jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Melek Türker Saçan | Gulcin Tugcu | Natalja Fjodorova | Katja Venko | Durbek Usmanov | Bakhtiyor Rasulev | Safiye Sağ Erdem | Marjana Novič
Source: Results in Chemistry (article, 2025-10-01)
Field: Chemistry — Organic Chemistry

In silico study of the solubility of fullerene derivatives in chlorobenzene Since the seminal discovery of fullerene in 1985, fullerene C60 and its derivatives have emerged as versatile nanostructured materials with applications spanning optics, electronics, cosmetics, and biomedicine. The synthesis of various fullerene derivatives (FDs) involves the addition of functional groups to C60. Primary production costs are intricately tied to the extraction and purification of C60, given its limited solubility in both polar and many organic solvents. A critical physicochemical characteristic defining FDs' industrial utility is their solubility, a factor integral to substance distribution in the environment. The application of fullerenes, particularly in renewable energy, has driven extensive Quantitative Structure-Property Relationship (QSPR) research into their performance-related properties. This study assesses FD solubility in chlorobenzene. Solubility in organic solvents is essential, as it is linked to bioaccumulation. We have generated linear and nonlinear machine learning-based QSPR models, validated for their robustness and accuracy. These models are based on experimental solubility data obtained for a set of FDs in chlorobenzene. Subsequently, these models were deployed to predict the solubility of a novel set comprising 117 FDs. The examined dataset encompasses diverse groups attached to the C60- and C70-core structures, with side chains strategically linked to the cyclopropane group. The insights drawn from this study bear significance in understanding how FDs with varying solubility levels can be assessed for distinct applications. By exclusively relying on developed “in silico” models and circumventing extensive testing, these QSPR models offer early evaluative tools, shedding light on FDs with optimal solubility for specific application domains. The outcomes of this research hold promise for advancing our comprehension of fullerene derivatives and streamlining their application in diverse industrial sectors. Prediction of Solubility of Fullerene Derivatives in Chlorobenzene. • Solubility of 27 fullerene derivatives in chlorobenzene was investigated. • Linear and nonlinear ML-QSPR models were developed to predict the solubility of FDs. • The solubility for a new set of 101 FDs was predicted by applying the ML-QSPR models. • Most soluble FDs have side chain linked to C60 core via a cyclopropane ring and contain two benzene rings. Solubility ; Quantitative structure–activity relationship ; Fullerene ; Chlorobenzene ; In silico ; Hildebrand solubility parameter Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Chemical Thermodynamics and Molecular Structure
Authors: Nuanfei Zhu (4370896) | Yixing Tian (490278) | Sinuo Tao (21554935) | Ze Qiao (5877071) | Zhugen Yang (1606936) | Ligang Hu (1654795) | Jingfu Liu (1556383) | Zhen Zhang (86004)
Source: Figshare (article, 2025-06-17)
Field: Chemistry — Spectroscopy

Chromium SpeciationMonitoring Platform for DrinkingWater: Machine Learning-Assisted Dual-Emission Fluorescence SensorArray Different chromium (Cr) speciation in drinking water shows distinct risk levels to humans, failing to reflect real environmental impacts only by total Cr analysis. Integrated with machine learning, a novel fluorescence sensor array was developed for rapid identification and quantitative detection of Cr speciation without sample pretreatment other than filtration. This system prepared three-component fluorescence hybrid materials (MSN@Zr@Au and MSN@Zr@AgAu) with dual emission wavelengths. The sensing unit with a dual-mode algorithm was specific for Cr speciation and accurately identified chromium speciation among 11 coexisting cations. The algorithm of linear discriminant analysis (LDA) assisting hierarchical cluster analysis (HCA) provided higher selectivity for Cr speciation for real samples. Finally, this method showed good analytical performance ranging from 1 to 60 μM, exhibiting a low detection limit of 1.29 μM. This strategy shows excellent practicability for Cr speciation analysis in drinking and tap water, developing a practical monitoring platform for real water. Genetic algorithm ; Chromium ; Detection limit ; Fluorescence ; Analytical Chemistry (journal) ; Sample (material) Molecular Sensors and Ion Detection ; Arsenic contamination and mitigation ; Analytical chemistry methods development
Authors: Ehsan Alibagheri (22331396) | Mohammad Khazaei (2004547) | Bassam Rabihavi (22331399) | Ahmad Ranjbar (14164199) | Thomas D. Kühne (1301100) | S. Mehdi Vaez Allaei (5758829)
Source: Figshare (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

SynthesizableTwo-Dimensional Haeckelites with AuxeticBehavior and Multifunctional Properties: A Computational Study IntegratingMachine Learning Interatomic Potentials Our recent high-throughput investigation has uncovered bulk semiconducting haeckelite structures that hold considerable promise for optoelectronic applications (Advanced Functional Materials, 10.1002/adfm.202402390). In this study, we perform a comprehensive search using evolutionary algorithms alongside first-principles simulations to identify stable two-dimensional haeckelite structures. This process reveals the presence of two-dimensional CdS, CdSe, ZnSe, and ZnTe haeckelites. We analyze the phonon dispersion relations and lattice thermal conductivity by utilizing machine learning interatomic potentials. The resulting nanosheets demonstrate a dynamic stability and exceptional mechanical properties. Additionally, they exhibit low lattice thermal conductivity ranging from 3 to 30 W/mK, with band gaps of 2.1 to 2.8 eV. Notably, the CdS and CdSe haeckelite structures show high electron and hole mobilities along with strong visible light absorption, making them highly suitable for optoelectronic applications. A particularly intriguing discovery is their negative Poisson’s ratio in all directions, indicating auxetic characteristics. Auxetics ; Phonon ; Lattice (music) ; Thermal conductivity ; Thermal stability ; Thermal ; Stability (learning theory) MXene and MAX Phase Materials ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Georgiy Asaturov | С. А. Гуда | Mikhail Lifar | O. V. Kudryakov | Alexander Soldatov | И. В. Колесников
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Intelligent Assistant for Magnetron Sputtering Diamond-Like Carbon Coatings Deposition Sputter deposition ; Flexibility (engineering) ; Coating ; Deposition (geology) ; Sputtering ; Knowledge base ; Carbon fibers Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Nanotechnology research and applications
Authors: Lianhua He | Qichao LIANG | Kaifan Pan | Tianyan Li | Qiang Ma | Xin Wang | Haibo Xu | Yingjin Ma
Source: npj Computational Materials (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

A framework of active data selection and quantum-enhanced regression for predicting magnetic properties of sintered NdFeB magnets Abstract Sintered neodymium-iron-boron (NdFeB) magnets are indispensable in high-performance applications, but their optimization is challenged by complex structure-property relationships and limited data. In this work, we curate the first multi-domain database for this system (1994 industrial and academic samples) and systematically evaluate active learning (AL) strategies on classical and quantum-enhanced regressors. First, our “domain-aware” analysis reveals quantitative differences in design heuristics between industrial and academic data. Second, we present a methodological blueprint for integrating quantum kernel regression into an AL framework using a bootstrapped ensemble for uncertainty quantification. Finally, and most significantly, our results reveal AL effectiveness is strongly model-dependent. Its advantage ranges from significant acceleration (Random Forest, SVR) to being diminished (XGBoost), or even inverted—proving detrimental compared to random sampling—as shown in our quantum-enhanced SVR case study. This finding provides critical new insights for the strategic application of machine learning in materials discovery. Magnet ; Regression ; Heuristics ; Selection (genetic algorithm) ; Active learning (machine learning) ; Kernel (algebra) ; Neodymium magnet ; Linear regression Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Sheng‐Wei Wang | Yi‐Siou Huang | Chih‐Yu Lee | Jimmy H. Ni | Carlos Rı́os
Source:  (article, 2025-08-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable Fano Resonances in Thin-Film Metamaterials We study three different material platforms to modulate the response of Fano-resonant optical coatings (FROCs): Sb<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>Se<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>, VO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, and Indium-Tin Oxide (ITO), whose optical properties can be tuned via phase transitions or charge doping. We develop a theoretical framework to estimate the Fano parameter and, thus, directly design Fano-resonant thin-film stacks with engineered optical response, i.e. to match targeted wavelengths, display transitions between Fano and Fabry-Perot-type responses, among other responses. In addition to theory, we develop a machine-learning-based inverse design method to find optimal stacks. Fano plane ; Fano resonance ; Inverse ; Metamaterial ; Phase (matter) ; Charge (physics) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Yameng Liu | Jing Guo | Zhilei Wang | Yanguo Li | Fan Zhao | Zhihao Zhang | Xinhua Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetostriction Model for Non-Oriented Silicon Steel Based on Physicochemical Parameters of Alloying Elements Magnetostriction ; Silicon ; Metallurgy ; Materials science ; Electrical steel ; Physics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Induction Heating and Inverter Technology
Authors: Carolina Massay | Mauricio Cornejo | Haci Baykara
Source: Materials science forum (article, 2025-11-07)
Field: Materials Science — Polymers and Plastics

Tensile Strength Prediction for Crystalline Nanocellulose-Polyvinyl Alcohol Composites Using Machine Learning Algorithms Machine learning (ML) algorithms can improve and innovate the design of new, eco-friendly composite materials. Therefore, this study aims to forecast tensile strength for polyvinyl alcohol composite reinforced by crystalline nanocellulose (CNC) through ML regression algorithms. Moreover, 107 datapoints from the literature were used to train and test ML models. However, this dataset had missing values for the input variables considered, so an Iterative Imputation with an Extra Tree (ET) Regressor model as estimator was performed, which reached a determination coefficient of 0.88. This study implemented five ML algorithms to predict tensile strength: Adaptive Boosting, Extreme Gradient Boosting, Random Forest, ET, and Gradient Boosting (GB). Additionally, a hyperparameter optimization was carried out using the Random Search optimization technique, obtaining that the GB optimized model had the highest precision with a determination coefficient of 0.97. Moreover, it was determined that PVA hydrolysis degree, CNC percentage, and CNC diameter were the most important variables for the GB-optimized model through SHAP analysis. Ultimate tensile strength ; Gradient boosting ; Composite number ; Nanocellulose ; Hyperparameter ; Random forest ; Coefficient of determination ; Boosting (machine learning) Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Scientific and Engineering Research Topics
Authors: Zhixuan ZHANG | Zongyao ZHANG | Guorui CHANG | Weili WANG | Na LI | Weibin ZHANG
Source: SHILAP Revista de lepidopterología (article, 2025-11-01)
Field: Materials Science — Ceramics and Composites

Spinodal Decomposition in (Ti, Zr)(C, N) Ceramics: Data-Driven Efficient Design and Hardness-Toughness Synergy Traditional transition-metal carbide and nitride ceramics often exhibit a trade-off between hardness and toughness, leading to significantly reduced service life under severe conditions such as wear, corrosion, and high temperature. In this study, a spinodal decomposition-induced phasese paration strategy was employed to simultaneously enhance the hardness and toughness of (Ti, Zr)(C, N) carbonitride ceramics. Guided by thermodynamic calculations, a series of compositional variants of (Ti, Zr)(C, N) ceramics were synthesized, and the effects of aging temperature and duration on the microstructural evolution were systematically investigated. The experimental results demonstrate that spinodal decomposition induces the formation of a nanoscale phase-separated network, which strengthens the material while preserving fracture resistance. Furthermore, machine-learning models were developed to quantitatively correlate composition, microstructural features, and mechanical properties, enabling efficient screening and optimization of carbonitride ceramics. This work not only elucidates the intrinsic mechanisms by which spinodal decomposition enhances ceramic mechanical performance but also provides a data-driven framework for the rational design of high-performance ceramics for extreme environments. Spinodal decomposition ; Spinodal ; Ceramic ; Carbide ; Microstructure ; Work (physics) ; Toughness ; Decomposition Advanced ceramic materials synthesis ; Metal and Thin Film Mechanics ; Boron and Carbon Nanomaterials Research
Authors: Teresa Benešová | Kateřina Pokorná | Andreas Erlebach | Christopher J. Heard
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Mobility and Sintering of Silica-Supported Platinum Clusters Via Reactive Neural Network Potentials Sintering ; Platinum ; Materials science ; Chemical engineering ; Composite material ; Chemistry ; Catalysis ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Carlos Murguiondo | Mario Garcı́a de Lacoba | Valentina Acosta-Borrero | Jorge Barriuso | Alicia Prieto
Source: ACS Omega (article, 2025-11-20)
Field: Materials Science — Biomaterials

Computational Study of a Versatile Lipase for the Degradation of Polylactic Acid Biocatalysis is an emerging and sustainable approach to depolymerize highly hydrophobic plastic polyesters such as poly-(lactic acid) (PLA), a bioplastic widely used in packaging and disposable items. Some enzymes, including lipases, cutinases, and proteases, have been described to hydrolyze PLA, but the activity strongly depends on stereochemistry and crystallinity. In this study, we explored the activity of the versatile lipase from<i>Ophiostoma piceae</i> (OPE) and three engineered variants (N81A, N94A, and N81/94A) on polylactic acid (PLA), comparing the experimental data with predictions from two computational methodologies, Thermal Titration Molecular Dynamics (a classical method) and the machine learning-guided XLPFE scoring function. Experimentally, mutant N81A showed the highest PLA hydrolytic activity, followed by WT OPE, with N94A and N81/94A being substantially less effective. This combined approach served to validate the reliability of these computational strategies for predicting enzyme interactions and highlights the importance of using long enough model substrates to guide future enzyme optimization. Polylactic acid ; Lipase ; Hydrolysis ; Bioplastic ; Degradation (telecommunications) ; Hydrolytic degradation ; Polyester ; Enzyme biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Xiaoxue Han | Shuaibo Zhang | Junping Yin | Shasha Zhang | Muhammad Waqas | Yuye Zhong | Xianwen Ke | Xinghai Liu
Source: Food Control (article, 2025-12-11)
Field: Materials Science — Biomaterials

Highly stable colorimetric hydrogel indicators based on β-cyclodextrin-Assisted Co-pigmentation and machine learning for real-time pork freshness monitoring Absorbance ; Food spoilage ; Carboxymethyl cellulose ; Citric acid ; Levulinic acid ; Cellulose ; Food packaging ; Bacterial cellulose Nanocomposite Films for Food Packaging ; Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety
Authors: Patyukova, Elena | Yin, Junwen | Basak, Susmita | Sanchez, Samuel Pinilla | Elena, Alin | Teobaldi, Gilberto
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Automatic generation of input files with optimised k-point meshes for Quantum Espresso self-consistent field single point total energy calculations Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results. This represents a particularly important problem for high-throughput and agentic workflows, where due to computational cost, any additional convergence studies are preferably to be avoided. So, there is a need for tools and models which are able to predict DFT parameters from basic input information, such as a structure. In this work, we develop a machine learning approach to predict the appropriate k-point sampling in DFT calculations and generate the input files for Quantum Espresso self-consistent field calculations. To achieve this, we first generated a training dataset comprising over 20,000 materials, each with an energy convergence threshold of 1 meV/atom. Several ML models were evaluated for their ability to predict k-points distance, and uncertainty estimation was incorporated to guarantee that, for at least 85-95% of compounds, the predicted k-distance lies within the convergence region. The best-performing models are made publicly available through an open-access web application. Convergence (economics) ; Field (mathematics) ; Single point ; Energy (signal processing) ; Point (geometry) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Gan Zhang | Hongyan Li | Qibing Zhu | Yao Qin | Dandan Zhai | Yu Yang | Zhiqing Yang | Peng Li
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-12-29)
Field: Chemistry — Analytical Chemistry

Deep learning regression model based on data pairing and pseudo-label fusion for NIR predictive modeling in food and pharmaceutical quality analysis Chemometrics ; Pairing ; Deep learning ; Autoencoder ; Pattern recognition (psychology) ; Sample (material) ; Reliability (semiconductor) ; Partial least squares regression ; Cross-validation Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Qiujiang Liang (8162496) | Jun Yang (2084)
Source: Figshare (article, 2025-03-29)
Field: Materials Science — Materials Chemistry

Polarizable WaterModel with Ab Initio Neural NetworkDynamic Charges and Spontaneous Charge Transfer Simulating water accurately has been a challenge due to the complexity of describing polarization and intermolecular charge transfer. Quantum mechanical (QM) electronic structures provide an accurate description of polarization in response to local environments, which is nevertheless too expensive for large water systems. In this study, we have developed a polarizable water model integrating Charge Model 5 atomic charges at the level of the second-order Mo̷ller–Plesset perturbation theory, predicted by an accurate and transferable charge neural network (ChargeNN) model. The spontaneous intermolecular charge transfer has been explicitly accounted for, enabling a precise treatment of hydrogen bonds and out-of-plane polarization. Our ChargeNN water model successfully reproduces various properties of water in gas, liquid, and solid phases. For example, ChargeNN correctly captures the hydrogen-bond stretching peak and bending-libration combination band, which are absent in the spectra using fixed charges, highlighting the significance of accurate polarization and charge transfer. Finally, the molecular dynamical simulations using ChargeNN for liquid water and a large water droplet with a ∼4.5 nm radius reveal that the strong interfacial electric fields are concurrently induced by the partial collapse of the hydrogen-bond network and surface-to-interior charge transfer. Our study paves the way for QM-polarizable force fields, aiming for large-scale molecular simulations with high accuracy. Water model ; Intermolecular force ; Polarizability ; Partial charge ; Charge (physics) ; Polarization (electrochemistry) ; Ab initio ; Quantum ; Polarizable continuum model Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Zedong Peng | Zeju Li | Meng Gao | Qiang Xu | Chen Zhang | Jieru Zhao
Source: ArXiv.org (preprint, 2025-07-04)
Field: Materials Science — Materials Chemistry

ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis High-Level Synthesis (HLS) plays a crucial role in modern hardware design by transforming high-level code into optimized hardware implementations. However, progress in applying machine learning (ML) to HLS optimization has been hindered by a shortage of sufficiently large and diverse datasets. To bridge this gap, we introduce ForgeHLS, a large-scale, open-source dataset explicitly designed for ML-driven HLS research. ForgeHLS comprises over 400k diverse designs generated from 846 kernels covering a broad range of application domains, consuming over 200k CPU hours during dataset construction. Each kernel includes systematically automated pragma insertions (loop unrolling, pipelining, array partitioning), combined with extensive design space exploration using Bayesian optimization. Compared to existing datasets, ForgeHLS significantly enhances scale, diversity, and design coverage. We further define and evaluate representative downstream tasks in Quality of Result (QoR) prediction and automated pragma exploration, clearly demonstrating ForgeHLS utility for developing and improving ML-based HLS optimization methodologies. The dataset and code are public at https://github.com/zedong-peng/ForgeHLS. Kernel (algebra) ; Bayesian optimization ; Code (set theory) ; Source code ; Bridge (graph theory) ; Economic shortage ; Bayesian probability ; Range (aeronautics) Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis