jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Coen de Graaf | Mar Reguero | Manuel F. Ruiz‐López
Source: Theoretical Chemistry Accounts (article, 2025-08-12)
Field: Chemistry — Inorganic Chemistry

Foreword to the special issue on the “electronic structure: principles and applications (ESPA 2024)” conference Library science ; Coronavirus disease 2019 (COVID-19) ; Archaeology ; Operations research ; Engineering ; History ; Computer science ; Medicine ; Infectious disease (medical specialty) Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Anand Kumar | Murali Krishna | Debdeep Sarkar
Source:  (article, 2025-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Automated Design and Inverse Modelling of Metasurfaces using Modified V-shaped Binary Particle Swarm Optimization and Convolutional Neural Networks This paper presents a fully automated framework for the design and inverse modeling of polarization-insensitive metasurfaces. A theoretical formulation of V-shaped transfer functions in Binary Particle Swarm Optimization (BPSO) is first established to analyze their stability and dynamics, from which a Modified V-shaped transfer function is analytically derived. This modification enhances the balance between exploration and exploitation with a ratio of 1.03, nearest to unity among existing methods, achieving an average mean-squared error of approximately 0.6% in binary electromagnetic (EM) optimization. The MVBPSO is integrated with a Python-CST automation pipeline that efficiently generates simulation data for training a lightweight Convolutional Neural Network (CNN). The trained CNN predicts metasurface geometries from target responses with sub-1% error using 50-60% fewer samples than conventional approaches. The combined MVBPSO-CNN framework enables one-shot optimization and inverse design. Experimental realization of polarization-insensitive bandpass and bandstop metasurfaces at 28 GHz and 79 GHz are performed. The results demonstrate a reliable, data-efficient, and general approach for EM optimization and inverse design. Particle swarm optimization ; Binary number ; Inverse ; Convolutional neural network ; Inverse problem ; Transfer function ; Artificial neural network Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Timothy J. Trinklein | Stanislav S. Rubakhin | Samuel Okyem | Seth W. Croslow | Marisa Asadian | K. R. Sabitha | Orly Lazarov | Fan Lam | Jonathan V. Sweedler
Source: Nature Communications (article, 2025-12-09)
Field: Chemistry — Spectroscopy

Multimodal mass spectrometry imaging for plaque- and region-specific neurolipidomics in Alzheimer’s disease mouse models The progressive accumulation of amyloid beta (Aβ) plaques is a hallmark of Alzheimer's disease (AD). However, the biochemical mechanisms of their formation and the consequences associated with plaque formation remain elusive. In female 5xFAD and APP<sup>NL-G-F</sup> mice, we map region-specific, plaque-associated lipids with large molecular coverage including isomers. We describe a multimodal framework that integrates matrix assisted laser desorption/ionization with laser-induced postionization (MALDI-2) mass spectrometry imaging, trapped ion mobility spectrometry, and fluorescence microscopy. Our approach improves detectability and spatial-chemical resolution. We couple these measurements with a computational pipeline for multimodal image coregistration and discovery of plaque-altered lipids. Here, we show the lipids in and around Aβ plaques are highly heterogeneous. Integration of our data with existing spatial transcriptomics data suggests that region-specific accumulation of simple gangliosides is likely driven by lysosomal degradation of complex species. Together, this work provides a generalizable framework to understand lipid alterations within the Aβ plaque microenvironment. Mass spectrometry imaging ; Mass spectrometry ; Molecular imaging ; Transcriptome ; Pipeline (software) ; Fluorescence-lifetime imaging microscopy ; Proteomics ; Amyloid (mycology) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Alzheimer's disease research and treatments
Authors: Kalfin Syah Kilau Mayya | Irsyad Fadhil Makarim | Wisanggeni Atthoriq Kuswirasatya | Eva Yulia Puspaningrum
Source: Prosiding Seminar Nasional Informatika Bela Negara (article, 2025-08-12)
Field: Chemistry — Organic Chemistry

Klasifikasi Tekstur Kayu Jati dan Mahoni Menggunakan Ekstraksi Fitur GLCM dan Convolutional Neural Network (CNN) Identifikasi jenis kayu secara manual membutuhkan keahlian dan waktu, serta rentan subjektivitas. Penelitian ini mengusulkan sistem klasifikasi otomatis untuk tekstur kayu jati dan mahoni menggunakan kombinasi Convolutional Neural Network (CNN) untuk ekstraksi fitur visual dan Gray Level Co- occurrence Matrix (GLCM) untuk ekstraksi fitur tekstur. Dataset terdiri dari 220 citra (110 jati, 110 mahoni) yang melalui tahap pra-pemrosesan termasuk augmentasi data. Model yang dibangun memiliki dua input (citra dan fitur GLCM) yang digabungkan sebelum lapisan klasifikasi. Model dilatih selama 20 epoch dan dievaluasi menggunakan metrik akurasi, precision, recall, F1-score, serta confusion matrix. Hasil evaluasi menunjukkan performa yang baik dengan akurasi keseluruhan mencapai 93%. Model mampu mengklasifikasikan kayu Jati dengan recall 100% dan Mahoni dengan recall 86%. Penggabungan CNN dan GLCM terbukti efektif meningkatkan akurasi klasifikasi tekstur kayu. Pattern recognition (psychology) ; Confusion matrix ; Convolutional neural network ; Artificial neural network Wood and Agarwood Research ; Computer Science and Engineering ; Agricultural and Environmental Management
Authors: Ružica Marković | Kurt V. Mikkelsen
Source: Applied sciences research periodicals. (article, 2025-07-11)
Field: Chemistry — Spectroscopy

Exploring Norbornadiene/Quadricyclane Structures for Possible Alzheimer’s Disease Detection Developing efficient and selective biomarkers for amyloid plaques, a hallmark of Alzheimer’s disease, is a crucial step to wardimproving detection methods and potentially preventing the disease. In this study, we explore fluorescent photoswitches based on a norbornadiene (NBD) system combined with pyrene as po tential biomarker candidates. Specifically, NBD-1PY and NBD 2PY were selected, each featuring different pyrene substitution patterns. Their UV-Vis absorption spectra were computed using time-dependent density functional theory (TDDFT) across various solvent systems. Our findings indicate that the calculated spec tra in toluene closely match experimental results. Additionally, minimal spectral shifts were observed upon transitioning to sol vents more suitable for biological applications. The energy differ ences between the photoswitchable NBD and quadricyclane (QC) forms remained largely unaffected by solvent variations. These results suggest that both NBD-1PY and NBD-2PY exhibit promis ing properties for further investigation as potential biomarkers for Alzheimer’s disease detection. Quadricyclane ; Norbornadiene ; Chemistry ; Organic chemistry Molecular Sensors and Ion Detection ; Computational Drug Discovery Methods
Authors: Vipin Kumar Sharma
Source: Progress in Petrochemical Science (review, 2025-05-14)
Field: Chemistry — Analytical Chemistry

Liquefied Petroleum Gas: A Comprehensive Review of Its Manufacturing and Refining Routes Liquefied Petroleum Gas (LPG) is a clean-burning, high-energy fuel that plays a vital role in energy systems across residential, commercial, and industrial sectors globally.This paper offers an extensive review of the diverse manufacturing and refining methods involved in LPG production, emphasizing both traditional and innovative technologies.LPG is mainly produced as a by-product of natural gas processing and petroleum refining.The review delves into essential processes such as crude oil distillation, catalytic cracking, and hydro processing, which are crucial for the separation and enhancement of propane and butane, the primary constituents of LPG.Furthermore, the paper examines the incorporation of gas recovery systems in upstream natural gas operations and the significance of fractionation units in maintaining the purity and safety of the final LPG product.It also addresses strategies for process optimization aimed at improving yield and energy efficiency, alongside environmental considerations linked to LPG production.Given the rising demand for low-emission fuels, the potential for producing bio-LPG from renewable resources is briefly discussed as a promising alternative.Data analysis was conducted using advanced Python programming, with predictive results generated accordingly.The study also sheds light on global production trends, regional differences in refining technologies, and the challenges related to storage, transportation, and regulatory compliance.Ultimately, this review aspires to be a valuable resource for engineers, researchers, and policymakers who wish to comprehend the technological framework of LPG manufacturing while pinpointing opportunities for innovation and enhancement considering changing energy needs and sustainability objectives. Refining (metallurgy) ; Liquefied petroleum gas ; Petroleum engineering ; Oil refinery ; Petroleum ; Process engineering ; Environmental science ; Engineering ; Waste management ; Chemistry ; Materials science ; Metallurgy ; Organic chemistry Petroleum Processing and Analysis ; Global Energy and Sustainability Research
Authors: M. Adam Khan | G. Ebenezer | J.T. Winowlin Jappes | Abdel‐Hamid I. Mourad | Dinu Thomas Thekkuden
Source: Scientific Reports (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Investigation on micro drilling of low carbon NiTi alloy using tungsten carbide micro drill tool The demand for advancements in Functional Materials due to their extensive applications especially on the development of product miniaturization is ever growing. The current study focusses on the influence of micro drilling process parameters such as tool rotational speed (1500, 2000, 2500 rpm), feed rate (0.2, 0.4, 0.6 mm/min) for a constant length of cut (500 μm) with 495 μm diameter drill tool on the low carbon nickel-titanium (NiTi) alloy, augmented with 0.05 wt% carbon. Studies on the selected rotational speeds and 0.05% carbon added NiTi are highly lacking. A blind hole of 500 μm was micro drilled on NiTi alloy using tungsten carbide (WC) micro drill bit. The results have revealed that high-speed machining at 2500 RPM facilitated the production of an accurately drilled hole with a close dimension of 501 μm at a minimum feed rate (0.2 mm/min). Under the same high-speed conditions, an increase in feed rate leads to a larger drill hole diameter due to tool outing losing precision. In addition, the drill tool experiences buckling as the feed rate increases, drill opening with pre-eminent plastic deformation, and shear, ultimately momentous in the formation of metal chips. In terms of machining time, high-speed machining requires less time (0.43 min) to complete the drilling process, whereas slow-speed machining consumes more time (up to 1.78 min). In high-speed machining, the tool edges undergo severe wear, characterized by both adhesion and abrasion. This work introduces optimized procedure to produce micro sized holes through high-speed machining of low-carbon NiTi alloy using a WC drill tool. Drill ; Machining ; Drilling ; Tungsten carbide ; Nickel titanium ; Tool wear ; Carbide Shape Memory Alloy Transformations ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Raymond F. Berkeley | B. Cook | Daniel Ji | A. Foroughi | Yifei He | Maxwell J Bachochin | Mark A. Herzik
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-06)
Field: Materials Science — Surfaces, Coatings and Films

ANTIDOTE: A Metadata-Driven Neural Network for Improving CryoEM 3-D Particle Sorting Despite the maturation of cryogenic electron microscopy (cryoEM) methodologies, generating high-resolution three-dimensional (3-D) reconstructions from micrographs is a time-intensive process involving iterative rounds of subjective data curation and hyperparameter optimization. Current approaches to particle classification are often unable to remove all low-quality particles from particle stacks, largely due to the low signal-to-noise ratio, the high dimensionality of particle images, and the multiple degrees of freedom associated with each particle's unknown rotation, orientation, and class assignment. The retention of low-quality particles negatively affects the overall quality of the final EM density and continued efforts to eliminate their deleterious contributions are warranted. Here, we present ANTIDOTE (A Neural network Trained In Deleterious Object deTection and Elimination), a neural network framework that discriminates between constructive and deleterious particles using per-particle metadata generated during 3-D classification in RELION. Using benchmark and real-world cryoEM datasets, we demonstrate that ANTIDOTE paired with RELION 3-D classification achieves higher particle classification accuracy than conventional data processing approaches alone, yielding improvements in reconstruction quality, global and local resolution, and map interpretability while reducing time-consuming hyperparameter optimization. We additionally detail practical use-case scenarios for ANTIDOTE and demonstrate its versatility in increasing particle curation accuracy for high-quality cryoEM reconstruction. Artificial neural network ; Interpretability ; Hyperparameter ; Deconvolution ; Convolutional neural network ; Curse of dimensionality ; Benchmark (surveying) ; Particle (ecology) Electron and X-Ray Spectroscopy Techniques
Authors: Atila Wuingryfi Reis Uchoa
Source: RCT - Revista de Ciência e Tecnologia (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Ultra-fast hybrid method for predicting chemical shifts of ¹³C: hybrid strategy with dynamic internal patterns and linear parameters NMR spectroscopy is an essential tool for molecular characterization and structure elucidation, but the accurate prediction of chemical shifts in complex systems is still a challenge. In this study, we developed an ultrafast predictive model for ¹³C chemical shifts, applied to a phenylpiperazine derivative, combining linear scaling and three internal standards: TMS, methanol and benzene, using the HF/3-21G/MPW1PW91/6-31G(d) method. The proposed computational model, using methanol as the internal standard, reduced the average errors by more than 50%, surpassing the computationally intensive methods described in the literature. Biological system ; Materials science ; Computer science ; Biology Advanced NMR Techniques and Applications
Authors: Yalçin Nesibe | Ünaldi Sibel | Dept. of Electrical and Electronics  Engineering | Bartos Gaye Ediboglu
Source:  (article, 2025-11-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Explainable MLP-Based Surrogate Model for Predicting Terahertz Metamaterial Absorber Performance This study presents an explainable surrogate modeling approach for predicting the absorption behavior of broadband THz metamaterial absorbers based on vanadium dioxide (VO2) by a Multi-Layer Perceptron (MLP) network. The proposed model was trained on an electromagnetic full- wave simulation dataset, which included design parameters and frequency, with the corresponding absorption as output. The MLP-based surrogate model was evaluated using performance metrics such as the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Additionally, explainable artificial intelligence (XAI) methods were used to understand how the input parameters affect the absorption. Thus, the proposed model offers important clues regarding the physical relationship between design parameters and absorption performance. The results obtained indicate that the MLP model achieves high prediction accuracy. This approach offers a promising alternative for the design and optimization of next-generation THz absorber devices. Surrogate model ; Terahertz radiation ; Mean squared error ; Absorption (acoustics) ; Perceptron ; Metamaterial ; Metamaterial absorber ; Broadband Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Thermal Radiation and Cooling Technologies
Authors: P. Divya Bharathi | K. Dhanalakshmi
Source: International Journal of Advanced Science and Engineering (article, 2025-09-27)
Field: Chemistry — Analytical Chemistry

A Secured and an Improved Feature Weighted Fuzzy Clustering and Hybrid Classification Model for the Detection of Agricultural Pests A stable agricultural economy, food security, and quality of life largely depend on efficient plant Pest Detection (PD).Rapid insect and pest infestations severely affect annual crop yields, and existing detection methods often face security and performance limitations.Globally, researchers are exploring scientific approaches that leverage Cloud Computing (CC) systems for pest detection.To address these challenges, several automation and Image Processing (IP)-based strategies have emerged in recent years.This study proposes a Hybrid Deep Learning (HDL) and Improved Feature Weighted Fuzzy Clustering (IFWFC) model for effective Pest Recognition (PR).Initially, preprocessing is performed using Z-Score Normalization (ZSN).The IFWFC process is then introduced, and data security is enhanced through a Henon Chaotic Map Encryption (HCME) technique.For feature selection, the Modified Cuckoo Search Algorithm (MCSA) is employed.Finally, pest recognition is achieved using a hybrid framework that combines HDL with Bidirectional Long Short-Term Memory (Bi-LSTM).In addition, a Faster Region Convolutional Neural Network (FRCNN) is integrated with Bi-LSTM for hybrid classification.To improve the performance further, the Improved Butterfly Optimization Algorithm (IBOA) is applied.Experimental evaluation using the IP102 dataset demonstrates that the proposed model achieves superior accuracy (ACC) compared to conventional Machine Learning (ML) methods, highlighting its effectiveness for automated pest recognition. Cluster analysis ; Feature (linguistics) ; Fuzzy logic ; Fuzzy clustering ; Pattern recognition (psychology) ; Agriculture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Wireless Sensor Networks and IoT
Authors: Fabian Zills
Source: The Journal of Open Source Software (article, 2025-12-12)
Field: Chemistry — Organic Chemistry

molify: Molecular Structure Interface The increasing prevalence of Machine-Learned Interatomic Potentials (MLIPs) has shifted requirements for setting up atomistic simulations.Unlike classical force fields, MLIPs primarily require atomic positions and species, thereby removing the need for predefined topology files used for classical force fields in molecular dynamics software like GROMACS (Abraham et al., 2015), LAMMPS (Thompson et al., 2022), ESPResSo (Weik et al., 2019), or OpenMM (Eastman et al., 2024).Consequently, the Atomic Simulation Environment (ASE) (Larsen et al., 2017) has become a popular Python toolkit for handling atomic structures and interfacing with MLIPs, particularly within the material science and soft matter communities.ASE originates from the electronic structure community, which shares the same setup as MLIP-driven studies.In contrast to ab initio, MLIPs are much faster and can run on much larger systems, making high-throughput simulations of more complex systems feasible and increasing the need for efficient initial structure generation.Concurrently, RDKit (Landrum et al., 2023) offers extensive functionality for cheminformatics and manipulating chemical structures.However, standard RDKit workflows are not designed for MLIP-driven simulation, while typical ASE-MLIP workflows may lack rich explicit chemical information such as bond orders or molecular identities, as well as capabilities for generating different conformations or searching substructures. Python (programming language) ; Interfacing ; Workflow ; Cheminformatics ; Molecular dynamics ; Software ; Biomolecular structure ; Interface (matter) Chemistry and Stereochemistry Studies ; Chemical Thermodynamics and Molecular Structure ; Crystal structures of chemical compounds
Authors: Kuswana Kuswana | La Ode M. Firman | Robertus Dhimas Dhewangga Putra | Reza Abdu Rahman
Source: Eurasian journal of physics and functional materials (article, 2025-06-30)
Field: Materials Science — Polymers and Plastics

Preliminary Functionality Assessment on Supercooling Suppression of Binary Polyol Mixture as Energy Material for Thermal System The functionality of the material has valuable aspect for energy utilization, specifically for optimization and renewable technology. Modification from the base material mostly performed to overcome some drawbacks to achieve the technical function, particularly for storing energy material (SEM). This work studies the modification of SEM-TS (thermal system) using polyols (sorbitol/SR and mannitol/MN) as the base SEM. Modification is performed to minimize the supercooling effect for the both polyols, and processed without causing fundamental changes on the structure and chemical composition. The mixture was produced at ratio 25 wt%, 50 wt% and 75 wt% (relative to SR), where the evaluation demonstrated various performance changes. The chemical identification shows the mixture has identic profile with the base, showing no chemical transformation. The diffraction from XRD is also similar for SR and MN, which also confirmed by the thermogravimetric curve which has identic decomposition pattern between the mixture and base material. Key improvement is achieved where the mixture indicates lower supercooling degree (5.1 – 10.8 °C). Also, each mixture indicate solidification transition occurs at longer range, showing the ability to recrystallize during discharge operation. Energy input/output evaluation demonstrate significant change related to temperature increment for heating cycle, giving a clearer indication on the change of melting mechanism of the mixture. It also observed for the heat discharge, showing the mixture has ability to releasing the stored heat in a better manner compared to SR. In general, functionality of the mixture as SEM-TS is more favorable than the base material, potentially unlocking the application of polyol-based SEM-TS in thermal system. Supercooling ; Polyol ; Binary number ; Materials science ; Thermal ; Thermodynamics ; Composite material ; Physics Textile materials and evaluations ; Structural Analysis of Composite Materials ; Polymer composites and self-healing