefer to occupy tetrahedral (T) sites, the dependence of H diffusivities on temperature exhibits a familiar 'C' shape. A lattice expansion of approximately 1%-2% causes the stable position of H atoms dissolved in Pd to shift from the O to T sites, and H diffusion in expanded Pd is no longer suppressed by NQEs, as observed in Al. This finding has important implications for interpreting kinetic processes involving the crossover from classical to quantum behaviour of H atoms moving between different interstitial sites. Path-integral simulation results describing the approximate quantum dynamics of the Pd-H system, using a machine-learning-based interatomic potential with accuracy similar to the density functional theory calculations, are presented. This computational approach paves the way for elucidating the quantum behaviour of H isotopes in various materials. Kinetic isotope effect ; Chemistry ; Isotope ; Diffusion ; Ab initio ; Hydrogen ; Arrhenius equation ; Kinetic energy ; Path integral formulation ; Quantum ; Chemical physics ; Thermodynamics ; Atomic physics ; Physical chemistry ; Physics ; Deuterium ; Quantum mechanics ; Activation energy Hydrogen Storage and Materials ; Advanced Chemical Physics Studies ; Nuclear Physics and Applications
Authors: Raouf Hassan | Alireza Baghban
Source: Materials Today Communications (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

Beyond the surface: Quasi-SMILES machine learning approaches for precise estimation of organic sorption This study develops advanced machine learning models to predict soil sorption of organic compounds (logKd), a critical factor influencing their environmental fate. We aim to create robust models that capture complex interactions between soil properties and chemical characteristics for accurate sorption predictions across diverse soil environments. Utilizing a comprehensive dataset of 20,945 experimental records, covering 419 organic compounds and 1,037 soil types, we applied a range of algorithms including XGBoost, LightGBM, Random Forest, SVMs, linear regression, and others. Input features included equilibrium concentration (log Ce), soil-to-solution ratio (log SS ratio), soil organic content (SOC%), cation exchange capacity (CEC), pH, pKa, pKb, and Kd/Kf. Model performance was rigorously assessed using R-squared, MSE, and visualization tools, with data reliability ensured through Monte Carlo outlier detection. XGBoost, LightGBM, and Random Forest achieved superior performance, with R-squared values up to 0.9957 and MSE as low as 0.0067 on test data, significantly outperforming other methods. SHAP analysis revealed Kd/Kf as the dominant predictor, followed by log Ce and log SS ratio, highlighting their critical roles in sorption processes. These findings demonstrate the power of machine learning in delivering precise, reliable predictions for soil sorption, offering valuable insights for environmental risk assessment and pollutant management. Unlike previous studies that typically rely on smaller datasets or linear regression approaches, our work integrates one of the largest curated soil sorption datasets to date with advanced ensemble learning and interpretable AI tools. This combination enables not only highly accurate predictions but also mechanistic insights into nonlinear soil–compound interactions, marking a significant advancement over earlier modeling efforts. • Monte Carlo algorithm is used to find outliers. • 12 different machine learning models are applied soil sorption of organic compounds • SHAP analysis identified Kd/Kf as the most influential factor on soil sorption Sorption ; Random forest ; Outlier ; Reliability (semiconductor) ; Monte Carlo method ; Linear regression ; Range (aeronautics) ; Nonlinear system ; Predictive modelling Machine Learning in Materials Science
Authors: Yahong Pu | Jie Zhang | Peixuan Li | Guolin Wan | Jinbo Pan | Yanfang Zhang | Shixuan Du
Source: The Journal of Physical Chemistry Letters (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Unveiling the Mechanism of Negative Poisson’s Ratio in Phosphorus-like 2D MX Materials Driven by Geometric and Electronic Structures Two-dimensional black-phosphorus-like materials with a re-entrant structure have been reported exhibiting positive or negative Poisson's ratio (NPR). However, uncovering the underlying geometric-electronic interplay and identifying design principles for NPR materials remain challenging. Using first-principles calculations, we investigate 26 two-dimensional black-phosphorus-like MX monolayers with 10 valence electrons (M = cation, X = anion). Among them, PN, AsN, SbN, AsP, and GeSe exhibit out-of-plane NPR. Geometric structure analysis using machine learning links NPR to the variation of the X-M-X bond angle (θ<sub>M</sub>) and M-X-X-X dihedral angle (φ). Under zigzag (<i>y</i>)-direction strain, a larger reduction in θ<sub>M</sub> and a greater increase in φ are more favorable for NPR formation. Electronic structure analysis attributes the out-of-plane NPR under <i>y</i>-axis strain to the cation-anion <i>p</i><sub><i>z</i></sub> orbital interaction. For NPR materials with the same X element, a higher atomic number of M corresponds to a more negative Poisson's ratio. This work advances the development of novel materials with unique mechanical behaviors. Poisson distribution ; Mechanism (biology) ; Phosphorus ; Geometry ; Nanotechnology ; Materials science ; Chemical physics ; Mathematics ; Chemistry ; Physics ; Statistics ; Quantum mechanics ; Metallurgy Graphene research and applications ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Abdulrazak Jinadu Otaru | Zaid Abdulhamid Alhulaybi Albin Zaid
Source: Scientific Reports (article, 2025-10-29)
Field: Materials Science — Polymers and Plastics

Analysis of TGA data for polyvinyl alcohol at slow heating rate using deep neural networks, activation energy, and activation enthalpy This research aims to enhance the deep neural network (DNN) model previously developed by this group, as referenced in<sup>10</sup>, by integrating degradation time into its feature set. It specifically addresses the thermal decomposition of polyvinyl alcohol (PVA) at low heating rates of 2, 5, and 10 °C.min<sup>-1</sup>. In addition, the study presents a thermo-kinetic analysis of the data, facilitating the estimation of activation energy and activation enthalpy. The inputs to the DNN frameworks include degradation time, degradation temperature, and heating rate. Modifications were made to the DNN model to tackle overfitting and reduce the discrepancy between output signals and experimental scatter. This was accomplished through iterative adjustments to the learning rate, implementation of data augmentation techniques, prolongation of the training duration, and early termination to minimize error. An optimized DNN architecture, comprising two hidden layers and eight neurons, effectively facilitated the learning algorithms and successfully trained arbitrary constants. This resulted in output signals that closely aligned with the experimental data ([Formula: see text]), thereby providing a ranking of parameter sensitivity characterized by heating rate, time, and degradation temperature. The Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) model-free equations were used to estimate the activation energy ([Formula: see text]) of the thermogravimetric (TGA) data curves. The estimated average [Formula: see text] values derived from the FWO and KAS model-free equations were 64.6±3.2 kJ·mol⁻¹ and 58.8±2.9 kJ·mol⁻¹, respectively, based on conversion rates between 5 and 50 wt% (i.e., where [Formula: see text]). The estimated theoretical value of the activation enthalpy (ΔH) required for the formation of the activation complex, at these higher correlations, was determined to be positive (25.4- 102.0 kJ·mol⁻¹), indicating that the reaction is invariably endothermic and is consistent with established information in the literature. This approach could prove pivotal for manufacturers in the design and fabrication of polyvinyl alcohol (PVA) and composites with enhanced and novel properties. Overfitting ; Activation energy ; Thermogravimetric analysis ; Polyvinyl alcohol ; Enthalpy ; Artificial neural network ; Degradation (telecommunications) ; Decomposition ; Ranking (information retrieval) Polymer Science and PVC ; Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes
Authors: Wenlong Li | Yuwan Du | Yuan Liu | Chuanfang Zhao | Xincun Dou
Source: Sensors and Actuators B Chemical (article, 2025-05-21)
Field: Chemistry — Analytical Chemistry

Differentiation and identification of CA series synthetic cannabinoids based on SERS technology coupled with support vector machine Support vector machine ; Identification (biology) ; Series (stratigraphy) ; Synthetic cannabinoids ; Chemistry ; Combinatorial chemistry ; Computer science ; Artificial intelligence ; Cannabinoid ; Biochemistry ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research