jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changmin Guo | Hong Zhang | Xinlu Cheng
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Vibrational excitation and dissociation processes in high-temperature N&lt;sub&gt;2&lt;/sub&gt;-O&lt;sub&gt;2&lt;/sub&gt; state-to-state collisions based on neural network and dynamic simulation The scattering cross-sections and reaction rate coefficients are crucial parameters for elucidating the energy transfer mechanism of state-to-state collisions between molecular gases and also serve as a fundamental basis for modeling the non-equilibrium flow field. However, the database of kinetic processes related to nitrogen shock flows is still being developed. In this work, a detailed kinetic study of the N&lt;sub&gt;2 &lt;/sub&gt;+ O&lt;sub&gt;2&lt;/sub&gt; collision is carried out by combining the quasi-classical trajectory method (QCT) and neural network model (NN). Firstly, QCT is used to calculate 90 N&lt;sub&gt;2&lt;/sub&gt;(&lt;i&gt;v&lt;/i&gt;) + O&lt;sub&gt;2&lt;/sub&gt;(&lt;i&gt;w&lt;/i&gt;) processes with various initial vibrational states (&lt;i&gt;v&lt;/i&gt;,&lt;i&gt;w&lt;/i&gt;), and the contributions of all vibrational excitation and dissociation reaction channels are discussed. The following conclusions are drawn: 1) The contributions of the vibration-vibration (VV) energy exchange channel of O&lt;sub&gt;2&lt;/sub&gt; and N&lt;sub&gt;2&lt;/sub&gt; are similar, while the vibration-translational (VT) transition mainly occurs on O&lt;sub&gt;2&lt;/sub&gt;; 2) The total dissociation cross-section primarily results from the O&lt;sub&gt;2&lt;/sub&gt; single-dissociation channel, followed by the exchange-dissociation channel, with relatively minor contributions from the N&lt;sub&gt;2&lt;/sub&gt; single- and double-dissociation channels. Then, based on the QCT dataset, a high-performance NN model (&lt;i&gt;R&lt;/i&gt;-value of 0.99) is trained to predict the total dissociation cross-section caused by N&lt;sub&gt;2&lt;/sub&gt;(&lt;i&gt;v&lt;/i&gt;) + O&lt;sub&gt;2&lt;/sub&gt;(&lt;i&gt;w&lt;/i&gt;) collisions. Compared with the method that only uses QCT, the method that jointly uses OCT and NN model can achieve an approximately 91.94% reduction in computational cost. Finally, to facilitate use in kinetic modeling, Arrhenius-type fits for the VV/VT rate coefficients are provided over the temperature range of 5000–30000 K, and an exponential form related to the translational energy &lt;i&gt;E&lt;/i&gt;&lt;sub&gt;t&lt;/sub&gt; is used to fit the total dissociation cross-section. Dissociation (chemistry) ; Excitation ; Atomic physics ; State (computer science) ; Chemistry ; Physics ; Physical chemistry ; Mathematics ; Quantum mechanics ; Algorithm Spectroscopy and Laser Applications
Authors: Majharulislam Babor | Shanghua Liu | Arman Arefi | Agata Olszewska‐Widdrat | Joachim Venus | Barbara Sturm | Marina Höhne
Source: Results in Engineering (article, 2025-12-03)
Field: Chemistry — Analytical Chemistry

Interpretable domain adaptation enables robust lactic acid fermentation monitoring from waste • Transferring predictive models without chemical analysis from the new environment. • Addressing FTIR shifts using DANN-R, DeepCORAL-R, DARE-GRAM, and TCA methods. • Improving transparency and reliability in glucose and lactic acid predictions. • Enhancing monitoring accuracy with ShapDA by identifying domain-invariant features. Lactic acid is a key compound in the circular bioeconomy, supporting the production of biodegradable plastics, pharmaceuticals, and food products. To optimize its production, real-time fermentation monitoring is essential, particularly when using heterogeneous substrates such as agricultural residues or municipal bio-waste. While manual sampling and offline chemical analysis are impractical at scale, machine learning models coupled with spectroscopic sensors offer a promising alternative. However, they often fail to generalize across varying substrates (e.g., glucose and bio-waste) due to data distribution shifts, limiting industrial deployment. To address this, we present Shapley Additive exPlanations-based Domain Adaptation (ShapDA), an unsupervised domain adaptation framework that integrates model explainability with Fourier transform infrared spectroscopy. Unlike conventional domain adaptation methods that align statistical distributions or complex latent representations, ShapDA identifies a compact set of spectral features that remain stable across substrate domains. These domain-invariant features capture variable-specific spectral signatures, enabling calibration-free prediction under novel conditions. We apply ShapDA to 21 glucose-based fermentation batches, each consisting of spectra and their corresponding chemical measurements. We then transfer it to 83 batches of bio-waste fermentation-spanning diverse substrates, production scales, and microbial strains-without additional chemical measurements. ShapDA outperforms baseline and state-of-the-art methods, reducing the root mean squared error by 50 % for glucose and 65 % for lactic acid in bio-waste fermentations. These results demonstrate that ShapDA is a scalable solution for substrate-independent monitoring across diverse production environments. Our code is available at https://github.com/shl-shawn/ShapDA . Lactic acid ; Domain adaptation ; Fermentation ; Adaptation (eye) ; Reliability (semiconductor) ; Domain (mathematical analysis) ; Fourier transform ; Flexibility (engineering) Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Fault Detection and Control Systems
Authors: Xiao Wei Sun | Jun-Li Cao | Xin-Xuan Wang | Lulu Wei | Ting Song | Zi‐Jiang Liu
Source: Physical review. B./Physical review. B (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Investigating the impact of temperature on hydrous magnesium silicate Phase D using deep-learning-driven interatomic potentials The study of hydrogen bonding symmetry in lower mantle water-bearing minerals is scientifically critical for understanding the water cycle in the deep Earth, the stability and physical properties of minerals, and the electrical conductivity structure of the Earth's interior. We report classical molecular dynamics simulations of Phase D $({\mathrm{MgSi}}_{2}{\mathrm{O}}_{6}{\mathrm{H}}_{2})$, a dense hydrous magnesium silicate, using a deep-learning-optimized interatomic potential, demonstrating spontaneous hydrogen bonding symmetrization emergence at 33.7 GPa under hydrostatic compression. Additionally, within a specific pressure-temperature range, we observed abnormal phenomena in bulk modulus and lattice constant ratio. Further analysis indicates that these anomalies are actually caused by the phase transition from asymmetric hydrogen-bonded state 1 (AHB1) to AHB2 and the proton-disordering transition, decoupled from hydrogen bonding symmetry. This study provides insight into the structure and dynamics of the Earth's interior, as well as the distribution and occurrence state of hydrogen within the Earth. Silicate ; Magnesium ; Phase (matter) ; Interatomic potential ; Materials science ; Mineralogy ; Geology ; Chemistry ; Metallurgy ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Yanxia Li | Wenhao Fan | Mengyang Li | Jin Zhang | Jiayu Zhao | Wei Yan | Heng Li | Shougen Yin | Hui Yan
Source: Journal of Colloid and Interface Science (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

BP/PdSe2 heterojunction device for broadband polarization-sensitive synaptic simulation, logic operation, navigation, all-day image processing and recognition Heterojunction ; Broadband ; Artificial neural network ; Image processing ; Responsivity ; Polarization (electrochemistry) ; Semiconductor ; Process (computing) ; Photoelectric effect 2D Materials and Applications ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Xiang Dai | Haichao Song | Liangfu Zhou
Source: Chemical Engineering Science (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

Inline mixing efficacy evaluation using an image-based convolutional neural network combined with numerical simulation and mixture sampling Mixing (physics) ; Convolutional neural network ; Image (mathematics) ; Sampling (signal processing) ; Artificial neural network ; Computer science ; Artificial intelligence ; Computer simulation ; Pattern recognition (psychology) ; Computer vision ; Simulation ; Physics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Industrial Vision Systems and Defect Detection
Authors: Peiqi Yu | Shikun Duan | Qixiang Jia | Yuanyu Zhu | Xueyu Guan | Ruonan Ji | Hailing Wang | Zhongshi Huang | Lin Li | Xiaolong Zhu | Xuechu Shen | Shaowei Wang
Source: ACS Photonics (article, 2025-05-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-Controlled Metasurface Design for Full-Stokes Polarization Detector Driven by SPPN Full-Stokes real-time detection, an important polarization detection method, can capture the polarization, spectrum, and intensity information on vector light field simultaneously and improve the ability to accurately identify complex targets significantly. However, in existing systems, the components required for obtaining full-Stokes polarization and circular polarization are often difficult to integrate, relying on multiple parameter adjustments, resulting in time-consuming and heavily dependent on computational resources. In this article, we combine a phase-controlled metasurface with a two-dimensional material detector to design a multidimensional detector with wavelength and full-Stokes polarization selection characteristics. We proposed a spectral phase prediction network based on a convolutional neural network-gated recurrent unit, which effectively addressed the time-consuming and labor-intensive problem of searching for appropriate phase-corresponding structures in phase-regulated metasurface design. This innovation can reduce the design time by a factor of 106. The experimental results show that the circular dichroism of the design achieves 0.8 at the design wavelength, with transmission over 87%. According to the characteristics, the spectra can be improved by combining the designed device with the Fabry–Pérot cavity. Finally, we combined van der Waals materials with the metasurface to achieve a highly integrated full-Stokes polarization spectroscopic photodetector and circular polarization imaging. This study provides a reliable technological approach for the development of next-generation spatial sensing technology and holds promise for future high-dimensional sensing devices to achieve multidimensional detection and recognition of targets. Detector ; Polarization (electrochemistry) ; Stokes parameters ; Optics ; Optoelectronics ; Materials science ; Physics ; Scattering Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Saima Rathore | Eric B. Dammer | Anantharaman Shantaraman | Fang Wu | Duc M. Duong | Edward Fox | Erik C. B. Johnson | James J. Lah | Alzheimer’s Disease Neuroimaging Initiative | Nicholas T. Seyfried | Allan I. Levey
Source: medRxiv (preprint, 2025-10-27)
Field: Chemistry — Spectroscopy

Cerebrospinal fluid proteomics for predictive assessment of Alzheimer’s Disease risk Alzheimer's disease (AD) involves early molecular changes beyond amyloid-β (Aβ) and tau, that create heterogeneous disease biology, giving rise to variable disease initiation and highly variable longitudinal trajectories. Accurately predicting trajectories is vital for design of clinical trials and for clinical care, yet current CSF and PET biomarkers provide limited predictive capabilities despite their excellent diagnostic value. We performed CSF proteomics using tandem-mass-tag mass spectrometry in 1,104 ADNI participants with extensive longitudinal assessments. Machine learning-derived protein panels accurately predicted two classes of outcomes. First, they identified several key inflection points along the disease trajectory, including onset of 1) amyloid plaque pathology (Aβ- to Aβ+; AUC=0.88), 2) symptoms (asymptomatic to symptomatic; AUC=0.89), and 3) functional decline (MCI [due-to-AD] to AD Dementia; AUC=0.88). Second, protein panels forecast longitudinal trajectories of decline, spanning both clinical domains (cognition and function) and pathological process, including tau accumulation measured by tau-PET neocortical standardized uptake value ratio (SUVR) and neurodegeneration indexed by hippocampal volume and FDG-PET SUVR. Proteomics panels outperformed conventional CSF- and PET-based Aβ and tau markers. Importantly, these predictions were driven by novel mechanisms, spanning synaptic signaling, proteostasis, metabolic stress, vascular remodeling, and immune dysregulation, that anchor distinct inflection points and shape long-term trajectories. Together, these findings position CSF proteomics as a powerful approach for anticipating disease onset and progression, with direct implications for patient stratification and personalized intervention. Proteomics ; Disease ; Neurodegeneration ; Cerebrospinal fluid ; Inflection point ; Clinical trial ; Neuroimaging ; Alzheimer's Disease Neuroimaging Initiative Advanced Proteomics Techniques and Applications ; Health, Environment, Cognitive Aging
Authors: Huu Q. Tran | H. Le-Quoc
Source: Journal of Power Sources (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Plasma sources and plasma-assisted sputtering (PVD, CVD) for thin film deposition in materials for energy conversion and storage Thin film ; Chemical vapor deposition ; Sputtering ; Plasma ; Deposition (geology) ; Physical vapor deposition ; Plasma processing ; Plasma cleaning ; Energy storage MXene and MAX Phase Materials ; ZnO doping and properties ; 2D Materials and Applications
Authors: Hong He | Yunwei Zhang | Bin Li | Chengjin Tao
Source: IEEE Access (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Explainable Climate-Based Time Series Modeling for Predicting Chemical Compositions in Tobacco Leaves The quality of tobacco leaves significantly influences cigarette flavor and market value, with chemical composition serving as a critical quality indicator. However, existing tobacco quality prediction studies mainly rely on physical leaf samples, resulting in significant time lags and limited applicability for early decision-making. To address this, we construct a 153×6 climate factor matrix covering the tobacco growth period (May 1 to September 30), incorporating daily maximum temperature, minimum temperature, mean temperature, precipitation, mean sunlight intensity, and atmospheric pressure. Notably, atmospheric pressure is introduced for the first time to enhance model generalizability. We develop and compare five predictive models: multiple linear regression (MLR), eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network combined with LSTM (CNN+LSTM). The models are trained and validated using climate and chemical composition data from 98 tobacco-growing counties in Yunnan Province (2013-2021). Experimental results demonstrate that the CNN+LSTM model achieves superior predictive accuracy, effectively capturing complex spatiotemporal interactions in climate factors. The mean absolute percentage errors (MAPE) for total sugar, reducing sugar, and total nitrogen remain within 10%-20%, while nicotine and potassium exhibit errors in the range of 20%-30%. Furthermore, Integrated Gradients (IG) analysis is employed to interpret the CNN+LSTM model, revealing the contribution of individual climate factors to chemical accumulation patterns. Our approach improves on the time lag issue of existing studies, helps producers plan resources in advance, and provides a data-driven approach for optimizing tobacco cultivation and quality management. Series (stratigraphy) ; Time series ; Computer science ; Machine learning ; Geology Spectroscopy and Chemometric Analyses
Authors: Yaoyao Fan | Sanqing Liu | Qingyan Wang | Zuohui Wang | Yuan Long | Liping Chen | Wenqian Huang
Source: Microchemical Journal (article, 2025-12-30)
Field: Chemistry — Analytical Chemistry

Viability discrimination and vigor estimation of naturally aged open-pollinated maize seeds via hyperspectral imaging Hyperspectral imaging ; Germplasm ; Raman spectroscopy ; Seedling ; Partial least squares regression ; Thermography ; Plant breeding Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote Sensing in Agriculture
Authors: Wihan Adi | Bryan E. Rubio Perez | Yuming Liu | Sydney Runkle | Kevin W. Eliceiri | Filiz Yesilköy
Source:  (article, 2025-03-19)
Field: Materials Science — Biomaterials

Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues Label-free multimodal imaging methods are essential for comprehensive tissue analysis, particularly for studying the tumor microenvironment where changes in fibrillar collagen architecture are linked to cancer progression. This study presents a novel multimodal computational imaging approach combining mid-infrared spectral imaging (MIRSI) with second harmonic generation (SHG) microscopy to detect fibrillar collagen in biological tissues. Using SHG images as ground truth, a supervised machine learning (ML) model, specifically a random forest (RF) classifier, was trained to classify fibrillar collagen in mid-infrared hyperspectral images. The study utilized five human pancreatic tissue samples, encompassing 2.8 million MIRSI spectra for training and 68 million spectra for validation. The RF-MIRSI model demonstrated robust performance in collagen segmentation (boundary F-score: 0.8 at 4-pixel thresholds), orientation (Pearson’s R: 0.82), and alignment (Pearson’s R: 0.66) when compared to SHG ground truth. These findings highlight the potential of ML-driven label-free MIRSI for detailed collagen fiber and tumor microenvironment analysis, offering a complementary structural and chemical imaging tool for tumor pathology. Infrared ; Biomedical engineering ; Materials science ; Computer science ; Optics ; Medicine ; Physics Collagen: Extraction and Characterization
Authors: Mithony Keng | Kenneth M. Merz
Source: Journal of Chemical Information and Modeling (article, 2025-07-08)
Field: Chemistry — Spectroscopy

State Ensemble Energy Recognition (SEER): A Hybrid Gas-Phase Molecular Charge State Predictor Accurately resolving a three-dimensional structure that corresponds to an experimental mass spectrometry (MS) result is valuable for outcomes such as improved analyte identification, determination of physiochemical properties relating to conformation, analyte impurity testing, and drug chemical integrity analysis. Computational approaches utilizing charge state modeling, conformational sampling, quantum mechanical optimizations, relative energy scoring, and computed ion-neutral collision cross sections (CCS) have historically achieved success at assigning equilibrium structures to ion-mobility MS-derived CCS values. Despite this positive status, there remains a lack of new computational software to achieve higher throughput when modeling large systems. A major adverse impact on computational cost is the general increase in titratable sites with molecular size, which then warrants additional protonation/deprotonation models in order to ensure that the correct charge state is captured. Here, we introduce a user-friendly machine learning program called SEER (<b>S</b>tate <b>E</b>nsemble <b>E</b>nergy <b>R</b>ecognition) to accurately and efficiently predict the equilibrium charge states of MS-relevant ions. We report that for all systems within the test set, SEER successfully captured the lowest relative energy minimum charge states within its top two predicted candidates from an overall average number of ∼ seven titratable sites. Furthermore, the density functional theory optimized geometries for SEER assigned charge states produced CCS experimental errors that are within the acceptable threshold (i.e., ≤3% error) set for this work. The benchmark study compared SEER to two well-established charge state prediction software packages CREST and Epik classic and found that SEER is either on par or better at consistently locating the correct charge states for the test set with competitive efficiency. SEER requires no additional user programming and is readily accessible through the Google Colab platform at https://github.com/mitkeng/SEER. Charge (physics) ; Benchmark (surveying) ; Chemistry ; State of charge ; Computer science ; Ion ; Energy landscape ; Algorithm ; Statistical physics ; Physics ; Thermodynamics ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Sahar Raissi | W. Sassi | Nicolas Rouge | Jean‐Yves Hihn
Source: Materials Today Communications (article, 2025-09-28)
Field: Materials Science — Materials Chemistry

Experimental study and optimizing of plant-based corrosion inhibitors for copper in simulated seawater Copper ; Corrosion ; Chloride ; Erosion corrosion of copper water tubes ; Scanning electron microscope ; Corrosion inhibitor ; Cathodic protection ; Rosmarinus Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Smart Materials for Construction
Authors: Tanaorn Bamroongshawgasame | Xi Zhang | Qiliang Li
Source: IEEE Sensors Journal (article, 2025-11-25)
Field: Materials Science — Polymers and Plastics

Emerging Trends and Applications in Thermal Imaging Using Infrared Detectors: A Review Thermal infrared imaging has emerged as a transformative sensing technology, enabling non-contact, real-time visualization of temperature variations across a wide range of applications, including aerospace and defense, autonomous navigation, medical diagnostics, and industrial monitoring. Driven by advances in infrared detector materials, sensor architectures, and signal processing algorithms, thermal imaging systems have significantly improved in sensitivity, spatial and thermal resolution, and operational versatility. This review provides an in-depth examination of the fundamental principles of infrared detection, detailing the operating mechanisms and performance characteristics of thermal, conventional photon, and quantum engineered photon detectors. Advanced developments such as uncooled microbolometers, quantum well and superlattice photodetectors, and high-operating-temperature sensors are discussed in the context of enhancing performance while reducing cost and power consumption. The paper also explores emerging applications where infrared imaging is integrated with artificial intelligence for intelligent perception, predictive maintenance, and autonomous decision-making. Critical technical challenges, including emissivity variability, atmospheric interference, thermal drift, and high power demands, are examined alongside current solutions such as multispectral imaging, deep learning-based denoising, and novel calibration techniques. Finally, future directions are proposed, highlighting opportunities in materials science, computational imaging, and sensor fusion to unlock the next generation of compact, intelligent, and high-performance thermal imaging systems. This review aims to serve as a comprehensive resource for researchers and engineers seeking to advance the capabilities and applications of infrared thermography. Emissivity ; Context (archaeology) ; Multispectral image ; Infrared ; Aerospace ; Microbolometer ; Detector ; Image sensor Transition Metal Oxide Nanomaterials ; Electrical and Thermal Properties of Materials ; Thermal Radiation and Cooling Technologies