jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shinyoung Kang | Young-Hun Kim | Jihan Kim
Source: ACS Central Science (article, 2025-08-22)
Field: Chemistry — Inorganic Chemistry

Quantum Computing Based Design of Multivariate Porous Materials Multivariate (MTV) porous materials exhibit unique structural complexities based on their diverse spatial arrangements of multiple building block combinations. These materials possess potential synergistic functionalities that exceed the sum of their individual components. However, the exponentially increasing design complexity of these materials poses significant challenges for accurate ground-state configuration prediction and design. To address this, we propose a Hamiltonian model for quantum computing that integrates compositional, structural, and balance constraints directly into the Hamiltonian, enabling efficient optimization of the MTV configurations. The model employs a graph-based representation to encode linker types as qubits. Our framework enables quantum encoding of a vast linker design space, allowing representation of exponentially many configurations with linearly scaling qubit resources, and facilitating efficient search for optimal structures based on predefined design variables. To validate our model, a variational quantum circuit was constructed and executed using the Sampling Variational Quantum Eigensolver (VQE) algorithm in the IBM Qiskit. Simulations on experimentally known MTV porous materials (e.g., Cu-THQ-HHTP, Py-MV-DBA-COF, MUF-7, and SIOC-COF2) successfully reproduced their ground-state configurations, demonstrating the validity of our model. Furthermore, VQE calculations were performed on a real IBM 127-qubit quantum hardware for validation purposes signaling a first step toward a practical quantum algorithm for the rational design of porous materials. Multivariate statistics ; Computer science ; Porosity ; Materials science ; Machine learning ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Stochastic Gradient Optimization Techniques
Authors: Philipp Huber | Philipp N. Pleßow
Source: ChemPhysChem (article, 2025-06-30)
Field: Chemistry — Inorganic Chemistry

Accurate and Efficient Description of Acidic Zeolites with Plane‐Wave Density Functional Theory Using Range‐Separated Hybrid Functionals Brønsted acidic zeolites and their reactivity are routinely studied computationally, mainly with periodic density functional theory (DFT) using the generalized gradient approximation (GGA). In many cases, large errors are observed at the GGA-level of theory, in particular reaction barriers are often underestimated and the stability of carbocations is overestimated. The use of ab initio methods, such as MP2 and CCSD(T), also with local approximations, is mostly limited to nonperiodic cluster models. In this work, for a set of reaction energies and barriers, the random phase approximation and common density functionals are investigated by comparison to DLPNO-CCSD(T) and complete basis set extrapolated MP2 calculations on large cluster models. The most accurate functionals are the range-separated hybrids ωB97M-D4, ωB97M-V, ωB97X-D4, ωB97X-V, and ωB97-D. Compared to our reference calculations, these functionals give mean absolute errors below 8 kJ mol<sup>-1</sup> and also lead to few outliers. ωB97M-D4 performs best, with an MAE of 5.1 kJ mol<sup>-1</sup> and an error that is smaller than that of complete basis set extrapolated MP2. Range-separated functionals are shown to work well in periodic calculations with plane-wave DFT. This allows the efficient calculations of very accurate reaction energies and barriers directly for the periodic system at modest computational cost. Density functional theory ; Range (aeronautics) ; Hybrid functional ; Plane wave ; Plane (geometry) ; Computational chemistry ; Chemistry ; Materials science ; Chemical physics ; Physics ; Optics ; Mathematics ; Geometry Zeolite Catalysis and Synthesis ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Ruichao Zhu | Yajuan Han | Yuxiang Jia | Sai Sui | Tonghao Liu | Zuntian Chu | Huiting Sun | Juanna Jiang | Shaobo Qu | Jiafu Wang
Source: International Journal of Intelligent Systems (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Metasurface Design via Physics‐Simplified Machine Learning Metasurface can manipulate electromagnetic (EM) waves flexibly, which provides the basis for functional integration. Recently, the efficient machine‐learning‐assisted methods have attracted intensive attentions in multifunctional metasurfaces design. However, the conventional machine‐learning‐assisted metasurfaces design is to fit the internal relationship in the form of black box, which ignores the underlying physical logic, resulting in the increased complexity of machine learning architecture with the parameters increasing. In order to adapt to the multiparameter optimization in multifunctional metasurfaces design, we propose a multiplexing neural network (MNN) based on decoupling at the physical layer to simplify both the structural parameters and the network architecture. The four interacting parameters are simplified into four independently regulated parameters so that the facile design of four functions can be realized only by multiplexing a simple neural network. For verification, four functions of scattering, anomalous reflection, focusing, and hologram are integrated in the same metasurface aperture by MNN. Performances of the metasurface are fully demonstrated by simulation and measurement. Importantly, this work paves the way for the bidirectional simplification of machine learning and metasurface design via physical inspiration, which provides an integrated design method of multifunctional metasurfaces and can be potentially applied to satellite communications and other fields. Computer science ; Physics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Molecular Communication and Nanonetworks ; Antenna Design and Analysis
Authors: Naim Abdul-Khalek | Mario Picciani | Omar Shouman | Reinhard Wimmer | Michael T. Overgaard | Mathias Wilhelm | Simon Gregersen Echers
Source: Journal of Proteome Research (article, 2025-05-09)
Field: Chemistry — Spectroscopy

To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is an encoder-decoder with an attention mechanism, classifying peptides as flyers or non-flyers, and providing both binary and categorical probabilities for four distinct classes defined in this study. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias toward synthesizability, improving predictive capacity and outperforming state-of-the-art predictors in benchmark comparisons across different human and cross-species datasets. The study further investigates the influence of protein abundance and rescoring, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated into the DLOmix framework and is accessible on GitHub at https://github.com/wilhelm-lab/dlomix. On the fly ; Mass spectrometry ; Peptide ; Artificial intelligence ; Computer science ; Chemistry ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: J. Konieczny | K. Labisz | Satılmış Ürgün | Halil Yiğit | Sinan Fi̇dan | Mustafa Özgür Bora | Ş. Hakan Atapek | J. Ćwiek
Source: Materials (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Modelling of Hardness and Electrical Conductivity of Cu-4Ti (wt.%) Alloy and Estimation of Aging Parameters Using Metaheuristic Algorithms This study focuses on cold deformation and age effects on the microhardness and electric conductivity of the Cu-4Ti (wt.%) alloys. The samples were solution treated at 900 °C, quenched in water, and aged at 450-600 °C for 1-120 min. Fifty percent cold rolling was performed before aging to analyze the impact on their microstructure and properties. Hardness and electric conductivity were examined by the Vickers microhardness and Förster testing. Hardness increased significantly while electric conductivity was maintained. The optimal hardness of 298 HV appeared following 50% cold rolling and aging for 120 min at 450 °C, and an electric conductivity of 9.4 MS/m was achieved after 120 min at 600 °C in cold-rolled materials. The deformed and solution-treated materials reached 244 HV after 120 min at 500 °C, and electric conductivity reached 7.7 MS/m. Polynomial models of regression were used to analyze the impact of aging parameters on properties. Process parameters were properly optimized by applying metaheuristic algorithms. These contributions ensure a better understanding of the relationship between the microstructure and properties in Cu-Ti alloys, as well as their application in aircraft and electronics. Indentation hardness ; Microstructure ; Materials science ; Conductivity ; Electrical resistivity and conductivity ; Vickers hardness test ; Alloy ; Composite material ; Metallurgy ; Algorithm ; Mathematics ; Electrical engineering ; Chemistry Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Zia Ur Rehman | Zijing Lin
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Optoelectronic characteristics and stability evaluation of Ba<sub>2</sub>TiMxO<sub>6</sub> (Mx = Ge, Sn, Se, Te) p-type semiconductors as candidates for functional layers in optoelectronic devices An investigation of novel Ba 2 TiMxO 6 (Mx = Ge, Sn, Se, Te), highlighting promising candidates for UV optoelectronics and solar cell applications. Materials science ; Optoelectronics ; Semiconductor ; Stability (learning theory) ; Germanium ; Silicon Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Maryam Rashid | Neha Sajjad | Nusrat Shafiq | Shagufta Parveen | Erum Chaudhry | Rashid Ahmed Khan | Pravin Badhe | Mohammed H. AL Mughram | Musaab Dauelbait | Mohammed Bourhia
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Biomaterials

Unveiling Calotropis procera’s promising wound healing secrets by QSAR, molecular docking, DFT and ADMET profiling This research work comprises the computational analysis of natural compounds from Calotropis procera analyzed on the basis of their wound healing properties. 3D Quantitative structure activity relationship (QSAR) models were generated with statistical validation which was followed by leading compound candidates. This analytical technique was carried out for the evaluation of active compounds by correlation of structural parameters. Previously isolated natural compounds were selected on the basis of their in-vivo and in-vitro studies. After 3D-QSAR analysis, molecular docking procedure was performed on the active compounds against wound healing proteins (PDB: 6SMA, 5A8Y) and their binding scores were analyzed for the evaluation of binding interaction of Protein-Ligand complexes. Comparative analysis was also carried out by comparing the docking results of natural compounds and FDA approved antibiotics Cephalexin, Dicloxacillin, and Levofloxacin. After docking, Re-docking was also performed as an additional cross analysis technique for validation of the docking results. DFT study was carried out to reveal molecular activity by evaluation of quantum molecular properties. Hence, results revealed that Stigmasterol (12) is hit compound with better results than standard drugs Cephalexin and Levofloxacin. These derived results in this study provide significant information for wound healer drug-designing by using Stigmtasterol as natural compound having better wound healing property. The analysis suggested that the proposed hit compound exhibits the potential drug attribute, which indicates the suitability for future investigation to help treat the serious wounds or injuries. Calotropis procera ; Docking (animal) ; Profiling (computer programming) ; Quantitative structure–activity relationship ; Computational biology ; Chemistry ; Biology ; Stereochemistry ; Computer science ; Medicine ; Botany Phytochemistry and Bioactive Compounds ; Wound Healing and Treatments ; Microbial Applications in Construction Materials
Authors: Rabab Kamel | Giuseppina Nocca | Elena Mazzinelli | Somaia S. Abd El‐Karim | Valentina Papa | Ilaria Cacciotti | Nermeen A. Elkasabgy
Source: AAPS PharmSciTech (review, 2025-06-10)
Field: Materials Science — Biomaterials

Unveiling the Potential of Nanoclays in Pharmaceuticals Clay minerals or nanoclays are layered aluminosilicate nanoparticles with unique physicochemical properties, excellent biocompatibility, high surface area, lamellar structure, diverse ion-exchange capacity, tuneable surface chemistry enabling them to form different intermolecular interactions which offer supreme prospects for optimized pharmaceutical applications. This review article dives into the key characteristics of different types of nanoclays like Halloysite, Kaolinite, Sepiolite, Montmorillonite, Bentonite and Laponite, highlighting their possible exploitation within the pharmaceutical landscape. Also, the review describes the recent advances in nanoclay-based drug delivery systems and discusses the role of nanoclays as drug carriers and their use in tissue engineering and gene therapy. Nanoclays, are used to modulate drug release and stabilize the loaded active molecules as well as to enhance drug loading. Interdisciplinary research, advanced characterization techniques, and careful formulation designs are required to augment the use of nanoclays in various pharmaceutical applications. Despite the faced challenges, research on nanoclay-based drug delivery systems is growing. With targeted research and development, nanoclays are poised to redefine the biomedical domain, offering tailored and efficient therapies for a multitude of diseases. Chemistry ; Nanotechnology ; Materials science Clay minerals and soil interactions ; Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery
Authors: A. G. Usman | Sagiru Mati | Hanita Daud | Ahmad Abubakar Suleiman | Sani I. Abba | Hijaz Ahmad | Taha Radwan
Source: Scientific Reports (article, 2025-05-13)
Field: Chemistry — Analytical Chemistry

Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples The accurate determination of mycotoxins in food samples is crucial to guarantee food safety and minimize their toxic effects on human and animal health. This study proposed the use of a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) to predict chromatographic retention time of various food mycotoxin groups. The dataset was collected from secondary sources and used to train and validate the SVR-HHO and SVR-PSO models. The performance of the models was assessed via mean square error, correlation coefficient, and Nash-Sutcliffe efficiency. The SVR-HHO model outperformed existing methods by 4-7% in both the two learning (training and testing) phases respectively. By using metaheuristic optimization, parameter adjustment became more effective, avoiding trapping in local minima and improving model generalization. These results demonstrate how machine learning and metaheuristics may be combined to accurately forecast mycotoxin levels, providing a useful tool for regulatory compliance and food safety monitoring. The SVR-HHO framework is perfect for commercial quality assurance, regulatory testing, and extensive food safety programs because it provides exceptional accuracy and resilience in predicting mycotoxin retention times. In contrast to conventional models, SVR-HHO effectively manages intricate nonlinear interactions, guaranteeing accurate mycotoxin identification and improving food safety while lowering hazards to human and animal health. Computer science ; Mycotoxin ; Artificial intelligence ; Machine learning ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Water Quality Monitoring and Analysis
Authors: M. Wen | Weng Kin Wong | Tanja Junkers
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Synthesis of biodegradable vinyl copolymers <i>via</i> enforced regular sequence distribution from automated radical ring opening polymerisation A method based on continuous Bayesian optimization of monomer feed in a semi-batch copolymerization is demonstrated that allows countering the composition drift in copolymerizations stemming from disparate reactivity ratios. The method requires online monitoring of the reaction, but requires no prior kinetic knowledge on the copolymerization or any modelling of the polymerizations, making this the first method generally applicable to any copolymerization system to achieve this aim. Copolymerizations between acrylates, methacrylates and styrene are demonstrated to achieve perfectly statistical and homogeneous distributions, and the radical ring opening copolymerisation between a cyclic ketene acetal and methyl methacrylate is showcased as an example of a challenging copolymerization where countering the composition drift results in a completely degradable material, paving a pathway to new sustainable polymers in the future. Next to perfect regulation of the sequence distribution in these copolymers, we also demonstrate how the method can be applied to create non-natural composition drifts in polymers at will. Copolymer ; Ring (chemistry) ; Polymer chemistry ; Sequence (biology) ; Polymerization ; Ring-opening polymerization ; Radical polymerization ; Materials science ; Distribution (mathematics) ; Chemistry ; Organic chemistry ; Polymer ; Mathematics Advanced Polymer Synthesis and Characterization
Authors: Fangting Ye | Shaojie Zheng | Shuo Shan | Jianfeng Cai | Yufan Liu | Weichao Chen | Xinxin He | Chao Zhao
Source: Food Frontiers (article, 2025-04-02)
Field: Materials Science — Biomaterials

Delivery of Natural Small Molecules Through Nanocarriers for Cancer Treatment ABSTRACT Cancer is a formidable adversary, accounting for the second highest number of fatalities worldwide. A multitude of studies have indicated that naturally sourced small‐molecule compounds, owing to their low toxicity and multi‐target capabilities, significantly aid in the fight against tumorigenesis. However, natural small molecules have certain limitations, such as poor water solubility and permeability. These limitations curtail their clinical potency and result in lower bioavailability within the human body. To address these limitations, nano‐encapsulation techniques have emerged as a promising strategy to enhance the solubility, stability, and delivery of these natural compounds. For instance, in several preclinical studies, encapsulating resveratrol in liposomal nanocarriers increased its solubility by up to 50‐fold and enhanced its bioavailability by three to five times. This work is based on the reports published from 1999 to 2024 in PubMed and ISI Web of Science databases. This review examines the anticancer mechanisms of key natural small molecules, including polyphenols, alkaloids, and terpenes, and discusses how their incorporation into nanocarrier systems improves their efficacy. Additionally, this analysis provides a comprehensive overview of how nanotechnology can overcome the pharmacokinetic and bioavailability limitations of natural compounds, offering a foundation for future advancements in cancer treatment. Nanocarriers ; Natural (archaeology) ; Cancer therapy ; Nanotechnology ; Cancer treatment ; Cancer ; Medicine ; Drug delivery ; Materials science ; Internal medicine ; Geography ; Archaeology Nanoparticle-Based Drug Delivery ; Curcumin's Biomedical Applications ; Graphene and Nanomaterials Applications
Authors: Μαρία Κάζου | Katerina Psarafti | Efstathios Ζ. Panagou
Source: European Food Research and Technology (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

The future of the table olive sector: unlocking the potential of process analytical technology (PAT) in the era of digital transformation Abstract Table olives are considered as the most widely preferred fermented vegetables globally, due to their enhanced nutritional characteristics. Therefore, according to the updated Mediterranean Diet Pyramid for a balanced and healthy lifestyle, table olives should be consumed daily in moderate amounts. In recent years, the food industry has experienced substantial transformations, due to the innovative technologies introduced by the Fourth Industrial Revolution, or Industry 4.0. Within the context of the Internet of Things, Process Analytical Technology (PAT) facilitates in-line, on-line and at-line monitoring, utilizing multivariate data collected through analytical techniques and multivariate data analysis. Therefore, the implementation of PAT enables the analysis of a higher number of samples, resulting in enhanced comprehension and control of the raw materials, intermediate products during production, and, ultimately, the end products for consumption. This review summarizes the existing literature on the application of PAT in different stages of table olive production. Finally, the possibility of using PAT for table olive authentication is also discussed. Table (database) ; Process (computing) ; Process analytical technology ; Transformation (genetics) ; Engineering ; Business ; Process engineering ; Biotechnology ; Computer science ; Work in process ; Chemistry ; Operations management ; Biology ; Data mining Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Edible Oils Quality and Analysis