jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alec Horrmann | Yash Travadi | Jacob Carey | Ella Boytim | Kevin Mallery | Grant Schaap | Carissa Rungkittikhun | Kaylee Judith Kamalanathan | Nathaniel R. Bristow | Catalina Galeano-Garces | Adam Groth | Harrison Ball | Alexa R. Hesch | Pooja Advani | Justin Hwang | Badrinath R. Konety | Justin M. Drake
Source: medRxiv (article, 2025-12-09)
Field: Chemistry — Spectroscopy

Sensitive and Specific Early-Stage Breast Cancer Detection using Deep Proteome Profiling from Plasma Abstract Proteome-guided liquid biopsy tests hold immense promise for the future of early cancer detection. Our previous published work has shown strong performance identifying early-stage breast cancer patients using prospectively collected, case-controlled samples. Here, we analyzed the plasma proteome of 1,259 biobanked samples consisting of healthy women and women with breast cancer. The Astrin Biosciences’ breast cancer early detection test is a laboratory developed test (LDT) that uses a protein-based machine learning classifier to identify breast cancer with high accuracy. The classifier was trained on 845 women, comprising of 466 healthy and 379 with newly diagnosed, treatment naïve breast cancer and validated on 397 women (195 healthy and 202 breast cancer). All plasma samples were processed in a blinded manner coupled with semi-quantitative, label-free mass spectrometry (MS)-based analysis. The validation performance achieved 92.3% specificity, 92.6% sensitivity and an AUC of 0.975. Sensitivity remained high across all breast cancer stages and pathological and molecular subtypes. Gene set enrichment analyses (GSEA) identified epithelial-to-mesenchymal transition (EMT) and PI3K-AKT signaling as enriched in the breast cancer samples, highlighting that our test can identify cancer-related proteins in early-stage patients. A simulated population demonstrates the utility of our test as a supplement to mammography, detecting nearly all (93%) breast cancers missed by mammography and reducing the number of false positives relative to MRI and Contrast-Enhanced Mammography (CEM) by &gt;10-fold. Overall, our proteomic data demonstrates high sensitivity and specificity in women with breast cancer, especially at early stages, and is a favorable supplemental test post mammogram. Breast cancer ; Mammography ; False positive paradox ; Population ; Proteome ; Classifier (UML) ; Cancer ; Biopsy Advanced Proteomics Techniques and Applications ; Cancer Genomics and Diagnostics ; Cancer Cells and Metastasis
Authors: Caren R. Zeiger (21169739) | Kristoffer Eggestad (20895384) | Julian Walker (4451803) | Mari-Ann Einarsrud (2040109) | Benjamin A. D. Williamson (3612212) | Tor Grande (1750633)
Source: Figshare (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

QuantifyingEmptiness: On the Size of A‑SiteVacancies in Tetragonal Tungsten Bronzes Tetragonal tungsten bronzes (TTBs) represent the second largest family of ferroelectric materials and are characterized by their range of lattice sites and ability to have filled or vacant sites. However, the impact of empty A-sites (cation vacancies) on the stability and properties of TTBs has not been investigated so far. This study aims to determine the relative size of cation vacancies and propose a model to extract cation and cation vacancy sizes from lattice parameters by deconvoluting average site sizes into the separate cation and cation vacancy contributions. Lattice parameters and crystallographic data of “empty” lead-free TTB niobates were collected and compared to first-principles calculations of Sr<sub>5–5<i>x</i></sub>Ba<sub>5<i>x</i></sub>Nb<sub>10</sub>O<sub>30</sub> (SBN<i>x</i>) and Ba<sub>5–1.5<i>x</i></sub>RE<sub><i>x</i></sub>Nb<sub>10</sub>O<sub>30</sub> (BREN<i>x</i>, where RE = Bi<sup>3+</sup>, La<sup>3+</sup>, Sm<sup>3+</sup>, Y<sup>3+</sup>, or Dy<sup>3+</sup>). The neural network CHGNet was used for an initial screening of the complex configurational space of unfilled TTBs, followed by full geometry optimization of sets of configurations with the highest probabilities, using density functional theory (DFT). In empty TTBs, cation vacancies were found to take up more space than the cations that would normally be there. Vacancies at the A1-site require 13 % more space, while those at the A2-site need 6 % more space. The developed model showcases the same size trends extracted from the experimental data sets as for the DFT-optimized structures. This study is the first to report a detailed account of the effect of cation vacancies in TTBs and proposes a model to estimate the cation vacancy size, which is also useful when considering the stability of TTBs. Vacancy defect ; Tetragonal crystal system ; Tungsten ; Lattice (music) ; Crystal structure ; Density functional theory ; Space (punctuation) Ferroelectric and Piezoelectric Materials ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: H.L. Edmonds | Sudipta S. Mukherjee | Brooke Holcombe | Kevin Yeh | Rohit Bhargava | Ayanjeet Ghosh
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-01-13)
Field: Chemistry — Analytical Chemistry

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images Abstract Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUCs). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like Principal Component Analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating acquisition of a more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets and additional computational overhead. In this work we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly 6-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution, and subsequently using the upscaled spectra to accurate predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging. Infrared ; Artificial intelligence ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Computer vision ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaoyun Qin | Qinyuan Lou | Yiying Ling | Zhihao Zheng | Xiaomei Qin | Fenghua Chen | Jianbo Zhao | Yanghai Gui | Huishi Guo | Chao Zhao | Zhen Zhang
Source: Microchemical Journal (article, 2025-12-13)
Field: Chemistry — Electrochemistry

Self-assembled, ordered polystyrene microsphere-modified electrodes for simultaneous determination of Cu(II), Zn(II), and Pb(II) based on machine learning algorithms Stripping (fiber) ; Anodic stripping voltammetry ; Repeatability ; Electrode ; Polystyrene ; Detection limit ; Mean squared error ; Pressing ; Anode Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Conducting polymers and applications
Authors: Wendou Wu | Guoquan Pei | B. Zhou | Linnan Yang | Xinyong Liu | Yunmei Wang
Source: Smart Agricultural Technology (article, 2025-09-24)
Field: Chemistry — Analytical Chemistry

Soil-driven tea quality prediction via lightweight deep learning with rolled tensor representation Traditionally, tea quality assessment has heavily depended on chemical testing methods, which are typically time-intensive, expensive, and necessitate specialized laboratory equipment. These drawbacks hinder large-scale, rapid, and cost-effective quality evaluations. To tackle these issues, this paper introduces the Rolled-Mobile-Tea Network (RMTN), a model designed for tea quality prediction based on soil data. The model incorporates an innovative rolling mechanism that converts one-dimensional tea soil parameter vectors into three-dimensional tensors, overcoming the constraints of traditional convolutional neural networks (CNNs) and facilitating the extraction of more intricate soil-quality relationships. Within the RMTN architecture, the Mobile-Tea (MT) module functions as a lightweight feature extractor, utilizing cost-effective computational operations and identity mappings to generate comprehensive feature representations, thereby simplifying the model while boosting its performance. The soil data analyzed encompasses nine key indicators: Ca, Cu, Fe, K, Mg, Mn, Na, P, and Zn. This study aims to predict six crucial tea quality components: gallic catechin (GC), epigallocatechin (EGC), catechin (C), theaflavin (TF), theaflavin gallate (TF-G), and theaflavin gallate 2 (TF-G2). Experimental results reveal model's strong predictive capabilities, as evidenced by the following performance metrics (RMSE, MAE, and R2): GC (0.07, 0.05, 0.73); EGC (0.09, 0.07, 0.90); C (0.25, 0.20, 0.76); TF (0.01, 0.01, 0.82); TF-G (0.02, 0.02, 0.78); and TF-G2 (0.02, 0.02, 0.83). On average, the model achieves an RMSE of 0.08, an MAE of 0.06, and an R2 of 0.80, underscoring its robustness across various quality indicators. This research presents a new technical solution for convenient, low-cost, and accurate tea quality detection, and also offers a methodological framework for quality assessment in other crops, thereby advancing quality control practices. Boosting (machine learning) ; Theaflavin ; Convolutional neural network ; Robustness (evolution) ; Artificial neural network ; Deep learning ; Quality (philosophy) ; Catechin Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI
Authors: Dumortier, Loïc
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Model-informed training data curation for reactive all-atom potentials Computersimulaties helpen ons te begrijpen hoe de wereld werkt op het allerkleinste niveau: dat van atomen en moleculen. Deze simulaties zijn essentieel voor bijvoorbeeld de ontwikkeling van nieuwe materialen, nieuwe biomedische doorbraken,.... Maar om zulke modellen te bouwen, is er normaal gezien heel veel dure "trainingsdata" nodig. Mijn doctoraatsonderzoek focust op het ontwikkelen van slimmere, efficiëntere manieren gebasseerd op machinaal leren om deze data te verzamelen en te gebruiken. Ik heb twee nieuwe methoden ontwikkeld. De eerste, "Balanced Loss", is een nieuwe aanpak om bestaande modellen en trainingsdata beter af te stellen. In plaats van ingewikkeld handmatig werk, geef je nu simpelweg aan hoe nauwkeurig elk onderdeel van het model moet zijn. De tweede methode is een "actief lerend" systeem voor moderne, zelflerende modellen. Dit systeem zoekt zelfstandig naar de meest nuttige nieuwe informatie om het model slimmer te maken. Hiermee heb ik een machine learning model op maat gemaakt voor een complexe ionische vloeistof, dat sneller en efficiënter werkt dan conventionele fysische modellen. Deze technieken zullen het ontwikkelen en afstellen van nauwkeurige, snelle computersimulaties van chemische reacties intuitiever, slimmer en toegankelijker maken. Research data Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Physical and Chemical Molecular Interactions
Authors: Kaiqiang Ma | Zhang Lan | Huizhong Ma
Source: Ceramics International (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Machine learning potentials Decode the thermophysical properties and two-stage phase transition in LaNbO4 Materials science ; Phase transition ; Thermodynamics ; Phase (matter) ; Stage (stratigraphy) ; Condensed matter physics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Chromatography ; Geology Nuclear Materials and Properties ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Derrick Mirindi | David Sinkhonde | Tajebe Bezabih | Fatemeh Yazdandoust | James Hunter | Patrick Mirindi | Frédéric Mirindi
Source: Next research. (article, 2025-11-14)
Field: Materials Science — Polymers and Plastics

Predictive modeling of particleboard properties derived from agricultural waste biomass using machine learning algorithms Important advances in ethical resource use, self-determination, and equitable access to sustainable building materials have long been recognized as strategies for achieving affordable housing. This research systematically evaluates the physical and mechanical properties of particleboards manufactured from diverse agricultural wastes. This study also investigates the prediction of their thickness swelling (TS) based on the water absorption (WA) and their modulus of rupture (MOR) and internal bond (IB) based on the modulus of elasticity (MOE) using machine learning (ML) algorithms. Notably, particleboards made with macadamia nutshell and castor oil achieved TS values of 2.7%, meeting the ANSI/A208.1-1999 general-purpose board standard, while macadamia nutshell and gum Arabic panels can be used as P1 and P2 panels in accordance with BS EN 312. Pearson correlation analysis illustrated a strong positive relationship between WA and TS (r = 0.7162) and a significant negative correlation between density and WA (r = -0.7744), highlighting the importance of high-density formulations for moisture resistance. Mechanical properties were also strongly interrelated, with MOR-MOE (r = 0.8633) and MOR-IB (r = 0.8063) correlations indicating that strength enhancements often occur simultaneously. The predictive modeling demonstrated that decision tree (DT) models achieve the best performance compared to random forest (RF) models, with r², MAE, and RMSE values of 0.9910, 0.6889, and 1.1355 for TS, 0.8731, 0.2973, and 0.3905 for IB, and 0.9433, 1.1417, and 1.9834 for MOR prediction, respectively. Furthermore, DT models established threshold prediction, including WA ≤ 60.95% for TS equal to 16.28% and MOE ≤ 2318.0 MPa for MOR and IB exhibiting 10.86 MPa and 0.65 MPa, respectively. However, k-fold cross-validation results indicate low performance for DT models' generalization compared to training models due to outliers in the dataset. The present study recommends expanding datasets to include more agricultural residues in panel production, integrating cost and environmental impact metrics, and applying advanced ML algorithms for greater predictive accuracy and holistic sustainability assessment in future research for affordable housing. Absorption of water ; Decision tree ; Young's modulus ; Pearson product-moment correlation coefficient ; Predictive modelling ; Random forest ; Moisture ; Agricultural waste Natural Fiber Reinforced Composites ; Hygrothermal properties of building materials ; Wood Treatment and Properties
Authors: Xiaoyu Feng | Linhu Pan | Zikang Chen | Ruihui Wang | Xiurong Yang | Siwei Song | Yi Wang | Qinghua Zhang
Source: Physical Chemistry Chemical Physics (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Prediction and interpretability analysis of the properties of energetic ionic salts based on machine learning As a significant subset of energetic materials, energetic ionic salts (EISs) have garnered considerable attention due to their potential application in the fields of explosives and propellants. To accelerate their development, this study establishes machine learning models for predicting key properties of EISs including density (<i>ρ</i>), enthalpy of formation (Δ<i>H</i><sub>f</sub>), detonation velocity (<i>D</i><sub>V</sub>), detonation pressure (<i>P</i>), and thermal decomposition temperature (<i>T</i><sub>d</sub>). Using a customized descriptor set (CDS) and a dataset of 1202 EISs, five ML algorithms were evaluated. The results show that the optimal prediction model varies depending on target properties: MLP performs best for <i>ρ</i> (<i>R</i><sup>2</sup> = 0.91 and MAE = 0.036 g cm<sup>-3</sup>), SVR for <i>P</i> (<i>R</i><sup>2</sup> = 0.73 and MAE = 1.86 GPa), KRR for Δ<i>H</i><sub>f</sub> (<i>R</i><sup>2</sup> = 0.85 and MAE = 103.24 kJ mol<sup>-1</sup>) and <i>D</i><sub>V</sub> (<i>R</i><sup>2</sup> = 0.69 and MAE = 230.97 m s<sup>-1</sup>), and GBR for <i>T</i><sub>d</sub>. Prediction accuracy for <i>ρ</i> and Δ<i>H</i><sub>f</sub> exceeded that for detonation properties and <i>T</i><sub>d</sub>. Submodels based on nitro group attachment (C-NO<sub>2</sub><i>vs.</i> N-NO<sub>2</sub>) improved <i>T</i><sub>d</sub> prediction. SHAP analysis revealed that oxygen balance, and hydrogen and nitrogen counts are universally important, alongside property-specific descriptors. This work demonstrates the potential of ML in predicting the key properties of EISs, thereby accelerating their structural design. Detonation ; Interpretability ; Detonation velocity ; Explosive material ; Ionic bonding ; Work (physics) ; Energetic material ; Decomposition Machine Learning in Materials Science ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure
Authors: Oskar Munk Kronik | Ewa Makowicz | Jan H. Christensen | Nikoline Juul Nielsen | André de Villiers
Source: Journal of Chromatography A (article, 2025-10-11)
Field: Chemistry — Spectroscopy

Evaluation of ion mobility, uni- and multidimensional liquid chromatography for non-target screening of phenolic compounds in wheat flag leaves Non-target screening (NTS) of plant secondary metabolites is analytically challenging due to the complexity of mixtures with structurally similar compounds and isomers. This study evaluates the added value of ion mobility spectrometry (IMS) and comprehensive two-dimensional liquid chromatography (LC × LC) for enhancing separation, mass spectral quality, chromatographic structure and from access to collisional cross section (CCS) values in NTS workflows. Four LCHRMS configurations were compared: RPLCHRMS, HILICHRMS, RPLC × HILICHRMS, and HILIC × RPLCHRMS, each with and without travelling wave (TW)IMS. Data were acquired in data-independent acquisition (DIA) mode. Phenolic compounds in wheat flag leaf extracts were analyzed as a representative case. The results show that mass spectral purity improved up to 74 times when hyphenating RPLC with IMS for peaks with intensities <1.5 × 10<sup>4</sup>. The compound groups: flavones, flavan-3-ols, benzoic acid derivatives and phenolic acids were separated into distinct retention time regions in RPLC × HILIC, while co-eluting in RPLC and HILIC. Only two of ten isomeric pairs were partially resolved by IMS, with resolutions of 0.50 and 0.63, whereas RPLC achieved resolution values of 29 and 13 for the same pairs. Notably, the only isomers with IMS resolution ≥ 0.5 had m/z > 447. We showed that large [Formula: see text] deviations (-9.47 %) were observed when several deprotomers of a molecule were created and separated in IMS. Deprotomers are not accommodated for in the machine learning prediction models, which limits their use for such molecules. The complementary strengths of IMS and LC × LC should be exploited strategically to address specific analytical goals in plant metabolite screening. Resolution (logic) ; Mass spectrometry ; Benzoic acid ; Hydrophilic interaction chromatography ; High-performance liquid chromatography ; Electrospray ; Ion ; Reversed-phase chromatography ; Analytical Chemistry (journal) Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Jonas Fischer | T. Kunz | Kevin Treiber | Viktor Scherer
Source: Experimental Thermal and Fluid Science (article, 2025-11-29)
Field: Chemistry — Analytical Chemistry

Refuse derived fuels: Using near-infrared spectroscopy and machine learning to predict moisture content and dry particle spectra • NIRS enables fast prediction of RDF moisture content with good accuracy. • Simple regression at selected wavelengths yields precise moisture estimates. • MLP and ridge regression show robust feature selection for NIRS data. • Dry spectra are predicted from wet spectra using autoencoder and random forest. • Random forest achieves the best accuracy in dry spectrum reconstruction. Refuse derived fuel (RDF) sourced from industrial and municipal waste contains primarily plastics, paper/cardboard and textiles. Since RDF has high heating values and low prices, it is used as alternative fuel to coal in cement production, offering CO 2 reductions. However, the cement industry currently lacks real-time quality control of RDF, resulting in unnoticed reductions in heating value caused by elevated moisture content. Moisture content determination by drying takes up to 4 h, which does not allow for quick reactions in operation of the cement kiln. This study addresses the challenge by applying near-infrared spectroscopy (NIRS) to predict the moisture content of paper and cardboard particles, which retains most moisture in RDF. The particles were moisturized by different methods with NIRS measurements at each stage. For the prediction, different approaches ranging from linear regression of selected wavelength absorptions to multiple machine learning regression techniques like ridge, lasso and multilayer perceptron are compared in accuracy and feature representation. The multilayer perceptron showed accurate results and robust feature selection, although also simple linear regression at selected wavelength gave adequate accuracy. In a second step, moisture content and spectra of wet particles were used to predict the spectra of dry particles. These spectra are essential to correctly identify particle types or predict heating values, since training data consists of dry particle data. While linear models are not reaching acceptable results, both an autoencoder and a random forest regressor yielded spectra with correct shapes, which is sufficient for classification with derivative spectra. Moisture ; Water content ; Linear regression ; Multilayer perceptron ; Autoencoder ; Random forest ; Feature selection ; Lasso (programming language) ; Regression Spectroscopy and Chemometric Analyses ; Landfill Environmental Impact Studies ; Mineral Processing and Grinding