jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuhao Wang | Yajing Sun | Hui Chen
Source: Energy and AI (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

A comprehensive study of high-efficiency optimization method on designing segmented annular thermoelectric module • A high-efficiency method is proposed to obtain the optimal geometry structure of segmented annular thermoelectric module (SATEM) under different boundary conditions. • The relationships among the optimal parameters, output performance of SATEM and boundary conditions are analyzed and derived. • The optimal segment ratios are determined by the junction temperatures under both constant temperature and heat flux conditions To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module, such as complex modeling procedures, low computational efficiency, and poor adaptability to multi-objective design, this paper introduces an efficient structural optimization approach of segmented annular thermoelectric module that combines the uniformly equivalent element integral method and multi-parameter and multi-objective optimization algorithm under both constant temperature and heat flux boundary conditions. The optimization results show that the optimal resistance ratio is independent of the boundary conditions, and the optimal thermoelectric leg ratios remain approximately 1.2 across all studied cases in this study. Notably, the optimal segment ratios are highly sensitive to the temperatures at the two ends of the optimized segmented annular thermoelectric module under all conditions and can be directly calculated using the proposed fitting formulas. In addition, an optimal total thermoelectric leg angle exists for the segmented annular thermoelectric module to achieve the maximum temperature difference within the operating temperature range of the thermoelectric materials. The output power and efficiency of the optimized segmented annular thermoelectric module can be predicted using the parameter-based fitting formulas, with relative errors below 3% when compared to the direct optimization results. The proposed method in this paper offers significant advantages in terms of modeling simplicity, computational efficiency, and highly compatible with machine learning frameworks, thereby enabling artificial intelligence-assisted design and optimization pipelines for segmented annular thermoelectric modules. Thermoelectric effect ; Computer science ; Engineering drawing ; Materials science ; Mechanical engineering ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Anna Katharina Picha (22233654) | Sara Tkaczyk (9656749) | Thierry Langer (1316790) | Marcus Wieder (3685414) | Stefan Boresch (604024)
Source: Figshare (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Architecture-IndependentAbsolute Solvation Free EnergyCalculations with Neural Network Potentials Allowing atoms or molecules to disappear is a critical step in alchemical free energy simulations (FES). The necessary tricks are well understood when using force fields. Over the past few years, neural network potentials (NNPs) have seen rapid development. Their potentially higher accuracy compared to force fields makes them attractive for use in FES. Here, we outline a method for gradually decoupling atoms and molecules in systems that are fully described by NNPs. Specifically, we show that manipulating the neighbor list is equivalent to using soft-core potentials in force-field-based FES. Since constructing the neighbor list is a central step, regardless of the NNP’s inner workings, our approach is agnostic to NNP architecture. We validate the correctness of our methodology by demonstrating cycle closure for a model problem and report solvation free energies obtained with the MACE-OFF23(S/M) NNP. Solvation ; Correctness ; Artificial neural network ; Decoupling (probability) ; Energy (signal processing) ; Closure (psychology) ; Potential energy Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Yonghao Zhao | Zhouyuan Zhu | Sen Yang | Weihan Li
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-20)
Field: Chemistry — Analytical Chemistry

YeastSAM: A Deep Learning Model for Accurate Segmentation of Budding Yeast Cells An essential step for quantitative image analysis is cell segmentation, which is the process of defining the outline of individual cells in microscopy images. Segmentation of budding yeast is challenging due to their asymmetric cell division and mother-bud morphology. As a result, a dividing cell is frequently misidentified as two separate cells, causing errors in downstream analysis. Here, we overcame this challenge by developing YeastSAM, a deep learning-based segmentation framework derived from μSAM and optimized for budding yeast. YeastSAM achieves more than threefold higher accuracy in segmenting dividing cells compared to existing methods. When combined with single-molecule RNA imaging and organelle imaging, YeastSAM facilitates quantitative analysis of the spatial regulation of gene expression. This study offers an accessible, high-accuracy model for yeast cell segmentation, empowering researchers with minimal programming experience to perform quantitative image analysis. Budding yeast ; Deep learning ; Segmentation ; Budding ; Process (computing) ; Pattern recognition (psychology) ; Image segmentation ; Division (mathematics) Spectroscopy and Chemometric Analyses
Authors: Shuai Liu | Lili Yao | Jianpeng Chen | Kaiying Hu | Shengjie Gao | Ling Yan | Yu Mao | Baolei Dong | Lei Zheng
Source: Journal of Hazardous Materials (article, 2025-12-25)
Field: Chemistry — Organic Chemistry

Rapid and on-site detection of carbaryl in complex matrices using cellulose-based organogel sensor with assist of machine learning Carbaryl ; Cellulose ; Matrix (chemical analysis) ; Detection limit ; Complex matrix ; Sample preparation Polydiacetylene-based materials and applications ; Nanocomposite Films for Food Packaging ; Dye analysis and toxicity
Authors: Leenhouts, Roel | Jankelevitch, Sebastien | Raike, Roel | Müller, Simon | Vermeire, Florence
Source: TUHH Open Research (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Thermodynamics-informed graph neural networks for phase transition enthalpies Phase transition enthalpies, such as those for fusion, vaporization, and sublimation, are vital for understanding thermodynamic properties and aiding early-stage process design. However, measuring these properties is often time-consuming and costly, leading to increased interest in computational methods for fast and accurate predictions. Graph neural networks (GNNs), known for their ability to learn complex molecular representations, have emerged as state-of-the-art tools for predicting various thermophysical properties. Despite their success, GNNs do not inherently obey thermodynamic laws. In this study, we present a multitask GNN designed to predict vaporization, fusion, and sublimation enthalpies of organic compounds. We modified the loss function of the GNN, accounting for the thermodynamic cycle of the three phase transition enthalpies. To train the model, we digitized the extensive Chickos and Acree compendium, which encompasses 32,023 experimental measurements. Two approaches were explored: soft constraints, which guide the model toward thermodynamic consistency, and hard constraints, which enforce fully consistent predictions. The GNN achieved root mean squared errors (RMSEs) of 19.9 kJ/mol for sublimation, 11.0 kJ/mol for fusion, and 16.5 kJ/mol for vaporization enthalpies on the test set. Soft constraints were found to provide a good balance between accuracy and thermodynamic consistency, whereas hard constraints prioritized fidelity at the expense of predictive performance. When compared to the conventional Joback group contribution method the GNN demonstrated an improved accuracy and applicability range. This work underscores the potential of thermodynamics-informed GNNs for predicting thermodynamic properties accurately while maintaining consistency, paving the way for more reliable and efficient computational approaches. Thermodynamic process ; Artificial neural network ; Phase transition ; Graph ; Work (physics) ; Material properties ; Process (computing) ; Sublimation (psychology) Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics
Authors: K.C.  Hari Kumar | Madhav  Kumar Panampilly | Rohit Batra
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Martensite Start Temperature (Ms) of Steels Ranking (information retrieval) ; Rank correlation ; Reliability (semiconductor) ; Rank (graph theory) ; Correlation coefficient ; Feature (linguistics) ; Support vector machine ; Pearson product-moment correlation coefficient ; Mean squared error Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ayat Hussein Adhab | Morug Salih Mahdi | Dharmesh Sur | Anupam Yadav | R.M. Mehra | Pushpa Negi Bhakuni | Prakash Kanjariya | Fadhil Faez Sead | Aseel Salah Mansoor | Irwanjot Kaur | Chuang Song | Abhayveer Singh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Estimation of Osmotic Coefficient, Activity Coefficient and Water Activity of Aqueous Sugar Solutions Using Phsc Equation of State and the Artificial Neural Network Aqueous solution ; Artificial neural network ; Correlation coefficient ; Osmotic coefficient ; Activity coefficient ; Chemistry ; Sugar ; Coefficient of determination ; Path coefficient ; Equation of state ; Chromatography ; Thermodynamics ; Mathematics ; Computer science ; Physics ; Food science ; Physical chemistry ; Statistics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Alexandre E. Santos | Laiz R. Ventura | C. E. Fellows
Source: ACS Physical Chemistry Au (article, 2025-11-05)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric Acid Case This study proposes a noninvasive machine learning approach to infer pressure by analyzing the infrared spectral lines of the HCl molecule. High-resolution spectra were simulated using the HITRAN database across various pressures (15-900 mbar), temperatures (273-373 K), and optical paths (1-10.5 cm). Voigt profile parameters (amplitude, center, height, and Gaussian/Lorentzian widths) were extracted from these spectral lines and used to train six ML models. The ExtraTrees algorithm demonstrated superior performance, achieving an RMSE of 23.95 mbar on synthetic data. Validation with experimental spectra (78-790 mbar, 293 K) revealed strong agreement at lower pressures, with errors below 5% (e.g., 2.62% at 78 mbar). The hybrid methodology, which combines simulated training with experimental validation, circumvents the need for direct sensor exposure to corrosive environments and offers a reliable alternative for pressure retrieval. HITRAN ; Spectral line ; Line (geometry) ; Pattern recognition (psychology) ; Mean squared error ; Pressure sensor Crystallography and molecular interactions ; Phase Equilibria and Thermodynamics ; Spectroscopy and Laser Applications
Authors: Muhammad Achirul Nanda | S. Rosalinda | Reinaldy | Rahmat Budiarto | Inna Novianty | Taufik Ibnu Salim | Pradeka Brilyan Purwandoko | Dimas Firmanda Al Riza
Source: Journal of Food Composition and Analysis (article, 2025-10-26)
Field: Chemistry — Analytical Chemistry

Design and implementation of laser-light backscattering imaging system as a non-destructive technique for citrus taste evaluation Taste ; Sensory system ; Sensory analysis ; Receiver operating characteristic ; Citrus fruit ; Pattern recognition (psychology) ; Wavelength Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing in Agriculture
Authors: Zhiming Zhang (176541) | Athulya Surendran Palakkal (21237490) | Xiaoyu Wu (842130) | Jianwen Jiang (459722) | Zhongyi Jiang (583925)
Source: Figshare (article, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

DiscoveringUltra-Stable Metal–Organic Frameworksfor CO<sub>2</sub> Capture from A Wet Flue Gas: Integrating MachineLearning and Molecular Simulation The rapid increase in atmospheric CO<sub>2</sub>, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal–organic frameworks (MOFs) are promising adsorbents to potentially reduce CO<sub>2</sub> emissions from flue gases. However, many MOFs suffer from structural degradation and performance deterioration upon exposure to water in flue gases. Aiming to discover stable and efficient MOFs for CO<sub>2</sub> capture from a wet flue gas, we propose a hierarchical high-throughput computational screening (HTCS) strategy. Machine learning (ML)-assisted stability analysis is incorporated within the HTCS, leveraging prior experimental experience to predict ultrastable (including water-, thermal-, and activation-stable) MOFs from ∼280,000 candidates in the ab initio REPEAT charge MOF (ARC–MOF) database. Among 9755 shortlisted MOFs, molecular simulations identify 1000 top-performing MOFs. Remarkably, several vanadium-based MOFs are revealed to be ultrastable, exhibiting high CO<sub>2</sub> capture capability of 3–7 mmol/g and CO<sub>2</sub>/N<sub>2</sub> selectivity of 95–401. Subsequently, ML regressors are developed to derive design principles for MOFs capable of overcoming the trade-off effect. Furthermore, an ML classifier is developed to analyze the impact of water on CO<sub>2</sub> capture by comparing dry and wet conditions. The proposed hierarchical HTCS and developed ML models lay a solid foundation for the potential transition of MOFs into practical applications. Flue gas ; Stability (learning theory) ; Classifier (UML) ; Flue ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Lubna Therachiyil | Anju Surendranath | Anjana Anand | Rumeysa Dogan Sahin | Ammira S. Al-Shabeeb Akil | Ajaz A. Bhat | Shahab Uddin
Source: Journal of Proteome Research (review, 2025-12-12)
Field: Chemistry — Spectroscopy

Proteomics-Driven Cancer Biomarkers for Early Detection and Targeted Therapy: Insights from the Middle East Proteomics has become a transformative tool in oncology, offering unique opportunities for early detection, diagnosis, and cancer stratification. By enabling large-scale analysis of protein expression, interactions, and post-translational modifications, proteomics facilitates the discovery of clinically relevant biomarkers that reflect the dynamic molecular state of disease. These biomarkers, often detectable in easily accessible body fluids like blood, urine, or saliva, serve as minimally invasive "liquid biopsies" capable of identifying malignancies at asymptomatic stages. The integration of high-resolution mass spectrometry with advanced computational methods, including machine learning and network-based analytics, has accelerated the identification of reliable biomarker panels for cancer prediction, monitoring, and treatment response. Proteomics not only complements genomic and transcriptomic data but also provides a functional view of cellular states, bridging the gap between genotype and phenotype. This is especially important for customizing personalized treatment plans based on tumor-specific protein profiles, thus reducing the off-target effects of traditional therapies. Furthermore, proteomics plays a crucial role in finding cancer-related mechanisms, such as immune evasion, angiogenesis, and metastatic progression. Although proteomics-based biomarker discovery holds critical promise for cancer diagnosis and precision medicine, substantial translational challenges remain. These include assay standardization, sample accessibility from a regional biobank, regulatory obstacles, cross-population validation, and the integration of discoveries into clinical workflows. In this perspective, we offer a comprehensive overview of the latest progress in proteomics-driven cancer biomarker discovery, with a particular focus on its translational potential in early detection and precision oncology. The increasing incidence of cancer in the Middle East is highlighted, necessitating an urgent expansion of research into population-specific biomarkers. Proteomics ; Biomarker discovery ; Biomarker ; Cancer biomarkers ; Cancer ; Translational research ; Identification (biology) ; Personalized medicine Advanced Proteomics Techniques and Applications ; Clusterin in disease pathology ; Advanced Biosensing Techniques and Applications
Authors: Jianhui Ma | Zihang Ma | Muyang Li | Yujie Li | Benying Tan | Shuxue Ding
Source: Physica Scripta (article, 2025-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of multi-band absorbers driven by peak features: TIMD-based local feature enhancement Abstract Metasurfaces, due to their electromagnetic wave manipulation capabilities, provide a crucial technological approach for designing multi-band absorbers. Given the broad application potential of absorbers in various scenarios, their inverse design has increasingly become a research focus. Current deep learning–based inverse design methods for absorbers face two major challenges: the original absorption spectrum data sequences are excessively long, making effective feature extraction difficult and prone to dimensionality explosion. The Transformer for Inversed Metasurface Design (TIMD) method overcomes the sequence length limitation by introducing Multi-Head Self-Attention (MHSA) to effectively capture contextual dependencies in long sequences. However, the absorption peaks in the spectrum are jointly determined by the unit shape and material properties of the absorber, containing rich structural information. TIMD’s global attention mechanism, while computing global correlations, tends to weaken the focused emphasis on these critical peak features, which limits model performance improvement. To further enhance model performance, this paper proposes TIMD with Pyramid Attention and Learnable Feature Fusion (TIMD-PALFF). Based on TIMD, a pyramid attention mechanism is employed to progressively reduce the feature dimensionality in the attention process, guiding the model from global perception to local focus and extracting multi-level features. Meanwhile, a Learnable Feature Fusion (LFF) module is introduced to adaptively adjust the contribution of each level’s features through a set of learnable weights, thereby enhancing the weight of peak region features and improving overall model performance. The final model achieves a loss value converging to 6.14 × 10 −5 on the validation set, with the predicted absorber structures producing absorption spectra highly consistent with the target spectra. Inverse ; Feature (linguistics) ; Materials science ; Computer science ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Fatima Iftikhar | Aima Sameen Anjum | Zarish Choudry | Adan Zahid | Muhammad Qamar Khan | Hafiza Hifza Nawaz | Muhammad Musa UMAR
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Comparative Assessment of Sensory Comfort Properties Across Eco-Friendly Textile Materials for Apparel This study evaluates the sensory comfort of eco-friendly textiles, comparing natural (bamboo, organic cotton, hemp, jute, soya bean protein fibers), regenerated (Tencel (lyocell)), and synthetic (recycled polyester, recycled nylon) fibers for sustainable apparel applications. Through a comprehensive analysis, we quantify thermal, moisture, and tactile properties, revealing significant performance differences. Bamboo (thermal conductivity 0.049 W/m·K, air permeability 695 mm/s) excels in cooling and breathability, while hemp (0.022 W/m·K, thermal resistance 22.0 × 10<sup>−3</sup> m<sup>2</sup> ·K/W) offers superior insulation. Recycled polyester (1635 mm/s, moisture regains 0.4%) outperforms natural fibers in breathability but lags in moisture absorption compared to bamboo (13.0%) and Tencel (lyocell) (11.0%). Tencel (lyocell) and soya bean protein fibers provide smooth tactile comfort (friction 0.2–0.3), unlike coarser hemp (0.45). Emerging technologies like electrospinning enhance bamboo’s air permeability by 30% (~900 mm/s), while machine learning optimizes cotton blends for 15–20% better moisture management. Synthetic fibers’ low biodegradability (20–200 years vs. bamboo’s 1–2 years) poses environmental trade-offs. These metrics guide industry decisions, favoring bamboo and Tencel (lyocell) for moisture-absorbing apparel, hemp for insulation, and polyester for activewear, balancing comfort and sustainability. In this study, the comparative assessment of sensory comfort in garments, emphasizing the interplay between material properties, technological innovations, and sustainability goals, is reported.The study compares the sensory attributes of natural fabrics like organic cotton, hemp, and bamboo with regenerated fabrics like lyocell and synthetic alternatives like recycled polyester.This study provides actionable insights for research and industry stakeholders to advance the development of ecofriendly textiles that balance sensory comfort with environmental sustainability. In this study, the comparative assessment of sensory comfort in garments, emphasizing the interplay between material properties, technological innovations, and sustainability goals, is reported. The study compares the sensory attributes of natural fabrics like organic cotton, hemp, and bamboo with regenerated fabrics like lyocell and synthetic alternatives like recycled polyester. This study provides actionable insights for research and industry stakeholders to advance the development of ecofriendly textiles that balance sensory comfort with environmental sustainability. Lyocell ; Air permeability specific surface ; Bamboo ; Moisture ; Textile ; Thermal comfort ; Clothing ; Sustainability ; Synthetic fiber Textile materials and evaluations ; Natural Fiber Reinforced Composites ; Hygrothermal properties of building materials