jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haiyan Zhu | Hongyu Yu | Weiqin Zhu | Guoliang Yu | Changsong Xu | Hongjun Xiang
Source: Physical Review Letters (article, 2025-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Moiré-Induced Magnetoelectricity in Twisted Bilayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>NiI</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> Twisted magnetic van der Waals materials offer a promising route for multiferroic engineering, yet modeling large-scale moiré superlattices remains challenging. Leveraging a newly developed SpinGNN++ framework that effectively handles spin-lattice coupled systems, we develop a comprehensive interatomic machine learning potential and apply it to twisted bilayer NiI_{2}. Structural relaxation introduces moiré-periodic "bumps" that modulate the interlayer spacing by about 0.55 Å and in-plane ionic shifts up to 0.48 Å. Concurrently, our machine learning potential, which faithfully captures all key spin interactions, produces reliable magnetic configurations; combined with the more accurate generalized Katsura-Nagaosa-Balatsky mechanism, it delivers precise spin-driven polarization. For twist angles 1.89°≤θ≤2.45°, both mechanisms become prominent, yielding rich polarization textures that combine ionic out-of-plane dipoles with purely electronic in-plane domains. In the rigid (unrelaxed) bilayer, skyrmions are absent; lattice relaxation is thus essential for generating polar-magnetic topologies. In contrast, near θ≈60°, stacking-dependent ferroelectric displacements dominate, giving rise to polar meron-antimeron networks. These results reveal cooperative ionic and spin-driven ferroelectricity in twisted bilayer NiI_{2}, positioning twisted van der Waals magnets as adaptable platforms for tunable multiferroic devices. Ferroelectricity ; van der Waals force ; Multiferroics ; Dipole ; Bilayer ; Ionic bonding ; Skyrmion ; Polarization (electrochemistry) Multiferroics and related materials ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Jonathan Rains | Elliot Steeples | Ben Robinson | Andrew J. Pierce | Danielle Sayer
Source: Analytical Chemistry (article, 2025-06-05)
Field: Chemistry — Spectroscopy

High-Throughput HPLC Method for the Measurement of Octanol–Water Partition Coefficients without an Organic Modifier We present a high-performance chromatographic method for the determination of octanol-water partition coefficients of organic molecules, suitable for pharmaceuticals, agrochemicals, and fine-chemicals featuring acidic and basic p<i>K</i><sub>a</sub> values. By selecting short-alkyl-columns based on length and coating them with octanol present in the mobile phase, we can measure a log<i>P</i> range of 0-5.6 without the use of an organic additive. The method has shown to be accurate for a set of 146 commercial molecules reported here, as well as over 27,000 in-house proprietary research molecules, the results of which have been used to train our own machine learning model. The method is also insensitive to analyte concentration or purity, and can be partially automated to obtain a relatively high throughput for industrial purposes. Chemistry ; Partition coefficient ; High-performance liquid chromatography ; Chromatography ; Octanol ; Partition (number theory) ; Throughput Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Chemical and Physical Properties in Aqueous Solutions
Authors: Jibrin Muhammad Yelwa | Alhagie Drammeh | Haruna Musa | Opeoluwa O. Fasanya | Kalu M. Kalu | Shuaibu Abdullahi | Gueny Amirou Fall | Bashir M. Aliyu
Source: Academia green energy. (article, 2025-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced material innovations for self-healing high-performance supercapacitors Self-healing high-performance supercapacitors represent the state-of-the-art in energy storage development, which combines long-term durability and efficiency with the self-repair functionality after mechanical or other damages. In this review, a discussion about the very basis of supercapacitor technology is presented by studying the mechanisms and materials used for making supercapacitors self-healable, among other things, based on redox-active materials. This review further examines the performance analysis, applications, challenges, and prospects of self-healing supercapacitors used in consumer electronics, electric vehicles, and renewable energy systems. While these supercapacitors may be very promising for extending device lifetimes and reducing maintenance costs, challenges remain for matching the efficiency of self-healing with electrochemical performance, scalability, economic viability, and environmental sustainability. Some of the identified future research directions entail the inclusion of cost-effectiveness in materials, their integration with multifunctional and hybrid systems, and the use of advanced technologies such as machine learning to accelerate material discovery. In overcoming these challenges, self-healing supercapacitors could inspire new frontiers of energy storage applications by achieving sustainability and resiliency in next-generation technologies. Supercapacitor ; Self-healing ; Materials science ; Medicine ; Capacitance ; Physics ; Alternative medicine Supercapacitor Materials and Fabrication ; Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications
Authors: Pravin D. Patil | Meghna Suvarna | Niharika Gargate | Aruja Tiwary | Dhristi Palresha | Manishkumar S. Tiwari | Sneha Kagale | Vivek P. Bhange | Shamraja S. Nadar
Source: Journal of Industrial and Engineering Chemistry (article, 2025-06-07)
Field: Chemistry — Inorganic Chemistry

Recent advances in carbonous metal–organic frameworks (carbon-MOFs): Synthesis and environmental application Metal-organic framework ; Carbon fibers ; Nanotechnology ; Environmental chemistry ; Environmental science ; Chemistry ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Jian Sun | Junmeng Luo | Ming Gao | Fang Wang | Wenjing Nie | Moran Chen | Suming Chen
Source: Angewandte Chemie International Edition (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Quantum Chemistry Calculation‐Assisted Large‐Scale Collision Cross Section Prediction Empowers Derivatization‐Enhanced Multidimensional Metabolomics Derivatization-enhanced multidimensional metabolomics combined with ion mobility mass spectrometry will greatly improve the accuracy and coverage of metabolic analysis. However, accurate prediction of the large-scale collision cross section (CCS) of derivatized metabolites without relying on standards and the establishment of multidimensional analytical methods faces great challenges. Here, we propose quantum chemistry calculation-assisted machine learning strategies applicable to the accurate prediction of the CCS of derivatized sterols, develop C═C bond-targeted N-Me derivatization methods for unsaturated sterols, and create a large-scale, 4D information database of derivatized sterol lipids (n = 4891) by combining retention time and fragment ion prediction. Furthermore, a high-coverage unsaturated sterolomics at the isomer level was established on this basis, which quantitatively revealed the tissue-specific distribution patterns of over 100 sterol lipids. This study provides a key foundation for derivatization-enhanced metabolomics and provides important techniques and information for metabolic and functional studies of sterols. Derivatization ; Metabolomics ; Chemistry ; Mass spectrometry ; Scale (ratio) ; Ion-mobility spectrometry ; Sterol ; Computer science ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Yuling Luo | Li Xie | Dan Cai | Lixian Sun | Fen Xu | Hao Feng | Haibin Jia
Source: International Journal of Hydrogen Energy (article, 2025-07-15)
Field: Chemistry — Inorganic Chemistry

FusionStackBoost: A machine learning approach for accurate prediction of hydrogen storage performance in metal organic frameworks Hydrogen storage ; Computer science ; Metal-organic framework ; Hydrogen ; Machine learning ; Artificial intelligence ; Process engineering ; Chemistry ; Physical chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Yupei Wei | Juan Guo | Sun Yu | Hong Wang | Xiaohan Li | Xiaomei Jiang | Tuo He | Yafang Yin
Source: Microchemical Journal (article, 2025-06-18)
Field: Chemistry — Organic Chemistry

Breaking the barrier to rapid and non-destructive species-level wood identification using spectroscopy Identification (biology) ; Spectroscopy ; Materials science ; Environmental science ; Environmental chemistry ; Chemistry ; Physics ; Ecology ; Biology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Identification and Quantification in Food
Authors: Yunying Wang | Yun Liu
Source: Microchemical Journal (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Oxidase-mimicking nanozymes: Bridging advanced materials with smart biosensors Transformative learning ; Bridging (networking) ; Wearable computer ; Human health ; Emerging technologies ; Wearable technology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Bohong Zhang | Anand K. Nambisan | Abhishek Prakash Hungund | Xavier Jones | Qingbo Yang | Jie Huang
Source: RSC Advances (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Stochastic generalization models learn to comprehensively detect volatile organic compounds associated with foodborne pathogens <i>via</i> Raman spectroscopy Ensuring food safety requires continuous innovation, especially in the detection of foodborne pathogens and chemical contaminants. In this study, we present a system that combines Raman spectroscopy with machine learning (ML) algorithms for the precise detection and analysis of VOCs linked to foodborne pathogens in complex liquid mixtures. A remote fiber-optic Raman probe was developed to collect spectral data from 42 distinct VOC mixtures, representing contamination scenarios with dilution levels ranging from undiluted to highly diluted states. A dataset comprising 1445 Raman spectra was analyzed using classification and regression ML models, including multi-layer perceptron (MLP), random forest, and extreme gradient boosting decision trees (XGBDT). The optimized ML models achieved over 90% classification accuracy for pure VOCs and demonstrated robust performance in identifying mixtures containing up to six VOCs at concentrations as low as 0.25% (400-fold dilution). Additionally, regression analysis effectively predicted VOC concentrations at levels as low as 1% (100-fold dilution), with the best model achieving an <i>R</i> <sup>2</sup> value exceeding 0.82. This approach demonstrates the potential for rapid and real-time food safety monitoring, effectively overcoming the limitations of traditional methods such as culture-based or qPCR techniques, while its ability to reliably classify complex VOC mixtures makes it a valuable tool for on-site food safety assessments and quality control applications across various industries. Generalization ; Raman spectroscopy ; Food safety ; Contamination ; Chemistry ; Biochemical engineering ; Food science ; Environmental science ; Environmental chemistry ; Biology ; Physics ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alberto Pacini | Mauro Ferrario | Maria Clelia Righi
Source: Journal of Chemical Theory and Computation (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling Machine Learning Interatomic Potentials (MLIPs) offer a powerful way to overcome the limitations of <i>ab initio</i> and classical molecular dynamics simulations. However, a major challenge is the generation of high-quality training data sets, which typically require extensive <i>ab initio</i> calculations and intensive user intervention. Here, we introduce Strategic Configuration Sampling (SCS), an active learning framework to construct compact and comprehensive data sets for MLIP training. SCS introduces the usage of <i>workflows for the automated generation and exploration of systems</i>, collections of MD simulations where geometries and run conditions are set up automatically based on high-level, user defined inputs. To explore nontrivial atomic environments, initial geometries can be assembled dynamically via <i>collaging</i> of structures harvested from preceding runs. Multiple <i>automated exploration workflows</i> can be run in parallel, each with its own resource budget according to the computational complexity of each system. Besides leveraging the MLIP models trained iteratively, SCS also incorporates pretrained models to steer the exploration MD, thereby eliminating the need for an initial data set. By integrating widely used software, SCS provides a fully open-source, automatic, active learning framework for the generation of data sets in a high-throughput fashion. Case studies demonstrate its versatility and effectiveness to accelerate the deployment of MLIP in diverse materials science applications. Computer science ; Sampling (signal processing) ; Training set ; Training (meteorology) ; Set (abstract data type) ; Population ; Machine learning ; Data set ; Artificial intelligence ; Data mining ; Computer vision Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Fahimeh Ahmadi | Mohsen Hajihassani | Stefanos Papanikolaou | Panagiotis G. Asteris
Source: Ceramics International (article, 2025-01-11)
Field: Materials Science — Ceramics and Composites

Data-driven optimized XGBoost and LightGBM for predicting the Judd-Ofelt parameters in rare-earth doped phosphate-based glasses Materials science ; Doping ; Rare earth ; Phosphate ; Mineralogy ; Analytical Chemistry (journal) ; Optics ; Optoelectronics ; Environmental chemistry ; Physics ; Metallurgy ; Geology ; Chemistry Glass properties and applications ; Luminescence Properties of Advanced Materials ; Transition Metal Oxide Nanomaterials
Authors: Yasemin Şi̇mşek Türker | Şemsettin KILINÇARSLAN | Mehmet Avcar
Source: Composite Structures (article, 2025-02-20)
Field: Materials Science — Polymers and Plastics

Mechanical behaviors of glulam beam-to-column joints strengthened with fiber-reinforced polymer composites: Experimental investigation, numerical analysis and ANN prediction model Materials science ; Composite material ; Beam (structure) ; Fiber ; Structural engineering ; Engineering Natural Fiber Reinforced Composites ; Advanced machining processes and optimization ; Wood Treatment and Properties
Authors: Maimuna U. Zarewa | Tawfik A. Saleh
Source: ACS Sustainable Resource Management (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Refinery-Waste-Derived Carbon Quantum Dots for Selective Metal Ion Fluorescence Sensing: Experimental, DFT, and Deep Learning Insights In this study, we present a sustainable approach for synthesizing carbon quantum dots (CQDs) from petroleum refinery waste, transforming industrial residues into high-performance optical nanomaterials. Three different CQDs samples (QVR1, QVR2, and QVR3) were synthesized and extensively characterized using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Optical properties, assessed via UV–vis absorption and photoluminescence (PL), revealed strong and selective fluorescence enhancement in response to 30 environmentally relevant metal cations and anions. Specifically, QVR1, QVR2, and QVR3 exhibited selective and rapid interactions with Na+ (half-life = 11.33 s), Cd2+ (t1/2 = 7.38 s), and K+ (t1/2 = 17.32 s), respectively. Simplified Molecular Input Line Entry System (SMILES) was extracted using density functional theory (DFT) by RDKit and Open Babel, and combined with experimental PL data and binding energy values in a deep learning framework. The Graph Convolutional Network (GCN) model demonstrated superior predictive performance over ChemBERT, achieving an overall accuracy of 83.3% and a 42.9% recall for minority classes, compared to ChemBERT’s 80.6% accuracy and 0% recall for minority classes. This work highlights the synergistic use of experimental, theoretical, and AI-driven cheminformatics approaches for understanding nanoscale sensing behaviors and establishes refinery-derived CQDs as promising low-cost materials for selective and sensitive optical sensing. Photoluminescence ; Quantum dot ; Fluorescence ; X-ray photoelectron spectroscopy ; Spectroscopy ; Density functional theory ; Convolutional neural network ; Fourier transform infrared spectroscopy ; Hyperspectral imaging Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Pol Febrer | Peter Bjørn Jørgensen | Miguel Pruneda | Alberto Garcı́a | Pablo Ordejón | Arghya Bhowmik
Source: Machine Learning Science and Technology (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Graph2Mat: universal graph to matrix conversion for electron density prediction Abstract The electron density is a fundamental observable of an atomic system from which all ground-state properties can be computed. As a prediction target for machine learning (ML) models, electron density is often represented on a dense real space grid, which is data heavy, or through density fitting approximations. In this work, we show the power of targeting the density matrix (DM), a linear-scaling sparse SE(3) equivariant matrix that encodes the exact density. We introduce Graph2Mat, a universal function for converting molecular graphs into equivariant matrices. We demonstrate how a ML model that combines this Graph2Mat approach with state-of-the-art molecular graph representations can accurately predict the DM of molecular systems. The models achieve state-of-the-art performance on electron density prediction by matching the accuracy of grid-based methods, while using datasets that are at least one order of magnitude smaller. Accurately predicted electron densities can also accelerate density functional theory (DFT) calculations by reducing the number of self-consistent field (SCF) iterations needed to converge. In this work, we get an average 40% reduction on the number of SCF steps in DFT calculations of QM9 molecules with SIESTA. The novel prediction model also allows for two new and promising measures of uncertainty (total charge error and self-consistency error) that will facilitate its practical usage, e.g. within active learning workflows. These results open the door for many applications using hybrid ML-accelerated DFT methodologies, and uncertainty aware single iteration ab initio molecular dynamics. Graph ; Electron ; Computer science ; Statistical physics ; Physics ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Wang Ming | Yinan Hu | Haoyu Liu | Yucheng Li | Jianchao Xie | Yuping Huang | Jinchao Tong | Qinghua Yang | Dong Hu
Source: Food Control (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

A review of integrating sphere techniques for measuring optical absorption and scattering in agricultural products Integrating sphere ; Key (lock) ; Field (mathematics) ; Modalities ; Agriculture ; Absorption (acoustics) ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Surface Roughness and Optical Measurements ; Advanced Fiber Optic Sensors
Authors: Budi Kurniawan | Fiky Yosef Suratman | Fani Fauziah | Hurianti Vidyaningtyas | Desti Madya Saputri | Aloysius Adya Pramudita
Source: Emerging Science Journal (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Empoasca Pest Attack Classification on Tea Plantations Using Multispectral Imaging and Deep Learning This study aims to enhance the management of Empoasca pests in tea cultivation, a critical sector for Indonesia’s economy, by developing an innovative detection method. The challenge of pest infestations may significantly reduce tea production yields, and the misuse of chemical pesticides further compromises tea quality. We propose a novel approach that integrates multispectral imaging with Convolutional Neural Networks (CNN), specifically employing ResNet-50 and AlexNet architectures to accurately detect Empoasca infestations. We begin with the data collection process, followed by the development of the preprocessing model and evaluation of its performance. We classify tea leaves affected by Empoasca pests using spectral data obtained from a multispectral camera operating across Green, NIR (Near Infrared), REG (Red Edge), and RED channels. We evaluated various spectral channels and identified the green spectrum as the most effective for revealing visual characteristics, such as curled leaves associated with Empoasca damage. Experimental results demonstrated that ResNet-50 outperformed AlexNet, achieving a remarkable accuracy of 99% on the green channel, while AlexNet showed notable accuracy declines on other channel combinations. These findings underscore the effectiveness of the green spectrum and the superiority of ResNet-50 in achieving precise pest detection, offering a reliable technological solution for modern tea plantation management. PEST analysis ; Multispectral image ; Agroforestry ; Environmental science ; Remote sensing ; Artificial intelligence ; Geography ; Computer science ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food