jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zheng Fang | Jingxuan Xu | Shiliang Song | Mingke Lu | Hui Yan | Shuangshuang Li | Siyuan Chen
Source:  (article, 2025-01-10)
Field: Chemistry — Analytical Chemistry

Identification method for prohibited drugs based on x-ray absorption spectroscopy and machine learning This study explores a new method for identifying prohibited drugs using X-ray Absorption Spectroscopy (XAS) detection and machine learning algorithms. A laboratory-developed scientific equipment was employed to obtain x-ray absorption spectra of ten different chemical substances, including various isomers of prohibited drugs. Principal Component Analysis (PCA) was then applied to extract the spectral features, minimizing data redundancy. Subsequently, the Extreme Learning Machine (ELM) combined with the Sparrow Search Algorithm (SSA) was utilized for analysis. The results demonstrate that this method can accurately identify prohibited drugs, even excelling in automatically identifying isomers. This research offers a novel and promising technique for quick non-destructive drug detection. Identification (biology) ; Absorption (acoustics) ; Spectroscopy ; Computer science ; Absorption spectroscopy ; Materials science ; Artificial intelligence ; Optics ; Physics ; Astronomy ; Composite material Analytical Methods in Pharmaceuticals
Authors: Kumar, Pranav | Körmann, Fritz | Edalati, Kaveh | Grabowski, Blazej | Ikeda, Yuji
Source: Open MIND (preprint, 2025-11-22)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion in TiCr$_2$H$_x$ Laves phases: A combined ab initio and machine-learning-potential study The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ (0 &lt; $x$ &lt;= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent paths between the first-nearest-neighbor face-sharing interstitial sites. The hydrogen migration barriers are substantially higher for the paths that require breaking a Ti-H bond than for those that require breaking a Cr-H bond. Molecular dynamics (MD) simulations with the MLIPs also demonstrate that hydrogen migration occurs more frequently within the hexagonal rings made of the A$_2$B$_2$ interstitial paths, each requiring the breaking of Cr-H bonds, than along the inter-ring paths. The diffusion coefficients of hydrogen obtained from the MD simulations reveal a non-monotonic dependence on hydrogen concentration, which is more pronounced at lower temperatures. Time-averaged radial distribution functions of hydrogen further show that hydrogen avoids face-sharing positions during diffusion and that the hydrogen occupancy at the second-nearest-neighbor edge-sharing positions increases with increasing hydrogen concentration. The diffusion coefficients of hydrogen within 400-1000 K follow an Arrhenius relationship, with activation barriers consistent with most experimental values. One-order of magnitude overestimation of diffusion coefficients compared with some experiments suggests a substantial impact of hydrogen trapping by defects such as Cr vacancies and Ti anti-sites in non-stoichiometric TiCr$_2$ in experiments. Hydrogen ; Diffusion ; Ab initio ; Interstitial defect ; Density functional theory ; Arrhenius equation ; Hydrogen atom ; Spin isomers of hydrogen Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Lin, Peijia | Chen, Pin | Jiao, Rui | Mo, Qing | Cen, Jianhuan | Huang, Wenbing | Liu, Yang | Huang, Dan | Lu, Yutong
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Equivariant Diffusion for Crystal Structure Prediction In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Equivariant map ; Crystal structure prediction ; Translation (biology) ; Lattice (music) ; Convergence (economics) ; Generative grammar ; Inference ; Diffusion Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Huei Ruey Ong | Yifei Shi | Chi Shein Hong | Md Maksudur Rahman Khan | Wan Mohd Eqhwan Iskandar
Source: Journal of Thermoplastic Composite Materials (article, 2025-10-25)
Field: Materials Science — Polymers and Plastics

Effect of biomass-derived carbon nanosphere on mechanical properties of polypropylene/empty fruit bunch fibre composites Natural fibre-reinforced polymer composites have gained significant attention as sustainable alternatives to conventional synthetic materials. This study investigates the mechanical properties enhancement of polypropylene (PP) composites through hybrid reinforcement using oil palm empty fruit bunch (EFB) fibres and carbon nanospheres (CNS) derived from sago bark waste. The CNS was synthesized via pyrolysis at 500°C under nitrogen atmosphere and characterized using TEM, XRD and FTIR which reveals spherical nanoparticles with diameters ranging from 15 to 50 nm and partially crystalline structure. PP composites were fabricated with varying EFB fibre content (0–50 wt%) and CNS loading (0–1.5 wt%) using twin-screw extrusion followed by injection molding. FTIR analysis confirmed successful interfacial interactions between PP matrix, EFB fibres, and CNS through peak shifts and intensity modifications. Thermal analysis demonstrated improved thermal stability with 20°C higher degradation onset temperature for the nanocomposite. Mechanical testing revealed that the optimal composition of 20 wt% EFB fibre with 1 wt% CNS achieved remarkable property enhancements compared to neat PP/EFB composite in which the tensile strength increased by 17%, flexural strength improved by 20% and impact strength enhanced by 32%. An artificial neural network (ANN) model was developed to predict mechanical properties and achieve excellent correlation (R = 0.985) between predicted and experimental values, enabling efficient optimization of composite formulations. This research demonstrates a viable approach for converting agricultural waste into high-performance composites with enhanced mechanical properties suitable for automotive components and consumer product applications. Ultimate tensile strength ; Flexural strength ; Composite number ; Thermal stability ; Fourier transform infrared spectroscopy ; Polypropylene ; Pyrolysis ; Extrusion ; Izod impact strength test Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies
Authors: Zhe Sun | Xiujun Wang | Kun Xie | Weijun Shen
Source:  (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Investigation of Seepage Behavior and Spatial Distribution of Gel Systems in Micro-Porous Media Abstract An innovative hydrogel system (HBS) utilizing the principle of dynamic covalent bonds, was designed for deep conformance control. It has low-molecular-weight reactive precursors bearing diffusible ketone and acyl hydrazide groups. These precursors undergo in-situ crosslinking via dynamic hydrazone bonds within the pore spaces of the formation, effectively guiding the subsequent injected fluids toward unswept oil zones. The HBS system was investigated through experimental evaluation, numerical simulation, and artificial intelligence methods. A reaction index (RI) and its variation map were established to explore the dynamic chain growth behavior and resistance enhancement characteristics of the HBS system under various injection parameters. A dynamic chain growth experiment using a 9-meter-long core was conducted to evaluate the gelation efficiency of the HBS system. Results show that for HBS, as the chain growth reaction proceeds, the probability of interconnection between different molecular aggregates increases, leading to a corresponding enlargement of aggregate size, which rises its viscosity. Prolonged chain growth time enhances the retention and adsorption of the HBS system within porous media, resulting in higher resistance factors and residual resistance factors, thereby demonstrating distinct seepage characteristics. During migration through the long core, the HBS system exhibits excellent transport performance, effectively redirecting water flow and expanding the swept volume. Numerical simulations and machine learning analyses indicate that RI of the HBS system increases with higher concentration and injection rates. This dynamic chain growth not only enhances flow resistance but also enables deep profile control and further expansion of the swept volume. This research synergistically combines static experimental analyses, dynamic core flooding assessments, and artificial intelligence methodologies to comprehensively investigate the evolving chain growth dynamics and seepage behavior of the HBS system within porous formations. The integrated approach provides important theoretical support and methodological guidance for advancing enhanced oil recovery (EOR) technologies. Porous medium ; Spatial distribution ; Materials science ; Porosity ; Geology ; Computer science ; Composite material ; Remote sensing Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications
Authors: Jie Liu | Lin Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Insight into Packing Structures as Well as Electrical States for Ti Nanoparticles on Heating Through Integrating Machine Learning Potential with a Md/Qm Hybrid Strategy Nanoparticle ; Nanotechnology ; Materials science ; Engineering physics ; Computer science ; Engineering Machine Learning in Materials Science ; Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Lucas Miranda Santos | Matthews Silva Martins | Victor Fernando | Thafanys Sousa Travezani | Valério Garrone Baraúna
Source: Brazilian journal of food technology/Brazilian Journal of Food Technology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning with consensus voting and vibrational spectroscopy to identify milk spoilage Abstract Fourier-transform infrared (FTIR) spectroscopy is a robust technique that is sensitive to molecular structural information, obtained from vibrational characteristics that serve as molecular fingerprints. The dairy industry continually requires efficient, non-destructive methods to monitor milk quality and detect spoilage over time. This study employed Attenuated Total Reflectance (ATR), specifically ATR-FTIR spectroscopy and machine learning (Partial Least Squares Discriminant Analysis), to verify the effectiveness of a consensus method versus a traditional sample classification method in identifying milk spoilage. In this study, a 48-hour experiment was conducted, extracting pasteurized milk samples at regular intervals to create a predictive model capable of detecting spoilage. To further ensure the reliability of the model, sensory analysis was employed to confirm that the milk had indeed spoiled, providing validation through the sensory perceptions of participants. This study demonstrates the potential of combining these methodologies to enhance the efficiency and accuracy of spoilage detection in milk. Food spoilage ; Pasteurization ; Reliability (semiconductor) ; Linear discriminant analysis ; Pattern recognition (psychology) ; Sample (material) ; Raw milk Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Milk Quality and Mastitis in Dairy Cows
Authors: Pedro Escudero Murcia | Jian Xiang Lian | Yaroslav Grosu | Javier Carrasco
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Extraction and Standardization of Graphical Data from Scientific Literature Standardization ; Consistency (knowledge bases) ; Task (project management) ; Data extraction ; Scientific literature ; Graphical display Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Image Retrieval and Classification Techniques
Authors: Weijie Tang | Zhuodong Tang | Jinxiang Li | Ruixin Yang | Liping Jiang | Wenlei Zhu | Jun‐Jie Zhu | Zixuan Chen
Source: Angewandte Chemie (article, 2025-12-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Digital Decoding of Multicomponent Protein Systems via Nanocavity‐Confined Single‐Molecule Raman Fingerprinting Abstract The ability to identify individual protein species within multicomponent systems remains a major challenge, yet is essential for next‐generation molecular diagnostics and proteomic analysis. Here, we present a single‐molecule Raman fingerprinting strategy for automatic digital decoding of protein compositions in complex systems. A dual‐amplified, interface‐coupled plasmonic nanocavity architecture synergistically integrates gap‐mode coupling with surface plasmon resonance, generating ultralow‐volume, highly enhanced hotspots that reproducibly confine and isolate single proteins, enabling acquisition of intrinsic Raman spectra free from spectral overlap. Using this platform, we achieve high‐throughput hyperspectral Raman fingerprinting of seven representative proteins. A customized machine learning algorithm trained on single‐molecule Raman datasets enables automatic identification and spatial mapping of individual protein species, yielding quantitative and addressable decoding maps. This broadly applicable strategy establishes an intelligent, data‐driven framework for multiplexed protein analysis under ambient conditions, with far‐reaching implications for molecular diagnostics, biosensing, and mechanistic studies of protein function. Raman spectroscopy ; Decoding methods ; Hyperspectral imaging ; Plasmon ; Multiplexing ; Identification (biology) ; Coupling (piping) ; Raman scattering Gold and Silver Nanoparticles Synthesis and Applications ; Orbital Angular Momentum in Optics ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Lin, Peijia | Chen, Pin | Jiao, Rui | Mo, Qing | Cen, Jianhuan | Huang, Wenbing | Liu, Yang | Huang, Dan | Lu, Yutong
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Equivariant Diffusion for Crystal Structure Prediction In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Equivariant map ; Crystal structure prediction ; Translation (biology) ; Lattice (music) ; Convergence (economics) ; Generative grammar ; Inference ; Diffusion Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Hakimeh Negaresh | S. Hajir Bahrami | Masoumeh Haghbin Nazarpak | Morteza Mehrjoo
Source: Materials Today Communications (article, 2025-11-21)
Field: Materials Science — Biomaterials

Development and predictive modeling of PLLA/gelatin nanofibers with functionalized CNTs for bone tissue engineering Polymeric scaffolds are crucial for addressing bone defects. In this study, electrospun scaffolds composed of poly( L -lactic acid) (PLLA) and gelatin (Gel) in a 50:50 ratio were fabricated with varying concentrations of carbon nanotubes (CNTs) (1, 3, 5, and 7 wt%) and phosphate-modified CNTs (PCNTs). Electrospinning parameters were optimized to enhance fiber morphology. Nanofibers generated from a 12 % w/v PLLA/Gel solution containing 3 wt% CNT or PCNT exhibited uniform morphologies, with average diameters of 301 ± 93 nm and 296 ± 81 nm for PLLA/Gel/CNT and PLLA/Gel/PCNT, respectively. The scaffolds' physical, mechanical, and biological properties were systematically evaluated. Statistical analyses were employed to characterize physicochemical features, and machine learning (ML) models were developed to predict scaffold performance in bone tissue engineering. Six supervised ML algorithms were trained using the Seaborn and Scikit-learn Python libraries. Model evaluation using 5-fold cross-validation revealed that the Support Vector Regressor (SVR) achieved the best overall balance between error reduction and model fit, while Random Forest showed the lowest prediction error. Results revealed that CNT incorporation improved piezoelectric behavior, enhanced mechanical strength, and accelerated scaffold biodegradability. Additionally, biocompatibility assessments confirmed the non-toxicity of CNT-containing scaffolds. This work not only elucidates the physicochemical characteristics of electrospun composites but also introduces predictive ML methodologies for modeling cell-material interactions, offering a robust framework for advancing bone tissue engineering applications. Electrospinning ; Biocompatibility ; Scaffold ; Nanofiber ; Carbon nanotube ; Gelatin ; Tissue engineering Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; biodegradable polymer synthesis and properties
Authors: Al Ibrahim, Emad | Morgan, Nathan | Müller, Simon | Motati, Saikiran | Green, William
Source: TUHH Open Research (article, 2025-08-06)
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 dataset 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) ; Melting point ; Solvent ; Hildebrand solubility parameter ; Molecule ; COSMO-RS Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: C. M. A. Rahman | A. Romero | Prashnna Gyawali
Source: ArXiv.org (preprint, 2025-04-27)
Field: Materials Science — Materials Chemistry

Supervised Pretraining for Material Property Prediction Accurate prediction of material properties facilitates the discovery of novel materials with tailored functionalities. Deep learning models have recently shown superior accuracy and flexibility in capturing structure-property relationships. However, these models often rely on supervised learning, which requires large, well-annotated datasets an expensive and time-consuming process. Self-supervised learning (SSL) offers a promising alternative by pretraining on large, unlabeled datasets to develop foundation models that can be fine-tuned for material property prediction. In this work, we propose supervised pretraining, where available class information serves as surrogate labels to guide learning, even when downstream tasks involve unrelated material properties. We evaluate this strategy on two state-of-the-art SSL models and introduce a novel framework for supervised pretraining. To further enhance representation learning, we propose a graph-based augmentation technique that injects noise to improve robustness without structurally deforming material graphs. The resulting foundation models are fine-tuned for six challenging material property predictions, achieving significant performance gains over baselines, ranging from 2% to 6.67% improvement in mean absolute error (MAE) and establishing a new benchmark in material property prediction. This study represents the first exploration of supervised pertaining with surrogate labels in material property prediction, advancing methodology and application in the field. Property (philosophy) ; Robustness (evolution) ; Flexibility (engineering) ; Supervised learning ; Benchmark (surveying) ; Representation (politics) ; Class (philosophy) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Neural Network Applications
Authors: Qi Kang | Ying Ye | Shuailing Ma | Jing Cao | Z. B. Wang | Hui Huang
Source: Strength of Materials (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Using a Backpropagation Artificial Neural Network to Predict and Genetic Algorithm to Optimize the Mix Proportion Design of Magnesium Phosphate Cement: A Multi-Objective Optimization Method Backpropagation ; Artificial neural network ; Genetic algorithm ; Solid mechanics ; Magnesium ; Cement ; Materials science ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Composite material ; Metallurgy Magnesium Oxide Properties and Applications ; Multiferroics and related materials