jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cailum M. K. Stienstra | Christopher R. M. Ryan | Daniel Demczuk | Justine R. Bissonnette | Alief Dwi Arjuna | J. Larry Campbell | W. Scott Hopkins
Source: Analytical Chemistry (article, 2025-04-10)
Field: Chemistry — Spectroscopy

Towards Generalizable <i>In Silico</i> Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering Differential mobility spectrometry (DMS), a tool for separating chemically similar species (including isomers), is readily coupled to mass spectrometry to improve selectivity in analytical workflows. DMS dispersion curves, which describe the dynamic mobility experienced by an ion in a gaseous environment, show the maximum ion transmission for an analyte through the DMS instrument as a function of the separation voltage (SV) and compensation voltage (CV) conditions. To date, there exists no fast, general prediction tool for the dispersion behavior of ions. Here, we demonstrate a machine learning (ML) model that achieves generalized dispersion prediction using an <i>in silico</i> feature addition pipeline. We employ a data set containing 1141 dispersion curve measurements of anions and cations recorded in pure N<sub>2</sub> environments and in N<sub>2</sub> environments doped with 1.5% methanol (MeOH). Our feature addition pipeline can compute 1591 RDKit and Mordred descriptors using only SMILES codes, which are then normalized to sampled molecular distributions (<i>n</i> = 100 000) using cumulative density functions (CDFs). This tool can be thought of as a "learned" feature fingerprint generation pipeline, which could be applied to almost any molecular (bio)cheminformatics tasks. Our best performing model, which for the first time considers solvent-modified environments, has a mean absolute error (MAE) of 2.1 ± 0.2 V for dispersion curve prediction, a significant improvement over the previous state-of-the-art work. We use explainability techniques (<i>e.g.</i>, SHAP analysis) to show that this feature addition pipeline is a semideterministic process for feature sets, and we discuss "best practices" to understand feature sets and maximize model performance. We expect that this tool could be used for prescreening to accelerate or even automate the use of DMS in complex analytical workflows (<i>e.g.</i>, 2D LC×DMS separation) and perform automated identification of transmission windows and increase the "self-driving" potential of the instrument. We make our models available as a free and accessible tool at https://github.com/HopkinsLaboratory/DispersionCurveGUI. Chemistry ; Fingerprint (computing) ; In silico ; Differential (mechanical device) ; Artificial intelligence ; Machine learning ; Aerospace engineering ; Biochemistry ; Computer science Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Chemical Sensor Technologies
Authors: H. Mohit | V.V. Vamsi Krishna | Sanjay Mavinkere Rangappa | Suchart Siengchin | Ibrahim M. Alarifi | Adel Alblawi | Mohammed A. Alamir | Femiana Gapsari
Source: Journal of Materials Research and Technology (article, 2025-07-01)
Field: Materials Science — Polymers and Plastics

Thermal-mechanical characteristics of groundnut shell/ ATH/ Si/ SiC ceramic particulates reinforced glass fiber-epoxy hybrid composites The present investigation demonstrates the influences of reinforcing groundnut shell and inorganic fillers (aluminum trihydrate, silicon and silicon carbide) on the characteristics of glass fabric-reinforced epoxy laminates. The laminates were manufactured using wet layup method and characterized for mechanical, physical and thermal characteristics. The reinforcement of fillers enhanced tensile strength, flexural strength, and impact strength by 70.89%, 62.78%, and 232.21%, respectively when compared with standard sample (GE). Thermogravimetric and differential scanning calorimeter analysis showed improved thermal stability with higher onset temperature. Fourier transform infrared spectra affirmed the existence of functional groups from the reinforced fillers. An artificial neural network model was generated to predict composite characteristics, attaining higher precision with R-squared values around 1.0 and select the best composite material. Statistical evaluation using one-way ANOVA affirmed significant deviation in characteristics between the laminates. The investigation illustrates the capability of natural and inorganic filler blends to improve the effectiveness of epoxy laminates for cutting-edge applications. Materials science ; Composite material ; Epoxy ; Ceramic ; Silicon carbide ; Shell (structure) ; Glass fiber ; Fiber ; Thermal ; Composite number ; Particulates Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Cellular and Composite Structures
Authors: S Chellaiya | Thomas Rueshwin | R.D. Eithiraj
Source: Scientific Reports (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Cationic substitution, dynamical stability, thermal stability, electronic and thermoelectric properties in 2D dialkali metal monoxides via DFT and ML approach Using the WIEN2k software, Density Functional Theory (DFT) was applied to analyse the impact of cationic substitution on the physical features of the 1T-K<sub>2</sub>O monolayer. Phonon dispersion analysis confirmed dynamical stability, Ab-initio Molecular Dynamics (AIMD) simulations validated thermal stability, and cohesive energy calculations ensured thermodynamic stability of 1T-K<sub>2</sub>O. Based on the phonon studies, both 1T-KNaO and 1T-KRbO are dynamically unstable with a slightly visible imaginary frequency. Specifically, for electronic property assessment, generalized gradient approximation (GGA) and hybrid exchange-correlation functionals were utilized. This study unveiled the 1T-KXO (X = Na, K, Rb) monolayers as an indirect band gap semiconductor, for 1T-K<sub>2</sub>O, 1T-KNaO and 1T-KRbO were 0.94 eV (1.84 eV), 1.03 eV (1.94 eV) and 0.84 eV (1.77 eV) obtained implementing GGA and hybrid functionals, respectively. Using a machine learning (ML) approach, the band gap was predicted as 1.45 eV (0.85 eV) for 1T-K<sub>2</sub>O, 1.79 eV (0.97 eV) for 1T-KNaO, and 1.17 eV (0.72 eV) for 1T-KRbO, with random forest regression (linear regression) method. The physical properties were tailored by the impact of cationic substitution on the 1T-K<sub>2</sub>O were studied. The variation in the physical properties were investigated. Optical analysis indicated a strong absorption coefficient, underscoring the 1T-KXO monolayers potential for photovoltaic applications in the UV region. The ZT value obtained at room temperature are 0.58, 0.86 and 0.69 for 1T-K<sub>2</sub>O, 1T-KNaO and 1TKRbO, respectively. Additionally, the 1T-KNaO demonstrated promising thermoelectric properties, at 400 K achieving a figure of merit (ZT) of 0.93, indicating its suitability for waste heat recovery. A ML model was trained to predict the ZT of 1T-KXO using random forest regression and linear regression. Density functional theory ; Band gap ; Phonon ; Materials science ; Monolayer ; WIEN2k ; Ab initio ; Thermoelectric effect ; Computational chemistry ; Thermodynamics ; Chemistry ; Condensed matter physics ; Physics ; Local-density approximation ; Nanotechnology ; Optoelectronics ; Quantum mechanics 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Zhihao Gao | Yintao Zhou
Source: Micron (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Automated grain analysis via data augmentation and grain boundary detection Pipeline (software) ; Probabilistic logic ; Key (lock) ; Artificial neural network ; Translation (biology) ; Grain boundary ; Pattern recognition (psychology) ; Metallography Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Siyuan Liu | Yanhua Zhang | Yi‐Jun Wu | Zheng Ji | Ruitao Zhang | Dan Zhang | Mei Ma | Min Li | Jiang Wu | Ling Yang
Source: Journal of the Energy Institute (review, 2025-06-14)
Field: Materials Science — Materials Chemistry

Layered transition metal chalcogenides for the removal of elemental mercury from flue gas: a review Flue gas ; Elemental mercury ; Mercury (programming language) ; Transition metal ; Waste management ; Environmental chemistry ; Environmental science ; Chemistry ; Process engineering ; Materials science ; Nuclear engineering ; Inorganic chemistry ; Engineering ; Computer science ; Organic chemistry ; Catalysis 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Mercury impact and mitigation studies
Authors: Giuseppe Bonifazi | Sergio Bellagamba | Giuseppe Capobianco | Riccardo Gasbarrone | Ivano Lonigro | Sergio Malinconico | Federica Paglietti | Silvia Serranti
Source: Sustainability (article, 2025-01-24)
Field: Chemistry — Analytical Chemistry

Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (RecallM and AccuracyM equal to 1.00), followed by PCA-KNN (RecallM of 0.98–1.00 and AccuracyM equal to 1.00). Poorer performances were obtained by PLS-DA (RecallM of 0.68–0.72 and AccuracyM equal to 0.95) and PCA-DA (RecallM of 0.66–0.70 and AccuracyM equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies. Hazardous waste ; Mineral ; Infrared ; Spectroscopy ; Infrared spectroscopy ; Mineral exploration ; Environmental science ; Materials science ; Geology ; Mining engineering ; Engineering ; Chemistry ; Waste management ; Optics ; Physics ; Metallurgy ; Organic chemistry Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Geochemistry and Geologic Mapping
Authors: DEMET SARIYER
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Hybrid Machine Learning-Monte Carlo Approach for Neutron Shielding in High-Energy Proton Facilities Electromagnetic shielding ; Monte Carlo method ; Neutron ; Computation ; Proton ; Linear particle accelerator ; Nuclear data ; Neutron transport ; Gradient boosting Radiation Shielding Materials Analysis ; Radiation Therapy and Dosimetry ; Nuclear reactor physics and engineering
Authors: Yi-fan Wang | Chun‐lai Ren | Yu-qiang Ma
Source: Physical review. E (article, 2025-06-30)
Field: Chemistry — Spectroscopy

Integrating coarse-grained simulations and machine learning to uncover protein aging mechanisms Protein condensate aging is a widespread phenomenon, yet quantitative insights into its molecular mechanisms remain rare. Here, we employ the sticker-spacer model and integrate coarse-grained lattice simulations with machine learning to investigate the liquid-to-solid transition in protein condensates. The transition arises from the intricate interplay of heterotypic interactions among stickers within proteins. By quantitatively distinguishing inhomogeneous solid-like and liquid-like structures, we confirm the formation of a solid-like shell at the condensate interface. Proteins at the interface, characterized by extended conformations and orientations, promote the formation of solid-like structures via the aggregation of aging stickers. Notably, variations in the number, location, and intensity of aging stickers significantly influence the structural and viscoelastic properties of the condensates, mediated by the balance between competition and cooperation in heterotypic interactions. Our findings provide a quantitative framework for understanding condensate aging and suggest potential strategies to regulate aging process. Computer science ; Artificial intelligence ; Machine learning Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Vladimir Ladygin | C. N. Saunders | C. M. Bernal-Choban | D. L. Abernathy | Michael E. Manley | Brent Fultz
Source: Physical Review Materials (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Phonon second harmonic generation in NaBr studied by inelastic neutron scattering and computer simulation The phenomenon of second harmonic generation (SHG) was found for phonons in anharmonic NaBr by inelastic neutron scattering. The temperature dependence of this phonon SHG was measured from 300 K to 650 K. At 300 K the second harmonic (SH) is seen as a high-energy branch around 33 meV, nearly independent of $\stackrel{P\vec}{Q}$. The temperature effective potential (TDEP) method and classical molecular dynamics (MD) simulation with machine learning interatomic potential were able to reproduce the SH, and showed that SHG occurs with the flat transverse optical (TO) phonon branch. A classical model of a nonlinear medium explains the intensity and lifetime of the SH, compared to those of the TO modes. Also successful was a quantum model based on the Heisenberg-Langevin equation for interacting phonons coupled to a thermal bath, which also predicts a spectral distribution of the SH. The measured temperature dependence of the intensity of the second harmonic showed that it follows the Planck distribution of a one-phonon quasiparticle, and not two TO phonons. Materials science ; Inelastic neutron scattering ; Phonon ; Neutron ; Inelastic scattering ; Neutron scattering ; Quasielastic neutron scattering ; Harmonic ; Condensed matter physics ; Small-angle neutron scattering ; Scattering ; Atomic physics ; Nuclear physics ; Optics ; Physics ; Quantum mechanics Thermal properties of materials ; Solid-state spectroscopy and crystallography ; Acoustic Wave Resonator Technologies
Authors: Tyra C. Douglas | Michael J. Dzara | Andrew J. E. Rowberg | Keith A. King | Maria Syrigou | Nicholas A. Strange | Robert Bell | Anuj Goyal | Pin-Wen Guan | Robert B. Wexler | Joel B. Varley | Tadashi Ogitsu | Stephan Lany | Anthony H. McDaniel | Sean R. Bishop | Matthew Witman
Source: Materials Horizons (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H<sub>2</sub>, but state-of-the-art (SOTA) CeO<sub>2</sub> has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr<sub>3</sub>PrMn<sub>2</sub>O<sub>8</sub> and Ba<sub>2</sub>Fe<sub>2</sub>O<sub>5</sub>, display hydrogen yields greater than CeO<sub>2</sub> under specific redox conditions. These results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO<sub>2</sub>. Redox ; False positive paradox ; Graph ; Hydrogen ; Vacancy defect ; Artificial neural network ; Hydrogen production Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Norlaylawate Mohd Kama | Noor Fazrieyana Hamidon | Hayati Mukhair | Hayyiratul Fatimah Mohd Zaid | Yeek‐Chia Ho | Marzhan S. Kalmakhanova | Amun Amri | Hamza Ahmad Isiyaka | Khairulazhar Jumbri
Source: Journal of Solid State Chemistry (article, 2025-07-16)
Field: Chemistry — Inorganic Chemistry

UiO-66(Zr) for herbicides Adsorption: Integrating experimental nanoarchitectonics and computational approaches Adsorption ; Chemistry ; Materials science ; Nanotechnology ; Physical chemistry Radioactive element chemistry and processing ; Adsorption and biosorption for pollutant removal ; Analytical chemistry methods development
Authors: Wiebke Zielasko | Rafael Bonacin de Oliveira | Edgar Dutra Zanotto | Michael Ryan Hansen | Hellmut Eckert | Henrik Bradtmüller
Source: Acta Materialia (article, 2025-07-15)
Field: Materials Science — Ceramics and Composites

Contrasting structural roles of Nb2O5 vs. La2O3 in silicate glasses: a quantitative solid-state NMR study Materials science ; Silicate ; Solid-state nuclear magnetic resonance ; Solid-state ; Chemical engineering ; Mineralogy ; Crystallography ; Physical chemistry ; Nuclear magnetic resonance Glass properties and applications ; Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography
Authors: Kayeen Vadakkan | Sreeshna Karippali
Source: Next Nanotechnology (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Carbon-based quantum dots in oncology: Properties and applications in cancer therapy and diagnosis Carbon dots (CDs) are emerging as transformative nanomaterials in the field of cancer diagnostics due to their unique photoluminescence, high biocompatibility, aqueous solubility, and facile surface functionalization. This review presents a comprehensive synthesis of recent advancements in the application of multifunctional CDs for cancer diagnosis. Emphasis is placed on their potential as optical probes for non-invasive bioimaging, fluorescence-guided tumor identification, and early-stage cancer detection. The photophysical properties of CDs- including excitation-dependent emission and size-tunable fluorescence enable high-contrast imaging with reduced photobleaching, which is critical for real-time monitoring of tumor microenvironments. Additionally, CDs offer significant promise in biosensing platforms for detecting cancer biomarkers such as nucleic acids, proteins, and specific antigens with high sensitivity and specificity. Their functionalized surfaces facilitate conjugation with targeting ligands, enabling selective cellular uptake and tumor localization. This review also highlights the advantages of CDs over conventional contrast agents, including reduced toxicity, deeper tissue penetration in the near-infrared range, and the capability for multimodal imaging. Key synthetic routes are compared in terms of yield, scalability, and control over optical properties. Despite these advantages, translational challenges remain, particularly in terms of standardizing synthesis, ensuring reproducibility, and evaluating long-term biocompatibility. Overall, CDs represent a promising frontier in precision diagnostics, offering the potential for integration into next-generation diagnostic nanoplatforms that aim for early, accurate, and minimally invasive cancer detection. • CDs enable customizable fluorescence for cancer imaging and early detection. • CDs are suitable for biological applications due to their biocompatibility and low toxicity levels. • Functionalized CDs allow for controlled drug delivery and tumor identification. • CDs act as both photothermal and photodynamic agents in cancer therapy. • Machine learning improves CD synthesis for precise medicinal applications. Cancer therapy ; Quantum dot ; Cancer imaging ; Biocompatibility ; Cancer ; Deoxyribozyme ; Cancer biomarkers ; Nanomaterials ; Surface modification Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Feng Wang | Ning Wang | Lipeng Zhong | She Chen | Qiuqin Sun | Xutao Han | Puming Xu | Sanwei Liu | Tao Peng | Ying Yan | Xiao Deng
Source: IET Science Measurement & Technology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research on Recognition of Multiple Partial Discharge Sources in Switchgear Based on the Combination of GST‐TEV and ResNet‐18 ABSTRACT Switchgear can develop insulation defects due to electrical, thermal, and chemical stresses during manufacturing or prolonged operation. Moreover, as voltage levels rise, multiple insulation defects can coexist within the switchgear. Traditional partial discharge (PD) recognition methods often suffer from poor generalisation and low accuracy, limiting their practical applications. This paper proposes a method to identify multiple PD sources by combining generalised S‐transform (GST) with the ResNet‐18 network. PD tests confirm that the designed monitoring device effectively detects transient earth voltage (TEV) signals from diverse single and mixed insulation defects. Given the non‐stationary nature of TEV signals, this paper employs the generalised S‐transform (GST) for time–frequency analysis. The findings demonstrate that the GST method offers high time–frequency resolution, significantly improving the feature extraction of various partial discharge sources. Additionally, deep learning algorithms are employed to classify the time–frequency image dataset derived from GST‐TEV. The results demonstrate that, compared to traditional manual feature extraction methods, the ResNet‐18 network efficiently extracts GST‐TEV features from both single and mixed partial discharge sources, achieving a recognition accuracy of 99.41%. This study provides new methods and theoretical support for identifying multiple partial discharge sources in switchgear. Switchgear ; Partial discharge ; Residual neural network ; Pattern recognition (psychology) ; Computer science ; Environmental science ; Artificial intelligence ; Engineering ; Electrical engineering ; Artificial neural network ; Voltage High voltage insulation and dielectric phenomena ; Advanced Sensor and Control Systems ; Power Transformer Diagnostics and Insulation
Authors: S. Braroo | Xingsheng Sun | K.T. Ramesh
Source: Mechanics of Materials (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

An uncertainty quantification guided approach to modeling high-velocity impact into advanced ceramics Materials science ; Ceramic ; Uncertainty quantification ; Mechanics ; Composite material ; Mathematics ; Statistics ; Physics High-Velocity Impact and Material Behavior ; Geophysical Methods and Applications ; Probabilistic and Robust Engineering Design