jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmed El‐Mallul | Ryszard Tomasiuk | Tadeusz Pieńkowski | Małgorzata Kowalska | David Hasan | M. Kostrzewa | Dominik Czerwonka | Aleksandra Sado | Wiktoria Rogowska | Igor Z. Zubrzycki | Magdalena Wiącek
Source: Nanomaterials (review, 2025-08-06)
Field: Materials Science — Biomaterials

Applications of Nanoparticles in the Diagnosis and Treatment of Ovarian Cancer Nanotechnology offers innovative methodologies for enhancing the diagnosis and treatment of ovarian cancer by utilizing specialized nanoparticles. The utilization of nanoparticles offers distinct advantages, specifically that these entities enhance the bioavailability of therapeutic agents and facilitate the targeted delivery of pharmacological agents to neoplastic cells. A diverse array of nanoparticles, including but not limited to liposomes, dendrimers, and gold nanoparticles, function as proficient carriers for drug delivery. Nevertheless, notwithstanding the auspicious potential of these applications, challenges pertaining to toxicity, biocompatibility, and the necessity for comprehensive clinical evaluations pose considerable barriers to the widespread implementation of these technologies. The incorporation of nanotechnology into clinical practice holds the promise of significantly transforming the management of ovarian cancer, offering novel diagnostic tools and therapeutic strategies that enhance patient outcomes and prognoses. In summary, the deployment of nanotechnology in the context of ovarian cancer epitomizes a revolutionary paradigm in medical science, amalgamating sophisticated materials and methodologies to enhance both diagnostic and therapeutic outcomes. Continued research and development endeavors are essential to fully realize the extensive potential of these innovative solutions and address the existing challenges associated with their application in clinical settings. Nanotechnology ; Context (archaeology) ; Ovarian cancer ; Drug delivery ; Nanomedicine ; Medicine ; Computer science ; Cancer ; Risk analysis (engineering) ; Nanoparticle ; Materials science ; Biology Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; RNA Interference and Gene Delivery
Authors: Domantas Kuryla | Fabian Berger | Gábor Csányi | Angelos Michaelides
Source: ArXiv.org (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets Training of general-purpose machine learning interatomic potentials (MLIPs) relies on large datasets with properties usually computed with density functional theory (DFT). A pre-requisite for accurate MLIPs is that the DFT data are well converged to minimize numerical errors. A possible symptom of errors in DFT force components is nonzero net force. Here, we consider net forces in datasets including SPICE, Transition1x, ANI-1x, ANI-1xbb, AIMNet2, QCML, and OMol25. Several of these datasets suffer from significant nonzero DFT net forces. We also quantify individual force component errors by comparison to recomputed forces using more reliable DFT settings at the same level of theory, and we find significant discrepancies in force components averaging from 1.7 meV/Å in the SPICE dataset to 33.2 meV/Å in the ANI-1x dataset. These findings underscore the importance of well converged DFT data as increasingly accurate MLIP architectures become available. Density functional theory ; Component (thermodynamics) ; Training set ; Interatomic potential ; Net (polyhedron) ; Data modeling Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Protein Structure and Dynamics
Authors: Ming Wu | Yiqing Zeng | JianGang Chen | Zhen Yang | Siyuan Song | Ran Yan | Taofik Al Hassan | Y P Zhang
Source: Cyborg and Bionic Systems (article, 2025-12-29)
Field: Chemistry — Organic Chemistry

Ferrocene-Modified Nanoscale Covalent Organic Frameworks for Ferroptosis-Based Sonodynamic Therapy Inhibit Breast Cancer and Its Bone Metastasis Breast cancer continues to be a marked risk to women's health worldwide. In particular, bone metastasis stands as one of the leading causes of death among breast cancer patients. In recent years, therapies that reprogram the tumor's immunosuppressive microenvironment have emerged as promising antimetastatic strategies. In this study, we developed a sonodynamic nanoplatform that simultaneously eradicates primary breast tumors via ferroptosis and apoptosis and blocks their spread to bone tissues. Ferrocene-grafted nanoconfined covalent organic frameworks (mCOFs) were synthesized by condensing aminoferrocene with micrometer-sized COFs, yielding a multifunctional agent capable of amplifying reactive oxygen species (ROS) production. Following ultrasound treatment, the mCOFs generated abundant ROS, while the ferrocene moieties catalyzed the Fenton-like conversion of endogenous H<sub>2</sub>O<sub>2</sub> into cytotoxic •OH radicals. In vitro, ultrasound-activated mCOFs concurrently induced apoptosis and ferroptosis in breast cancer cells and triggered the robust release of immunogenic factors. In orthotopic mouse models, intravenously administered mCOFs preferentially accumulated in tumors; upon ultrasound exposure, these mCOFs markedly inhibited the growth of primary tumors, reprogrammed the immunosuppressive tumor microenvironment, and effectively reduced bone metastasis. This study proposes a versatile nanomedicine-based strategy that integrates sonodynamic therapy with immunomodulation to control breast cancer progression and bone metastasis, offering a broad approach for reducing metastatic disease. Breast cancer ; Bone metastasis ; Metastasis ; Cancer ; Disease Ferrocene Chemistry and Applications ; Ferroptosis and cancer prognosis ; Cancer, Lipids, and Metabolism
Authors: Henrique Musseli Cezar | Tilmann Bodenstein | Henrik Andersen Sveinsson | Morten Ledum | Simen Reine | Sigbjørn Løland Bore
Source: npj Computational Materials (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Learning atomic forces from uncertainty-calibrated adversarial attacks Abstract Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs). While already providing great practical value, little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled. We propose the Calibrated Adversarial Geometry Optimization (CAGO) algorithm to discover adversarial structures with user-assigned errors. Through uncertainty calibration, the estimated uncertainty of MLIPs is unified with real errors. By performing geometry optimization for calibrated uncertainty, we reach adversarial structures with the user-assigned target MLIP prediction error. Integrating with active learning pipelines, we benchmark CAGO, demonstrating stable MLIPs that systematically converge structural, dynamical, and thermodynamical properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures, where previously many thousands were typically required. Adversarial system ; Computer science ; Computer security ; Artificial intelligence Machine Learning in Materials Science ; Nuclear Physics and Applications ; Force Microscopy Techniques and Applications
Authors: Andrew T. Powis | D. Rivera | Alexander Khrabry | Igor Kaganovich
Source: ArXiv.org (preprint, 2025-10-02)
Field: Chemistry — Spectroscopy

Accelerating kinetic plasma simulations with machine learning generated initial conditions Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with traditional first-principles simulations and high-performance computing offer many interesting pathways towards resolving this challenge. We consider acceleration of kinetic plasma simulations via machine learning generated initial conditions. The approach is demonstrated through modeling of capacitively coupled plasma discharges relevant to the microelectronics industry. Three models are trained on simulations across a parameter space of device driving frequency and operating pressure. The models incorporate elements of a multi-layer perceptron, principal component analysis, and convolutional neural networks to predict the final time-averaged profiles of ion-density and velocity distribution functions. These data-driven initial condition generators (ICGs) provide a mean speedup of 17.1x in convergence time, when measured using an offline procedure, or a 4.4x speedup with an online procedure, with convolutional neural networks leading to the best performance. The paper also outlines a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins. Speedup ; Convolutional neural network ; Acceleration ; Artificial neural network ; Convergence (economics) ; Workflow ; Hyperparameter Mass Spectrometry Techniques and Applications
Authors: Jan Weinreich | Konstantin Karandashev | Daniel Jose Arismendi Arrieta | Kersti Hermansson | O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (preprint, 2025-07-29)
Field: Materials Science — Materials Chemistry

Calculated solvation and ionization energies for thousands of organic molecules relevant to battery design Abstract We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound’s utility as a lithium battery electrolyte. The first property is energy changes associated with charge excitations of molecules, namely ionization potential and electron affinity. They were estimated for 7000 randomly chosen molecules with up to 9 non-hydrogen atoms C, N, O, and F (QM9 dataset) using the DH-HF, DF-HF-CABS, PNO-LMP2-F12, and PNO-LCCSD(T)-F12 methods as implemented in the Molpro software, and the aug-cc-pVTZ basis set. Additionally, we provide the corresponding atomization energies at these levels of theory, as well as the CPU time and disk space used during the calculations. The second property is solvation energies for 39 different solvents, which we estimate for 18361 molecules connected to battery design (Electrolyte Genome Project dataset), 309463 randomly chosen molecules with up to 17 non-hydrogen atoms C, N, O, S, and halogens (GDB17 dataset), as well as 88418 atoms-in-molecules of the ZINC database of commercially available compounds and 37772 atoms-in-molecules of GDB17. For these calculations we used the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) method; we additionally provide estimates of gas-phase atomization energies, as well as information about conformers considered during the COSMO-RS calculations, namely coordinates, energies, and dipole moments. Solvation ; Battery (electricity) ; Organic molecules ; Ionization ; State (computer science) ; Molecule ; Computational chemistry ; Materials science ; Chemical physics ; Chemistry ; Computer science ; Physics ; Organic chemistry ; Thermodynamics ; Ion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Geneviève Dusson | Michael F. Herbst | Benjamin Stamm
Source: Oberwolfach Reports (article, 2025-08-29)
Field: Chemistry — Physical and Theoretical Chemistry

Mathematical Methods in Quantum Chemistry Quantum chemistry focuses on modelling and simulating the behaviour of molecular systems using the fundamental principles of quantum mechanics. However, the underlying equations, such as the Schrödinger equation for computing the ground state of N electrons, which is a partial differential equation defined on \mathbb{R}^{3N} , suffer from the curse of dimensionality. As a result, simulating even moderately sized molecules demands advanced techniques in analysis, approximation and reduced-order modelling for overcoming the naive computational complexity. In this workshop, the rapidly growing mathematical community working in the field together with several quantum chemists and physicists were gathered to discuss recent advances in areas such as (1) the mathematical and numerical analysis of standard models used in the field, including Density Functional Theory, Coupled Cluster, and the Density Matrix Renormalization Group (2) the development of efficient numerical methods, relying e.g. on the development of error bounds and (3) the opportunities of recent data-driven methods towards the approximation of wavefunctions or density matrices. Quantum chemistry ; Chemistry ; Theoretical physics ; Computer science ; Computational chemistry ; Physics ; Organic chemistry ; Molecule ; Supramolecular chemistry History and advancements in chemistry ; Advanced Physical and Chemical Molecular Interactions
Authors: Md. Nurul Amin | Muhammad Mudassir Ahmad Alwi | Muhammad Jawad | Monaf Hodhod
Source: Research Square (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware ML Surrogates for DFT Strain–Bandgap Engineering in CsSnX3 (X = Cl, Br, I) Isotropy ; Gaussian ; Kernel (algebra) ; Sensitivity (control systems) ; Gaussian process ; Gaussian function Machine Learning in Materials Science ; Perovskite Materials and Applications ; Model Reduction and Neural Networks
Authors: Karolina Majewska | Arkadiusz Szczęśniak | Olaf Dybiński | Aliaksandr Martsinchyk | Pavel Shuhayeu | Viktoriya Podhurska | А.С. Куприн | J. Milewski
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Electrochemical Degradation Mechanisms in Molten Carbonate Fuel Cells and Strategies for Corrosion Mitigation Molten carbonate fuel cells (MCFCs) offer high efficiency and fuel flexibility, but they suffer from corrosion of metallic components, particularly current collectors. This work examines degradation mechanisms of Fe-, Cr-, and Ni-based alloys under MCFC conditions, highlighting oxide dissolution and spinel growth that increase ASR. Advanced strategies, including alloying and coatings (CrN, TiN, TiAlN, Co-Cr, Cu-Ni-Zn-Fe), are discussed as pathways to improve durability and enable liquid-fuel MCFC applications. Molten carbonate fuel cell ; Dissolution ; Corrosion ; Spinel ; Degradation (telecommunications) ; Fuel cells ; Oxide Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Fidele Benimana | Christopher Kucha | Anupam Roy | Anand Mohan
Source: eFood (article, 2025-12-22)
Field: Chemistry — Analytical Chemistry

Spectroscopic Methods of Edible Flower Authentication and Quality Control for Food Applications ABSTRACT The global demand for edible flowers has increased due to their diverse applications in food, nutraceuticals, and the medical field. However, issues of species identification, adulteration, contamination, and quality necessitate the use of advanced methods to authenticate product quality for edible flowers. Conventional methods are expensive, time‐consuming, and require highly skilled personnel and technical expertise. Spectroscopic methods, including Fourier transform infrared, near‐infrared, and Raman spectroscopy, are efficient, fast, and non‐destructive, providing rapid insight into the chemical structure and authenticity of edible flowers. This review systematically summarizes the recent advances in spectroscopic methods for authenticating edible flowers, including the detection of chemical changes and ensuring product integrity. The primary goal is to examine the applications of spectroscopic techniques for assessing quality changes in edible flowers during processing for food applications. Spectroscopic techniques, such as FT‐IR, NIR, and Raman spectroscopy, are rapid, accurate, and non‐destructive alternatives for authenticating the composition and quality of edible flowers. These methods enable the detection of bioactive compounds, differentiation of species, and identification of adulterants with minimal sample processing. Furthermore, chemometric models enhance data analysis, allowing for automated classification and real‐time quality monitoring of edible flowers. Authentication (law) ; Quality (philosophy) ; Identification (biology) ; Product (mathematics) ; Food products ; Food quality ; Edible plants Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Identification and Quantification in Food
Authors: Abdullah Kepçeoğlu | Yavuz Ekíncíoğlu
Source: Düzce Üniversitesi Bilim ve Teknoloji Dergisi (article, 2025-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical Investigation of the Molecular Properties of the Fluoroaniline and Fluoroanisole Isomers This research paper aims to analyse the molecular properties of fluoroaniline and fluoroanisole isomers through a range of theoretical methods. These methods include optimization of molecular structures, conformational analysis, and calculation of nonlinear optics (NLO) properties, frontier molecular orbital (HOMO-1, HOMO/SOMO, LUMO, LUMO+1) energies, chemical reactivity descriptors (ionization potentials - vertical and adiabatic, electron affinity, chemical hardness, softness, and electronegativity), molecular electrostatic potential (MEP), natural bonding orbital (NBO), and UV-Vis spectra. To achieve this, the density functional theory method with B3LYP functional and 6-311++G (d, p) basis set were used for the calculations. Additionally, the research examines the vertical and adiabatic ionization energy parameters of the molecules by constructing singly charged cation radicals. The outcome of this study provides valuable insights into the molecular properties of fluoroaniline and fluoroanisole isomers, which can be useful in the production of pharmaceuticals and agrochemicals. HOMO/LUMO ; Electronegativity ; Natural bond orbital ; Electron affinity (data page) ; Ionization energy ; Molecular orbital ; Chemistry ; Computational chemistry ; Molecule ; Density functional theory ; Ionization ; Hyperpolarizability ; Chemical physics ; Organic chemistry ; Polarizability ; Ion Nonlinear Optical Materials Research ; Organic Chemistry Cycloaddition Reactions ; Free Radicals and Antioxidants
Authors: Mustafa Ahmed Jalal Al-Sammarraie | Łukasz Gierz | Ghaith H. Jihad | Zeki Gökalp | Osman Özbek | Piotr Markowski
Source: Food and Bioprocess Technology (article, 2025-06-23)
Field: Chemistry — Analytical Chemistry

Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks Abstract Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality. Convolutional neural network ; Jet (fluid) ; Plasma ; Image (mathematics) ; Artificial neural network ; Image processing ; Artificial intelligence ; Biological system ; Environmental science ; Chemistry ; Pattern recognition (psychology) ; Computer science ; Physics ; Biology ; Mechanics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Sami M. Ibn Shamsah
Source: Materials (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Modeling Magnetic Transition Temperature of Rare-Earth Transition Metal-Based Double Perovskite Ceramics for Cryogenic Refrigeration Applications Using Intelligent Computational Methods Rare-earth transition metal-based double perovskite ceramics E<sub>2</sub>TMO<sub>6</sub> (where E = rare-earth metals, T = transition metals, and M = metal) have received impressive attention lately for cryogenic applications as a result of their intrinsic physical features such as multiferroicity, dielectric features, and adjustable magnetic transition temperature. However, determination and enhancement of magnetic transition temperature of E<sub>2</sub>TMO<sub>6</sub> ceramic are subject to experimental procedures and processes with a significant degree of difficulties and cumbersomeness. This work proposes an extreme learning machine (ELM)-based intelligent method of determining magnetic transition temperature of E<sub>2</sub>TMO<sub>6</sub> ceramics with activation function sigmoid (SM) and sine (SE) at varying magnetic field. The outcomes of the SE-ELM and SM-ELM models were compared with genetically optimized support vector regression (GEN-SVR) predictive models using RMSE, CC, and MAE metrics. Using the testing samples of E<sub>2</sub>TMO<sub>6</sub> ceramics, SE-ELM predictive model outperforms GEN-SVR with a superiority of 6.3% (using RMSE metric) and 15.7% (using MAE metric). The SE-ELM predictive model further outperforms the SM-ELM model, with an improvement of 5.3%, using CC computed with training ceramic samples. The simplicity of the employed descriptors, coupled with the outstanding performance of the developed predictive models, would potentially strengthen E<sub>2</sub>TMO<sub>6</sub> ceramics exploration for low-temperature cryogenic applications and circumvent energy challenges in different sectors. Ceramic ; Refrigeration ; Dielectric ; Work (physics) ; Transition temperature ; Perovskite (structure) ; Support vector machine Advanced Thermoelectric Materials and Devices ; Phase Change Materials Research ; Machine Learning and ELM