jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jean Philip Filling | F. Post | Michael Wand | Denis Andrienko
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Direct Molecular Polarizability Prediction with SO(3) Equivariant Local Frame GNNs We introduce a novel equivariant graph neural network (GNN) architecture designed to predict the tensorial response properties of molecules. Unlike traditional frameworks that focus on regressing scalar quantities and derive tensorial properties from their derivatives, our approach maintains $SO(3)$-equivariance through the use of local coordinate frames. Our GNN effectively captures geometric information by integrating scalar, vector, and tensor channels within a local message-passing framework. To assess the accuracy of our model, we apply it to predict the polarizabilities of molecules in the QM7-X dataset and show that tensorial message passing outperforms scalar message passing models. This work marks an advancement towards developing structured, geometry-aware neural models for molecular property prediction. Scalar (mathematics) ; Focus (optics) ; Equivariant map ; Property (philosophy) ; Polarizability ; Tensor (intrinsic definition) ; Artificial neural network ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zicheng Ma | Xiaoran Zhao | Rui Zhao | Huixian Qing | Yu Yin | M. Lv | Yuyang Zhu | Lili Sun | Meiling Chen | Xiaoliang Ren
Source: Journal of Food Composition and Analysis (article, 2025-12-02)
Field: Chemistry — Analytical Chemistry

Rapid differentiation of Atractylodes chinensis and Atractylodes japonica using FT-IR spectroscopy, HPLC combined with Machine Learning Atractylodes japonica is frequently confused with or used as a substitute for Atractylodes chinensis in commercial products due to their morphological similarities, posing risks to product quality and safety. This study developed a rapid, multi-platform approach to differentiate these two species using Fourier Transform infrared (FT-IR) spectroscopy, high-performance liquid chromatography (HPLC), and machine learning. First, FT-IR data (81 spectra × 967 variables) were processed using multiple preprocessing techniques and the random frog algorithm, which selected 87 key spectral features. Based on these features, machine learning models including Extreme Learning Machine (ELM), Support Vector Machine (SVM), and Random Forest (RF) achieved 100 % classification accuracy. The robustness of this approach was further validated by correctly classifying an external data set of 54 unknown mixed samples. Furthermore, HPLC fingerprinting combined with chemometrics identified five differential chemical markers. Models built using only these five markers also achieved 100 % classification accuracy. This integrated FT-IR and HPLC strategy offers a robust and interpretable solution for authenticating Atractylodes chinensis , significantly improving quality control and preventing adulteration in functional foods and herbal products. • Integrated FT-IR spectroscopy and HPLC identifies Atractylodes species. • Machine learning models achieved 100 % classification accuracy. • Five chemical markers were identified for precise quality control. • External validation confirmed robustness on unknown mixed samples. • Prevents adulteration in functional foods and herbal products. Atractylodes ; High-performance liquid chromatography ; Chemometrics ; Support vector machine ; Random forest ; Robustness (evolution) ; Preprocessor Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Alireza Nameni | Arthur Declercq | Ralf Gabriels | Robbe Devreese | Sven Degroeve | Lennart Martens | Robbin Bouwmeester
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-02)
Field: Chemistry — Spectroscopy

iDeepLC: chemical structure information yields improved retention time prediction of peptides with unseen modifications Abstract Deep learning has notably advanced the field of liquid chromatography–mass spectrometry-based proteomics. Accurate prediction of peptide retention times significantly enhances our ability to match LC-MS data with the correct peptides and proteins, especially for DIA data. While numerous models predict peptide LC retention times with high accuracy, few can accurately predict the retention times of chemically modified peptides, particularly those with modifications not encountered during model training. In our previously developed DeepLC model, accurate predictions could be made for unseen modifications by leveraging the chemical composition of (modified) residues. Here, however, we present a further enhancement of this model based on chemical structural information. The resulting model, called iDeepLC, shows overall more accurate predictions, and better generalization performance for predicting the retention time of unseen modifications than DeepLC. iDeepLC is freely available as open-source software under the Apache2 license and can be found at https://github.com/CompOmics/iDeepLC . Retention time ; Generalization ; Field (mathematics) ; Artificial neural network ; Peptide ; Software Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Mohamed M. Elsenety
Source: ChemistrySelect (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

High‐Throughput Deep Learning Prediction of D‐π‐A Dye Absorption Maxima on Titania Surfaces Abstract The absorption maxima of D‐π‐Aorganic dyes on titania surfaces directly influence their ability toefficiently harvest sunlight in dye‐sensitized solar cells (DSSCs) andrelated technologies. Traditional methods of synthesizing and testing dyes aretime‐consuming.While theoretical TD‐DFT calculations are commonly used to estimateabsorption maxima, they require complex, computationally demanding clusterapproaches, making them impractical for high‐throughput screening. Accuratepredictions streamline the design process, saving both time and cost. In thiswork, we present a highly accurate and rapid deep‐learning approach thatleverages chemical fingerprint descriptors to predict the absorption maxima ofdyes on titania surfaces. Our method is trained on experimental data from over4000 organic chemical structures, ensuring robust and reliable performance. Ourensemble of 10 models achieved an R² score of 0.94, indicating an excellent, fast, and efficient predictive model capable of capturing complex relationshipsbetween molecular features and absorption behavior. To support the broaderscientific community, we have developed a freely available web‐based framework.This user‐friendly tool enables researchers to predict absorption maxima andperform high‐throughput screening of multiple and unknown chemical structuresdirectly from SMILES representations within seconds, serving as a significantasset for accelerating dye design. Maxima ; Absorption (acoustics) ; Maxima and minima ; Deep learning ; Analytical Chemistry (journal) ; Absorption spectroscopy Machine Learning in Materials Science ; Nanoplatforms for cancer theranostics ; Quantum Dots Synthesis And Properties
Source: International Journal of Corrosion and Scale Inhibition (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Withania somnifera extract (WSE) as eco-friendly corrosion inhibitor for Al in acidic solutions: Experimental and mathematical analyses This study investigates the anticorrosion behavior of Withania somnifera extract (WSE) as a green inhibitor for Al in hydrochloric acid (HCl) solutions.The preparation of the plant extract involved extraction by dichloromethane.Mass loss measurements and electrochemical techniques were used to evaluate its efficacy.Results indicate a significant reduction in the corrosion rate of Al, with inhibition efficiency increasing with extract concentration, achieving up to 81.16% at 300 ppm.The adsorption behavior was analyzed, revealing a combination of physical and chemical interactions, with Gibbs free energy calculations indicating favorable adsorption processes.Surface examinations using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) confirmed the formation of a protective layer on Al surfaces.Additionally, temperature variations were shown to influence the corrosion rate and inhibition efficiency.Additionally, machine learning techniques showed that Gradient Boosting Regression (GBR) outperformed K-Nearest Neighbors (KNN) and Support Vector Regression (SVR), with R 2 values from 0.923 to 0.982.Feature importance analysis indicated that inhibitor concentration was a key predictor across datasets.These findings suggest that Withania somnifera is a promising eco-friendly alternative for corrosion inhibition in acidic environments, promoting the use of natural compounds in industrial applications. Withania somnifera ; Corrosion ; Environmentally friendly ; Corrosion inhibitor ; Chemistry ; Materials science ; Organic chemistry ; Biology ; Medicine Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Jiankai Wang | Lei Rao | Lihai Xu | Shuhui Tan | Xi Chen | Jun Tang | Wei Chen | Haodong Tang
Source: ACS Applied Nano Materials (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

A Search-and-Verification Framework for Efficient Annealing Optimization in PbS Quantum Dot Films Precise annealing is vital for achieving high performance in solution-processed PbS quantum-dot (QD) devices, yet conventional methods such as one-variable-at-a-time, full factorial design, and standard machine-learning schemes require large data sets and separate validation steps. Here we report an integrated “search-and-verification” framework that couples ant-colony optimization with hill climbing (ACO-HC) and feeds real-time experimental results back into the algorithm for precise annealing parameter prediction. Starting from a coarse large-spacing factorial design, the ACO-HC method narrows the search space iteratively to 1 °C and 1 min resolution without exhaustive screening. Applied to layer-by-layer PbS QD photoconductors, ACO-HC identifies an annealing condition of 109 °C for 32 min that boosts responsivity by 265% relative to the unannealed baseline and outperforms the best large-spacing factorial point (110 °C, 30 min). Compared with the dense small-spacing full factorial design method, ACO-HC cuts experimental time and material consumption by up to 99%. This universal, scalable strategy provides an efficient route for multiparameter process optimization and holds strong potential to accelerate the development of next-generation solution-processed semiconductor technologies. Quantum dot ; Quantum annealing ; Annealing (glass) ; Simulated annealing ; Materials science ; Optoelectronics ; Nanotechnology ; Quantum ; Computer science ; Physics ; Algorithm ; Quantum computer ; Quantum mechanics ; Composite material Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Mohamed Boubchir | Rachid Boubchir | Nadir Mohamed Belmessaoud | H. Aourag
Source: Journal of Alloys and Compounds Communications (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Data-driven prediction of structural and mechanical properties of rare-earth pyrochlores Formability ; Pyrochlore ; Compressibility ; Bulk modulus ; Ionic radius ; Atom (system on chip) ; Ionic bonding ; Elastic modulus Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Nuclear Materials and Properties
Authors: Yuganesan R | Rajkumar S | Gowseelan C | Varun Manikam S
Source:  (article, 2025-12-06)
Field: Materials Science — Surfaces, Coatings and Films

Fusion of Multi-Scale Wavelet, LBP, GLCM Features for SEM Image Classification using Machine Learning Techniques In General, Scanning Electron Microscope (SEM) images are used to strengthen the analysis of surfaces and structures of materials at microscopic scales. The automated classification of these images helps researchers efficiently manage datasets and identify materials faster. This study examined a classic machine learning approach to classify SEM images with handcrafted features for automated classification. The dataset used comprises of 18,577 SEM images which were collected at CNR-IOM (Trieste, Italy). These images fall into ten categories, includes Biological, Fibres, Nanowires, Particles, etc. The images contained no scientific metadata, and the images were divided into training and testing sets. Feature extraction was performed using Wavelet Transform, Local Binary Pattern (LBP), and Gray Level Co-occurrence Matrix (GLCM) to obtain texture and structural patterns. Four classifiers namely Random Forest, Decision Tree, K-Nearest Neighbors (K-NN), and Support Vector Machine are trained and evaluated. To evaluate the performance of the machine learning methods like accuracy, precision, and recall metrics are used. From the experimental observed that the Random Forest classifier achieved a mean cross-validation accuracy of 67.88% and a maximum test accuracy of 77.42%. Among the classifiers, SVM is the least effective compared to K-NN but lower than all the others. The results suggested that texture-based handcrafted features when combined with classical machine learning methods can provide a simple yet effective solution for SEM image classification. Pattern recognition (psychology) ; Contextual image classification ; Fusion ; Image fusion ; Statistical classification ; Feature extraction Electron and X-Ray Spectroscopy Techniques ; Image and Object Detection Techniques ; Computational Physics and Python Applications
Authors: Fabian L. Thiemann | Thiago Reschützegger | M. Esposito | Tseden Taddese | Juan D. Olarte-Plata | Fausto Martelli
Source: ArXiv.org (preprint, 2025-03-31)
Field: Materials Science — Materials Chemistry

Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks Molecular dynamics (MD) simulations play a crucial role in scientific research. Yet their computational cost often limits the timescales and system sizes that can be explored. Most data-driven efforts have been focused on reducing the computational cost of accurate interatomic forces required for solving the equations of motion. Despite their success, however, these machine learning interatomic potentials (MLIPs) are still bound to small time-steps. In this work, we introduce TrajCast, a transferable and data-efficient framework based on autoregressive equivariant message passing networks that directly updates atomic positions and velocities lifting the constraints imposed by traditional numerical integration. We benchmark our framework across various systems, including a small molecule, crystalline material, and bulk liquid, demonstrating excellent agreement with reference MD simulations for structural, dynamical, and energetic properties. Depending on the system, TrajCast allows for forecast intervals up to $30\times$ larger than traditional MD time-steps, generating over 15 ns of trajectory data per day for a solid with more than 4,000 atoms. By enabling efficient large-scale simulations over extended timescales, TrajCast can accelerate materials discovery and explore physical phenomena beyond the reach of traditional simulations and experiments. An open-source implementation of TrajCast is accessible under https://github.com/IBM/trajcast. Benchmark (surveying) ; Autoregressive model ; Trajectory ; Molecular dynamics ; Equivariant map ; Computational complexity theory ; Interatomic potential Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Moradi, Hamidreza | Filvantorkaman, Melika
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Broadband Thermoelectric Energy Harvesting for Wearable Biosensors Using Plasmonic Field-Enhancement and Machine-Learning-Guided Device Optimization Wearable biosensors increasingly require continuous and battery-free power sources, but conventional skin-mounted thermoelectric generators are limited by the small temperature differences available in real environments. This work introduces a hybrid thermoplasmonic and thermoelectric energy harvester that combines multiband plasmonic absorption with machine-learning-guided optimization to improve on-body energy conversion. A broadband metasurface made of cross-bowtie nanoantennas is designed to absorb infrared radiation across the 2 to 12 micron range, capturing human body emission, ambient infrared radiation, and near-infrared sunlight. Electromagnetic simulations show strong field enhancement in nanoscale antenna gaps, producing localized thermoplasmonic heating directly above flexible Bi2Te3 thermoelectric junctions. Coupled optical, thermal, and electrical modeling indicates that this localized heating increases the effective temperature difference from the typical 3 to 4 degrees C of standard wearable thermoelectric generators to approximately 13 degrees C. This results in a power density of about 0.15 mW per cm^2 under indoor-relevant infrared flux, representing a four- to six-fold improvement over existing flexible devices. A machine-learning surrogate model trained on multiphysics data predicts temperature rise and electrical output with high accuracy (R2 greater than 0.92) and identifies optimal device geometries through Pareto-front analysis. The proposed hybrid thermoplasmonic, thermoelectric, and machine-learning framework provides a scalable route toward more efficient, compact, and flexible energy harvesters for autonomous and long-term wearable physiological monitoring. Energy harvesting ; Multiphysics ; Wearable computer ; Thermoelectric effect ; Broadband ; Plasmon ; Thermoelectric generator ; Scalability ; Wearable technology Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermal Radiation and Cooling Technologies
Authors: Moradi, Hamidreza | Filvantorkaman, Melika
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Broadband Thermoelectric Energy Harvesting for Wearable Biosensors Using Plasmonic Field-Enhancement and Machine-Learning-Guided Device Optimization Wearable biosensors increasingly require continuous and battery-free power sources, but conventional skin-mounted thermoelectric generators are limited by the small temperature differences available in real environments. This work introduces a hybrid thermoplasmonic and thermoelectric energy harvester that combines multiband plasmonic absorption with machine-learning-guided optimization to improve on-body energy conversion. A broadband metasurface made of cross-bowtie nanoantennas is designed to absorb infrared radiation across the 2 to 12 micron range, capturing human body emission, ambient infrared radiation, and near-infrared sunlight. Electromagnetic simulations show strong field enhancement in nanoscale antenna gaps, producing localized thermoplasmonic heating directly above flexible Bi2Te3 thermoelectric junctions. Coupled optical, thermal, and electrical modeling indicates that this localized heating increases the effective temperature difference from the typical 3 to 4 degrees C of standard wearable thermoelectric generators to approximately 13 degrees C. This results in a power density of about 0.15 mW per cm^2 under indoor-relevant infrared flux, representing a four- to six-fold improvement over existing flexible devices. A machine-learning surrogate model trained on multiphysics data predicts temperature rise and electrical output with high accuracy (R2 greater than 0.92) and identifies optimal device geometries through Pareto-front analysis. The proposed hybrid thermoplasmonic, thermoelectric, and machine-learning framework provides a scalable route toward more efficient, compact, and flexible energy harvesters for autonomous and long-term wearable physiological monitoring. Energy harvesting ; Multiphysics ; Wearable computer ; Thermoelectric effect ; Broadband ; Plasmon ; Thermoelectric generator ; Scalability ; Wearable technology Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermal Radiation and Cooling Technologies
Authors: Saeed Samadi | Łukasz Cywiński | Jan A. Krzywda
Source: ArXiv.org (preprint, 2025-10-15)
Field: Materials Science — Materials Chemistry

Statistical Structure of Charge Disorder in Si/SiGe Quantum Dots Properties of quantum dot based spin qubits have significant inter-device variability due to unavoidable presence of various types of disorder in semiconductor nanostructures. A significant source of this variability is charge disorder at the semiconductor-oxide interface, which causes unpredictable, yet, as we show here, correlated fluctuations in such essential properties of quantum dots like their mutual tunnel couplings, and electronic confinement energies. This study presents a systematic approach to characterize and mitigate the effects of such disorder. We utilize finite element modeling of a Si/SiGe double quantum dot to generate a large statistical ensemble of devices, simulating the impact of trapped interface charges. This work results in a predictive statistical model capable of generating realistic artificial data for training machine learning algorithms. By applying Principal Component Analysis to this dataset, we identify the dominant modes through which disorder affects the multi-dimensional parameter space of the device. Our findings show that the parameter variations are not arbitrary, but are concentrated along a few principal axes, i.e. there are significant correlations between many properties of the devices. We finally compare that against control modes generated by sweeping the gate voltages, revealing limitations of the plunger-only control. This work provides a framework for enhancing the controllability and operational yield of spin qubit devices, by systematically addressing the nature of electrostatic disorder that leads to statistical correlations in properties of double quantum dots. Quantum dot ; Qubit ; Controllability ; Spin (aerodynamics) ; Charge (physics) ; Principal component analysis ; Statistical ensemble ; Quantum ; Curse of dimensionality Silicon Nanostructures and Photoluminescence ; Semiconductor Quantum Structures and Devices ; Semiconductor materials and interfaces