jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Osman Mamun | Chenlu Yang | Shuwen Yue
Source: ChemRxiv (preprint, 2025-03-14)
Field: Materials Science — Materials Chemistry

Deep Graph Kernel Learning for Material and Atomic Level Uncertainty Quantification in Adsorption Energy Prediction For high-throughput catalytic material discovery, Graph Neural Networks (GNNs) provide an efficient method for predicting the adsorption energies of adsorbates on transition metal surfaces. While GNNs perform well on in-domain prediction tasks, they often struggle to generalize to out-of-domain scenarios. This limitation necessitates a robust method for quantifying prediction uncertainty to enable informed catalyst discovery. Gaussian Processes (GPs) offer a principled approach to uncertainty quantification within a Bayesian framework. However, traditional implementations suffer from key challenges, including cubic time complexity, high memory requirements, and an inability to learn meaningful representations from graph structures. To address these issues, we introduce Deep Graph Kernel Learning (DGKL), a scalable framework that integrates a GNN backbone with sparse variational Gaussian Processes (SVGP) for uncertainty quantification in adsorption energy prediction. We benchmark DGKL against state-of-the-art methods such as ensemble/query-by-committee and Monte Carlo dropout, using both ranking-based metrics (e.g., Spearman's rank correlation, negative log-likelihood, miscalibration area) and error-based metrics (e.g., RMSE vs. RMV and error vs. standard deviation plots). DGKL consistently outperforms existing methods across all evaluation metrics while maintaining computational efficiency and scalability. For example, the correlation coefficient between RMSE and RMV for DGKL ranges from 0.98 to 1.00, slightly exceeding the next best method (ensemble learning). More significantly, the expected normalized calibration error (ENCE) for DGKL ranges from 0.06 to 0.15 across different datasets and GNN backbones, while the ensemble method exhibits a wider range of 0.36 to 1.55. DGKL can be incorporated into an active learning framework to iteratively explore catalytic material space, guiding the discovery of novel active catalysts. Additionally, we propose a variation of DGKL capable of predicting atomic-level uncertainty, a feature absent in existing methods. This enables fine-grained insights into out-of-domain data and provides a pathway for enhancing predictive model performance. Adsorption ; Kernel (algebra) ; Graph ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Chemistry ; Mathematics ; Theoretical computer science ; Physical chemistry ; Discrete mathematics Machine Learning in Materials Science
Authors: Mudassir Shah (11866428) | Siyang Liu (245063) | Lei Guo (36221) | Yusong Zhang (1884208) | Lingli Deng (742672) | Xiangnan Xu (9653813) | Zhaodong Fei (12508366) | Jingjing Xu (418877) | Jiyang Dong (3661738)
Source: Figshare (article, 2025-10-28)
Field: Chemistry — Spectroscopy

MSInet: A Self-SupervisedCNN Framework IntegratingGlobal and Local Context for Robust Mass Spectrometry Imaging Segmentation Mass spectrometry imaging (MSI) enables label-free molecular mapping in tissues but presents challenges for spatial segmentation due to high dimensionality, nonlinear spectral variation, and tissue heterogeneity. Traditional unsupervised clustering methods often rely on predefined cluster numbers and overlook spatial information, yielding fragmented or biologically implausible results. We introduce MSInet, a self-supervised deep learning framework for robust, annotation-free MSI segmentation. MSInet combines two strategies within a convolutional neural network: patch-wise contrastive learning to capture global semantic relationships, and superpixel-guided refinement to enforce local spatial consistency. This dual-consistency design simultaneously enhances global context awareness and local boundary precision during training. MSInet was evaluated on MALDI-MSI of mouse brain, DESI-MSI of renal tumor, and a synthetic data set with ground truth. It consistently outperformed state-of-the-art methods (e.g., t-SNE + <i>k</i>-means, CNNAE + region-growing, and GCN-based models), achieving higher accuracy and biological fidelity. On simulated data, MSInet achieved an Adjusted Rand Index of 0.89 and Normalized Mutual Information of 0.86, with ∼25.8% ARI improvement over baselines. It also precisely delineated complex anatomical subregions in the brain (Silhouette Coefficient = 0.78) and distinguished tumor, necrosis, and healthy regions in renal tissues, closely aligning with histological references. MSInet further demonstrated robustness to MSI noise. By integrating global and local contextual modeling in a self-supervised architecture, MSInet offers a powerful, scalable solution for accurate and biologically meaningful MSI segmentation, with broad potential for spatial omics and biomedical applications. Pattern recognition (psychology) ; Segmentation ; Robustness (evolution) ; Convolutional neural network ; Context (archaeology) ; Cluster analysis ; Ground truth ; Spatial analysis ; Medical imaging Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses
Authors: Teodora Basile | Lucia Rosaria Forleo | Rocco Perniola
Source: Chemosensors (article, 2025-07-03)
Field: Chemistry — Analytical Chemistry

Investigating Consumer Preferences for Novel Grape Varieties Through Hedonic Sensory Analysis and Non-Destructive Near-Infrared Spectroscopy While resistance or tolerance is a highly sought-after trait in new grape varieties, producing such grapes does not guarantee acceptance in the market. The objective of this study was to explore consumers’ appreciation of a novel tolerant table grape variety by integrating chemical, textural, and sensory analyses with near-infrared (NIR) spectroscopy. To achieve this, we developed multivariate prediction models using artificial neural networks (ANNs) that were applied to the spectra of samples subjected to sensory analysis, allowing us to predict their composition. This approach offers a non-destructive way to conduct sensory analysis, as a single NIR spectrum can assess consumer appreciation and identify the chemical and physical characteristics of each berry. For the grape variety tested, we observed a significant favorable consensus among consumers. Therefore, instead of identifying differences, we focused on determining the optimal maturity ranges that enhance consumer appreciation for this variety. Our findings represent a step toward non-destructive sensory analysis; however, further research is still needed to refine this approach. Sensory system ; Sensory analysis ; Psychology ; Cognitive psychology ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis
Authors: Safwan Zayed
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Enhancing Steel Circularity through Machine Learning Abstract The environmental impact of steel production from raw materials is significant, making scrap steel recycling a crucial step toward sustainability. However, effective recycling depends on accurate identification of scrap steel, which remains a challenge. Conventional methods such as X-ray fluorescence (XRF) and Spark Optical Emission Spectroscopy (Spark-OES) offer reliable results but suffer from high costs and sensitivity to surface conditions, often requiring time-consuming sample preparation. This study proposes a novel, cost-effective scrap steel identification approach using machine learning. Two models—K-Nearest Neighbors (KNN) regression and polynomial regression—were developed to predict the mass concentrations of alloying elements based on mechanical properties derived from tensile testing, including tensile strength, yield strength, and elongation-at-fracture. The models were trained on a dataset of standardized steel grades compiled from British Standards. Both models achieved high predictive accuracy, with R2 scores of 0.902 (KNN) and 0.901 (polynomial) and normalized RMSE values of 0.054 and 0.053, respectively. Compared to existing techniques, this approach offers strong accuracy, lower cost, and reduced sensitivity to surface conditions. The proposed method can be integrated into current recycling workflows, enhancing the quality of recycled steel and contributing to a more sustainable steel industry. Scrap ; Sensitivity (control systems) ; Ultimate tensile strength ; Identification (biology) ; Response surface methodology ; Production (economics) ; Yield (engineering) ; Raw material ; Strip steel Machine Learning in Materials Science ; Material Selection and Properties ; Mineral Processing and Grinding
Authors: Sandro Wieser | Yu-Jie Cen | Georg K. H. Madsen | Jesús Carrete
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to equilibrium molecular-dynamics (MD) simulations. We analyze the performance of those strategies, taking InAs nanowires as our benchmark due to the diverse structures and complex phonon spectra of these quasi-1D systems. We demonstrate how, for low-thermal-conductivity systems, cepstral analysis can reduce computational demands while still delivering accurate results that do not require discarding arbitrary parts of the dataset. However, issues with this approach are revealed when treating high-thermal-conductivity systems, where the thermal conductivity is significantly underestimated. We discuss alternative methods to be used in that situation, relying on uncertainty propagation from independent simulations. We show that the contributions of the covariance matrix have to be included for a quantitative assessment of the error. The combination of these strategies with machine-learning interatomic potentials (MLIPs) provides an accelerated, robust workflow applicable to a diverse set of systems, as our examples using a highly transferable MACE potential illustrate. Thermal conductivity ; Complex system ; Benchmark (surveying) ; Workflow ; Set (abstract data type) ; Complex dynamics ; Covariance ; Interatomic potential Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Noor Aida Syakira Ahmad Sabri | Nur Athirah Syafiqah Noramli | Nik Fasha Edora Nik Kamaruzaman | Nurlaila Ismail | Zakiah Mohd Yusoff | Ali Abd Almisreb | Saıful Nizam Tajuddin | Mohd Nasir Taib
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2025-05-26)
Field: Chemistry — Organic Chemistry

Robust k-NN approach for classifying Aquilaria oil species by compounds Accurate classification of Aquilaria oil species is essential for ensuring the quality and authenticity of agarwood oils, which are widely used in perfumes and traditional medicine. This study investigated the effectiveness of the k-nearest neighbours (k-NN) machine learning model for classifying Aquilaria oil species based on four significant chemical compounds: dihyro-βagarofuran, δ-guaiene, 10-epi-γ-eudesmol, and γ-eudesmol. The dataset comprised 480 samples of Aquilaria oil, which were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The k-NN model, with an optimal k-value of 10 and using euclidean distance as the distance metric, achieved 100% accuracy, sensitivity, specificity, and precision in both training and testing datasets. These results demonstrate the robustness of k-NN in species identification, highlighting the discriminative power of the selected compounds. This study verifies that the integration of chemical profiling with machine learning offers a scalable solution for accurate species identification in the essential oil industry. Future work could explore hybrid models and data expansion techniques to further enhance the classification performance in more complex environmental conditions. Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Computer science ; Machine learning Wood and Agarwood Research ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Cailum
M. K. Stienstra (16470055) | Christopher R.
M. Ryan (16007811) | Daniel Demczuk (21033595) | Justine R. Bissonnette (12024936) | Anish Arjuna (21033598) | J. Larry Campbell (1461124) | W. Scott Hopkins (1282116)
Source: Figshare (article, 2025-04-10)
Field: Chemistry — Spectroscopy

Towards Generalizable <i>In Silico</i> Predictionsof Differential Ion Mobility Using Machine Learning and CustomizedFingerprint 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. Fingerprint (computing) ; Dispersion (optics) ; Feature (linguistics) ; Ion-mobility spectrometry ; Pipeline (software) ; Analyte ; Function (biology) ; Pattern recognition (psychology) ; Process (computing) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Muhammad Shalahuddin Al Ja’farawy | Woo-Sik Jung | Won‐Seok Kim | Yeonwoo Jeong | Jinyoung Kim | Jun-Young Yang | Jun Young Kim | Rowoon Park | Eun‐Kyung Lim | Tae Hwan Kim | Ho Sang Jung
Source: Sensors and Actuators B Chemical (article, 2025-06-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hotspot-driven molecular entrapment for label-free detection of illicit drugs in biofluids Drug-facilitated crimes (DFC) involve the administration of illicit substances, leaving traces in biological samples within particular period of time. A reliable method for detecting and monitoring illicit drug in biofluids is critical for forensic and clinical applications. Herein, we introduce the hotspot-driven molecular entrapment (HDME) method as highly sensitive strategy for trapping and label-free detection of illicit drug. This approach utilizes in-situ surface growth of plasmonic particles, forming hotspots around analytes and generating intense electromagnetic fields for enhanced Raman signal detection. The HDME technique demonstrated exceptional capability in detecting and monitoring trace levels of drugs in plasma and urine, with consistent results across various concentrations and time intervals. These findings were cross-validated with LC-MS/MS, showing strong correlation and confirming the reliability of HDME for accurate and sensitive analyte detection. Furthermore, HDME was combined with machine learning to validate drug analytes in urine samples with high sensitivity, specificity, and accuracy, confirming the reliability of this method. Urine was selected over plasma due to its non-invasive collection, and suitability for on-site analysis. The integration of HDME with machine learning offers a robust and adaptable platform for detecting and monitoring illicit drug, providing significant potential for advancing forensic investigations and improving clinical diagnostics. • Hotspot driven molecular entrapment (HDME) is alternative method for drug detection. • HDME provides hotspot generation around analyte via particle growth formation. • This method enables real-time tracking of drugs in biofluid. • HDME ensures reliability through cross-validation with LC-MS/MS. • Combination with machine learning offers a robust platform for drug analysis. Entrapment ; Chemistry ; Hotspot (geology) ; Nanotechnology ; Materials science ; Medicine Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Biosensors and Analytical Detection
Authors: Jingpei Zhu | Liu Zhong | Wenting Liu | Tao Hu | Sansan Shuai | Chaoyue Chen | Songzhe Xu | Wei Ren | Jiang Wang | Zhongming Ren
Source: Materials Today Communications (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Impact of W on atomic structural evolution of Ni–W and Ni–Al–W melts: An on-the-fly machine learning force field study Tungsten ; Atomic units ; Alloy ; Field (mathematics) ; Coordination number ; Scale (ratio) Solidification and crystal growth phenomena ; High Temperature Alloys and Creep ; Intermetallics and Advanced Alloy Properties
Authors: Alinda Ezgi Gerçek | Till Korten | Paul Chekhonin | Mas Ayu Hassan | Péter Steinbach
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7~M parameters) trained on just \textbf{10 annotated scanning electron microscopy images}. Despite limited data, our model achieves a \textbf{Dice-Sørensen coefficient of 0.98}, significantly outperforming the state-of-the-art in the field of metallurgy (classical image analysis: 0.85), while reducing annotation effort by one order of magnitude compared to the state-of-the-art data efficient segmentation model. This approach enables rapid, automated carbide quantification for alloy design and generalizes to other steel types, demonstrating the potential of data-efficient deep learning in reactor-pressure-vessel steel analysis. Carbide ; Microstructure ; Segmentation ; Alloy ; Scanning electron microscope ; Thresholding ; Pipeline (software) ; Optical microscope Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; High-Velocity Impact and Material Behavior
Authors: Peichen Zhong | Bowen Deng | Shashwat Anand | Tara P. Mishra | Gerbrand Ceder
Source: ArXiv.org (preprint, 2025-06-25)
Field: Materials Science — Materials Chemistry

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure ($δ$-phase) during electrochemical cycling. In this computational study, we used charge-informed molecular dynamics with a fine-tuned CHGNet foundation potential to investigate the phase transformation in Li$_{x}$Mn$_{0.8}$Ti$_{0.1}$O$_{1.9}$F$_{0.1}$. Our results indicate that transition metal migration occurs and reorders to form the spinel-like ordering in an FCC anion framework. The transformed structure contains a higher concentration of non-transition metal (0-TM) face-sharing channels, which are known to improve Li transport kinetics. Analysis of the Mn valence distribution suggests that the appearance of tetrahedral Mn$^{2+}$ is a consequence of spinel-like ordering, rather than the trigger for cation migration as previously suggested. Calculated equilibrium intercalation voltage profiles demonstrate that the $δ$-phase, unlike the ordered spinel, exhibits solid-solution signatures at low voltage. A higher Li capacity is obtained than in the DRX phase. This study provides atomic insights into solid-state phase transformation and its relation to experimental electrochemistry, highlighting the potential of machine learning interatomic potentials for understanding complex oxide materials. Interatomic potential ; Phase (matter) ; Valence (chemistry) ; Transformation (genetics) ; Phase transition ; Cathode ; Tetrahedron ; Metal Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Gabriel R. Francas | Anouar A. Delenda | Jacob W. Martin | Colin Bousige | Irene Suarez‐Martinez | Nigel A. Marks | Chris Ewels
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

High screw dislocation density impedes stacking in graphite Dislocation ; Stacking ; Graphite ; Materials science ; Crystallography ; Condensed matter physics ; Composite material ; Chemistry ; Physics ; Nuclear magnetic resonance Graphite, nuclear technology, radiation studies ; Graphene research and applications
Authors: Lei Feng | Guoping Ding | Yefa Hu | Wenjie Xu | Weiming Yin
Source: Research Square (preprint, 2025-06-02)
Field: Materials Science — General Materials Science

Identification of poroid damage in CFRP axle tubes based on modal parameters Modal ; Identification (biology) ; Axle ; Structural engineering ; Forensic engineering ; Engineering ; Materials science ; Composite material ; Biology ; Botany Material Properties and Applications ; Mechanical Behavior of Composites