jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anber Abraheem Shlash Mohammad | Suleiman Ibrahim Mohammad | Asokan Vasudevan | Badrea Al Oraini | Sultan Alaswad Alenazi
Source: International Journal of Drug Delivery Technology (article, 2025-12-25)
Field: Materials Science — Biomaterials

AI-Driven Optimisation of Nanoparticle-Based Drug Delivery Systems The purpose of the paper is to critically review the application of artificial intelligence (AI) to streamline the process of nanoparticle-based drug delivery systems, and in particular to address unresolved issues in therapeutic precision, safety, and clinical translation. The research uses the secondary research methodology and will synthesise evidence of peer reviews in the fields of nanotechnology, biomedical engineering, and computational modelling. This methodology allows the thorough assessment of various facets such as selection of nanoparticles material, optimization of particle size and shape, mode of surface functionalization, drug loading ability, controlled release prediction, targeting ligand design, analysis of cellular uptake pathway, prediction of pharmacokinetics, modeling of biodistribution, toxicity and biocompatibility, tumor microenvironment responsiveness, formulation optimization using AI, in vivo performance, real-time monitoring of delivery algorithms, personalized treatment, and optimization. According to the literature, it is constantly emphasised that AI will improve the accuracy of prediction, variability reduction, and speed up the design by combining multi-scale models, machine learning, and real-time monitoring. The results indicate that AI-based systems enhance drug encapsulation, can design targeted ligands, can simulate the biodistribution, and personalise treatment plans, thus closing the gap between laboratory innovations and clinical practice. Nevertheless, there are still issues in data heterogeneity, regulatory. Drug delivery ; Process (computing) ; Selection (genetic algorithm) ; Targeted drug delivery ; Personalized medicine ; Drug Nanoparticle-Based Drug Delivery ; Inhalation and Respiratory Drug Delivery ; Computational Drug Discovery Methods
Authors: Cheng Shi | Corey M. G. Carpenter | Damian E. Helbling | Gerrad D. Jones
Source: Environmental Science & Technology (article, 2025-09-05)
Field: Chemistry — Spectroscopy

Deconvoluting and Interpreting Nontargeted Chemical Data: A Data-Driven Forensic Workflow for Identifying the Most Prominent Chemical Sources in Receiving Waters Chemical forensics aims to identify major contamination sources, but existing workflows often rely on predefined targets and known sources, introducing bias. Here, we present a data-driven workflow that reduces this bias by applying an unsupervised machine learning technique. We applied both nonmetric multidimensional scaling (NMDS) and non-negative matrix factorization (NMF) on the same nontargeted chemical data set to compare their different interpretations of environmental sources. Weekly nontargeted data was collected from the Fall Creek Monitoring Station (Ithaca, NY), where daily samples were previously analyzed using source-defined models. NMF was first used to decompose the full nontargeted chemical data set into a small set of chemical factors representing distinct composition profiles. Each factor was then interpreted through (1) Spearman correlations with watershed characteristics (e.g., temperature, flow) and (2) suspect screening of high-weighted nontargeted features. In addition to confirming known anthropogenic inputs, our analysis revealed potential novel sources associated with snowmelt, groundwater seepage, and seasonal hydrological dynamics. We also detected an annual shift in the chemical composition, highlighting the evolving influence of these sources. This workflow enables watershed managers to move beyond predefined sources, detect both known and emerging chemical contributors, and apply adaptive, evidence-based strategies to protect water quality under changing conditions. Workflow ; Data science ; Computer science ; Database Mass Spectrometry Techniques and Applications ; Toxic Organic Pollutants Impact ; Forensic Fingerprint Detection Methods
Authors: Simone Marcorini | Rocco Pomodoro | Omar Abou El Kheir | Marco Bernasconi
Source: arXiv (Cornell University) (preprint, 2025-06-16)
Field: Materials Science — Materials Chemistry

Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge$_2$Sb$_2$Te$_5$ from simulations with a neural network potential Phase change materials are exploited in non-volatile electronic memories and photonic devices that rely on a fast and reversible transformation between the amorphous and crystalline phase upon heating. The recrystallization of the amorphous phase at the operation conditions of the memories occurs in the supercooled liquid phase above the glass transition temperature $T_g$. The dynamics of the supercooled liquid is thus of great relevance for the operation of the devices and, close to $T_g$, also for the structural relaxations of the glass that affect the performances of the memories. Information on the atomic dynamics is provided by the diffusion coefficient ($D$) and by the viscosity ($η$) which are, however, both difficult to be measured experimentally at the operation conditions of the devices due to the fast crystallization. In this work, we leverage a machine learning interatomic potential for the flagship phase change compound compound Ge$_2$Sb$_2$Te$_5$ to compute $η$, $D$ and the $α$-relaxation time in a wide temperature range from 1200 K to about 100 K above $T_g$. Large scale molecular dynamics simulations allowed quantifying the fragility of the liquid and the occurrence of a breakdown of the Stokes-Einstein relation between $η$ and $D$ in the supercooled phase. Isoconfigurational analysis provided a visualization of the emergence of dynamical heterogeneities responsible for the breakdown of the Stokes-Einstein relation. The analysis revealed that the regions of most mobile atoms are related to the presence of Ge atoms with particular local environments. Supercooling ; Fragility ; Amorphous solid ; Glass transition ; Polyamorphism ; Molecular dynamics ; Phase (matter) ; Dynamical heterogeneity Phase-change materials and chalcogenides
Authors: Edwin E. Reza-Zaldívar | Jorge L. Mejía-Méndez | Diego E. Navarro-López | A. Sánchez-Martínez | O. Ceballos-Sánchez | Vicente Rodríguez‐González | Miriam Jimenez-Perez | Luis Marcelo Lozano | Juan Armendáriz‐Borunda | Daniel A. Jacobo‐Velázquez | Gildardo Sanchez‐Ante | Edgar R. López-Mena
Source: Journal of Hazardous Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Optimizing machine learning models for cytotoxicity prediction in lanthanide-doped nanomaterials: A data-driven approach for minimizing environmental hazards Cytotoxicity ; Predictive modelling ; Regression analysis ; Test set ; Viability assay ; Computational model ; Renewable energy ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Cih-Yu Jheng | Wei-Qun Lai | Jun-Jeng Fen | Yu‐Fen Chang | C. Y. Cheng | Tai-Jay Chang | Yi‐Jun Lin | De‐Ming Yang | Kung‐Hao Liang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-10-04)
Field: Chemistry — Electrochemistry

Deep neural network-based detection of lead contamination via Förster resonance energy transfer in live cells Förster resonance energy transfer ; Biosensor ; Fluorescence ; Lead (geology) ; Lead exposure ; Convolutional neural network ; Microscopy ; Fluorescence microscope ; Contamination Electrochemical Analysis and Applications ; Neuroscience and Neural Engineering ; Ion channel regulation and function
Authors: Byungki Ryu | Ji Hui Son | Sungjin Park | Jaywan Chung | Hye-Jin Lim | SuJi Park | Young-Rok Do | Su-Dong Park
Source: ArXiv.org (preprint, 2025-05-25)
Field: Materials Science — Materials Chemistry

A High-Quality Thermoelectric Material Database with Self-Consistent ZT Filtering This study presents a curated thermoelectric material database, teMatDb, constructed by digitizing literature-reported data. It includes temperature-dependent thermoelectric properties (TEPs), Seebeck coefficient, electrical resistivity, thermal conductivity, and figure of merit (ZT), along with metadata on materials and their corresponding publications. A self-consistent ZT (Sc-ZT) filter set was developed to measure ZT errors by comparing reported ZT's from figures with ZT's recalculated from digitized TEPs. Using this Sc-ZT protocol, we generated tMatDb272, comprising 14,717 temperature-property pairs from 272 high-quality TEP sets across 262 publications. The method identifies various types of ZT errors, such as resolution error, publication bias, ZT overestimation, interpolation and extrapolation error, and digitization noise, and excludes inconsistent samples from the dataset. teMatDb272 and the Sc-ZT filtering framework offer a robust dataset for data-driven and machine-learning-based materials design, device modeling, and future thermoelectric research. Extrapolation ; Thermoelectric effect ; Thermoelectric materials ; Figure of merit ; Digitization ; Interpolation (computer graphics) ; Filter (signal processing) ; Set (abstract data type) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Dadashkarimi, Mojtaba
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-02)
Field: Materials Science — Materials Chemistry

Galois Connections in Density Functional Theory: Legendre-Fenchel Transforms and Electronic Structure This work introduces a novel perspective on Density Functional Theory (DFT), a fundamental method in quantum mechanics for analyzing the electronic properties of molecules and materials.The preprint addresses a critical issue in DFT: errors in widely used approximations, such as the local density and generalized gradient methods, which can result in inaccurate predictions, including incorrect dissociation energies and self-interaction artifacts. These errors, we propose, arise from disruptions in the essential relationship between potentials (the forces acting on electrons) and electron density (the distribution of electrons).To tackle this, we develop a mathematical framework grounded in category theory, which studies structures and their interconnections. We reframe the potential-density relationship as a Galois connection, a concept that pairs related sets, linked to the Legendre-Fenchel transform, a tool from optimization. The framework is structured around three key components: first, it formalizes the potential-density relationship; second, it reinterprets the foundational Hohenberg-Kohn theorems using profunctors, which are versatile mappings between categories; and third, it reformulates Lieb’s variational principle through enriched categories, enhancing the mathematical structure.This approach offers several benefits: it provides a method to evaluate the accuracy of DFT approximations by ensuring they preserve this key relationship, introduces a system to classify errors, and establishes a foundation for improved computational algorithms. For instance, we propose a Galois-corrected algorithm to enhance DFT calculations. Using the Hubbard dimer, a simple two-site model, we demonstrate how exact methods correctly localize electrons, unlike many approximations.The preprint is organized into sections covering DFT’s mathematical foundations, category theory fundamentals, the Galois connection approach, the comprehensive framework, practical applications, comparisons with existing research, and future directions, including potential extensions to time-dependent DFT and machine learning applications.This work seeks to connect abstract mathematics with practical quantum chemistry, providing valuable tools for researchers in computational physics and materials science. Preprint ; Galois theory ; Mathematical structure ; Simple (philosophy) ; Key (lock) ; Galois connection ; Connection (principal bundle) ; Density functional theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Marco Fronzi | Michael J. Ford | Kamal Singh Nayal | Olexandr Isayev | Catherine Stampfl
Source: ArXiv.org (preprint, 2025-10-03)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold Networks in Thermoelectric Materials Design The discovery of high-performance thermoelectric materials requires models that are both accurate and interpretable. Traditional machine learning approaches, while effective at property prediction, often act as black boxes and provide limited physical insight. In this work, we introduce Kolmogorov--Arnold Networks (KANs) for the prediction of thermoelectric properties, focusing on the Seebeck coefficient and band gap. Compared to multilayer perceptrons (MLPs), KANs achieve comparable predictive accuracy while offering explicit symbolic representations of structure--property relationships. This dual capability enables both reliable predictions and the extraction of physically meaningful functional forms. Benchmarking against literature models further highlights the robustness and generalisability of the approach. Our findings demonstrate that KANs provide a powerful framework for reverse engineering materials with targeted thermoelectric properties, bridging the gap between predictive performance and scientific interpretability. Thermoelectric effect ; Bridging (networking) ; Robustness (evolution) ; Benchmarking ; Thermoelectric materials ; Perceptron Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Quantum many-body systems
Authors: Christophe Bovet | Vincent Chiaruttini | Aurélien Vattré
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Full-scale crystal plasticity modeling and data-driven learning of microstructure effects in polycrystalline turbine blades Superalloy ; Turbine blade ; Microstructure ; Crystallite ; Plasticity ; Robustness (evolution) ; Nonlinear system ; Crystal plasticity ; Texture (cosmology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; High Temperature Alloys and Creep
Authors: Pjotrs Žguns (18965130) | Inga Pudza (22047543) | Alexei Kuzmin (3107304)
Source: Figshare (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Benchmarking CHGNetUniversal Machine Learning InteratomicPotential against DFT and EXAFS: The Case of Layered WS<sub>2</sub> and MoS<sub>2</sub> Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at a low computational cost, making them invaluable for materials modeling. Although uMLIPs are pretrained on vast ab initio data sets, rigorous validation remains essential for their ongoing adoption. In this study, we use the CHGNet uMLIP to model thermal disorder in isostructural layered 2H<sub><i>c</i></sub>-WS<sub>2</sub> and 2H<sub><i>c</i></sub>-MoS<sub>2</sub>, benchmarking it against ab initio data and extended X-ray absorption fine structure (EXAFS) spectra, which capture thermal variations in bond lengths and angles. Fine-tuning CHGNet with compound-specific ab initio (density functional theory (DFT)) data mitigates the systematic softening (i.e., force underestimation) typical of uMLIPs and simultaneously improves the alignment between molecular dynamics-derived and experimental EXAFS spectra. While fine-tuning with a single DFT structure is viable, using ∼100 structures is recommended to accurately reproduce EXAFS spectra and achieve DFT-level accuracy. Benchmarking the CHGNet uMLIP against both DFT and experimental EXAFS data reinforces confidence in its performance and provides guidance for determining optimal fine-tuning data set sizes. Ab initio ; Benchmarking ; Ab initio quantum chemistry methods ; Extended X-ray absorption fine structure ; Interatomic potential ; Density functional theory ; Experimental data 2D Materials and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Weijie Tang | Zhuodong Tang | Jinxiang Li | Ruixin Yang | Liping Jiang | Wenlei Zhu | Jun‐Jie Zhu | Zixuan Chen
Source: Angewandte Chemie International Edition (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 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 ; Multiplexing ; Identification (biology) ; Plasmon ; Coupling (piping) ; Raman scattering Gold and Silver Nanoparticles Synthesis and Applications ; Orbital Angular Momentum in Optics ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Meiqi Wei | Guoqing Zhang | Tingting Zhou | Jipeng Zhu | Yuhan Zhou | Changan Chen | Qi Wang
Source: International Journal of Hydrogen Energy (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Atomic-scale insights into hydrogen transport at tungsten grain boundaries with machine learning potentials Bottleneck ; Diffusion ; Grain boundary ; Tungsten ; Hydrogen ; Molecular dynamics ; Fusion Fusion materials and technologies ; Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Chi Thang Nguyen (8496897) | Miso Kim (773939) | Youn-Hye Kim (11097568) | Taehoon Cheon (6657683) | Ngoc Le Trinh (16484972) | Sehee Kim (2437423) | Soo-Hyun Kim (10655) | Bonggeun Shong (1291524) | Han-Bo-Ram Lee (1512616)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomic LayerModulation of Ruthenium Aluminum Oxidethrough Reactivity Control of Precursors for Seedless Copper Interconnects We fabricated the RuAlO<sub><i>x</i></sub> multicomponent thin film by using atomic layer modulation (ALM) based on precursor chemical reactivities and steric hindrance effects. Dicarbonyl-bis­(5-methyl-2,4-hexanediketonato)­Ru­(II) (Carish) and dimethylaluminum isopropoxide (DMAI) were employed as Ru and Al precursors, respectively, with H<sub>2</sub>O as the counter reactant. Theoretical calculations based on machine learning interatomic potential were performed to investigate the surface chemical reactions of the precursors and the feasibility of the ALM concept to modulate RuAlO<sub><i>x</i></sub> films. The transmission electron microscopy analysis revealed a distinctive structure of the RuAlO<sub><i>x</i></sub> thin films, where the typical columnar growth of Ru was prevented by the surrounding amorphous Al<sub>2</sub>O<sub>3</sub>. Sheet resistance measurement results and X-ray diffraction analyses confirmed that a 50 nm Cu/5 nm RuAlO<sub><i>x</i></sub>/SiO<sub>2</sub> structure remained stable even after annealing at 600 °C for 30 min, without any Cu silicide formation. These results suggest that a 5 nm RuAlO<sub><i>x</i></sub> thin film effectively prevents the diffusion of 50 nm of Cu. We believe that RuAlO<sub><i>x</i></sub> ALM thin films can be used as diffusion barriers against Cu, with improved performance compared to that of films with the typical Ru columnar grain structure. Annealing (glass) ; Amorphous solid ; Transmission electron microscopy ; Thin film ; Copper ; Atomic layer deposition ; Diffusion barrier ; Oxide ; Ruthenium Copper Interconnects and Reliability ; Semiconductor materials and devices ; Semiconductor materials and interfaces
Authors: Yinuo Wen | Xiao Jia | Jianjun Xiao
Source:  (article, 2025-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Performance Analysis of Metasurface Unit Cells Based on Deep-learning Metasurfaces can manipulate the wavefront of incident light by altering the geometric shape, dimensions, or material of their unit structures. However, traditional electromagnetic simulation methods for parametric sweeps of metasurface structures require extensive manual modeling, are prone to errors, and suffer from long simulation times. This paper proposes a residual neural network-based method for predicting reflection parameters of metasurface unit structures, significantly improving analysis speed. By using 2D structural images as input data, a metasurface reflection parameter prediction model was trained. Test results show close agreement between model predictions and traditional software simulations, validating the model's accuracy. Reflection (computer programming) ; Parametric statistics ; Wavefront ; Residual ; Software ; Parametric model ; Artificial neural network Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Yazhou Wang | Yiqiang Fan | Hai Xu | Tianxiang Li | Jianlong Xie
Source: Surface Topography Metrology and Properties (article, 2025-09-10)
Field: Materials Science — Surfaces, Coatings and Films

Study on the theoretical model of surface hydrophobicity in WEDM processing metal Abstract The hydrophobic surfaces of metals produced by wire electrical discharge machining (WEDM) currently lack a systematic theoretical understanding. This study proposes a theoretical model for predicting hydrophobic performance based on WEDM process parameters, developed through theoretical derivation, simulation, and machine learning. The model establishes a process-parameter-driven prediction mechanism for hydrophobicity, offering a new approach for modeling and controllable design of hydrophobic structures. In this work, micro–nano dual-scale structures were fabricated on the surface of 7075 aluminum alloy to validate the model. The results show that the calculated contact angles fall within the 90% confidence interval, demonstrating excellent predictive accuracy and reliability. This advances the transition from experience-based to theory-driven prediction of hydrophobic surfaces in WEDM, providing theoretical support and engineering guidance for the functional machining of key components across various metal systems. Machining ; Electrical discharge machining ; Aluminium ; Surface (topology) ; Process (computing) ; Alloy ; Contact angle Surface Modification and Superhydrophobicity ; Metal and Thin Film Mechanics ; Adhesion, Friction, and Surface Interactions