jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Durgesh Ameta | Heena Sharma | Praful Hambarde | Laxmidhar Behera
Source: IEEE Transactions on Instrumentation and Measurement (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

CIRSNet: A Lightweight Chemometric Infrared Spectral Network for Spectral Analysis Fourier Transform Infrared (FTIR) spectroscopy is a powerful, non-destructive analytical technique widely used in pharmaceuticals, chemicals, food safety, environmental monitoring, forensics, materials science, and biotechnology. However, data modeling remains a challenge beyond spectral acquisition, especially for real-time, edge-based applications. Traditional chemometric methods require extensive preprocessing and computationally intensive offline analysis, making real-time spectral interpretation difficult. We introduce Chemometric Infrared Spectra Network (CIRSNet), a lightweight deep learning model optimized for molecular characterization, enabling onboard processing directly on handheld spectrometers. Unlike conventional multivariate statistical methods and ML-based approaches, CIRSNet eliminates the need for external computation while maintaining high predictive accuracy. A major challenge in spectral analysis is its multilabel nature, where one spectrum may correspond to multiple properties, compounded by the highly imbalanced distribution of spectral data. To address this, CIRSNet is designed for imbalanced multilabel classification, ensuring robust performance even for underrepresented classes. Additionally, we integrate a Spectral-Spatial Attention (SSA) module, which enhances spectral representation by capturing both local and global patterns while preserving dominant peak positions and intensities. We evaluate CIRSNet on a publicly available, highly imbalanced spectroscopy dataset, achieving state-of-the-art performance in functional group detection, and validate its generalizability on an odorant property prediction dataset, where it outperforms existing methods. By enabling real-time, edge-compatible FTIR spectroscopy, CIRSNet advances molecular diagnostics, environmental monitoring, and pharmaceutical quality control, making automated spectral analysis more accessible and efficient. The source codes and trained models are available at: https://github.com/durgesgameta/CIRSNet/. Preprocessor ; Pattern recognition (psychology) ; Hyperspectral imaging ; Fourier transform ; Principal component analysis ; Partial least squares regression ; Representation (politics) ; Chemometrics ; Computation ; Artificial neural network Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Mamduh J. Aljaafreh | Sajjad Hussain Sumrra | Sadaf Noreen
Source: Journal of Fluorescence (article, 2025-08-02)
Field: Chemistry — Analytical Chemistry

Predicting Stokes Shifts of Fluorescent Chromophores Using Ensemble Learning and H2OAutoML Chemistry ; Chromophore ; Fluorescence ; Ensemble learning ; Stokes shift ; Biophysics ; Artificial intelligence ; Photochemistry ; Optics Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Eric D. Boittier | Silvan Käser | Markus Meuwly
Source: Journal of Chemical Theory and Computation (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Roadmap to CCSD(T)-Quality Machine-Learned Potentials for Condensed Phase Simulations Accurate, yet computationally efficient energy functions are essential for state-of-the-art molecular dynamics (MD) studies of condensed phase systems. Here, a generic workflow based on a combination of machine-learning-based and empirical representations of intra- and intermolecular interactions is presented. The total energy is decomposed into internal contributions and electrostatic and van der Waals interactions between monomers. The monomer potential energy surface is described using a neural network, whereas for the electrostatics the flexible minimally distributed charge model is employed. Remaining contributions between reference energies from electronic structure calculations and the model are fitted to standard Lennard-Jones (12-6) terms. For water as a topical example, reference energies for the monomers are determined from CCSD(T)-F12 calculations, whereas for an ensemble of cluster structures containing [2, 60] and [2, 4] monomers DFT and CCSD(T) energies, respectively, were used to adjust the van der Waals contributions. Based on the bulk liquid density and heat of vaporization, the best-performing set of LJ(12-6) parameters was selected and a wide range of condensed phase properties were determined and compared with experiment. MD simulations on the multiple-nanosecond time scale were carried out for water boxes containing 2000 to 8000 monomers, depending on the property considered. The performance of such a generic ML-inspired parametrization scheme is very promising and future improvements and extensions are discussed, also in view of recent advances for water in particular in the literature. van der Waals force ; Parametrization (atmospheric modeling) ; Electrostatics ; Intermolecular force ; Molecular dynamics ; Monomer ; Phase (matter) ; Range (aeronautics) ; Water model ; Electric potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Kawther Laajimi | Radhouane Laajimi | Mehdi Rahmani
Source: Journal of Alloys and Compounds (article, 2025-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning prediction of two metal-insulator transitions in the resistivity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si0009.svg"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">La</mml:mi> </mml:mrow> <mml:mrow> <mml:mn mathvariant="bold-italic">0.67</mml:mn> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">Ca</mml:mi> </mml:mrow> <mml:mrow> <mml:mn mathvariant="bold-italic">0.33</mml:mn> <mml:mo>−</mml:mo> <mml:mi mathvariant="bold-italic">x</mml:mi> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">Sr</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="bold-italic">x</mml:mi> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">Mn</mml:mi> </mml:mrow> <mml:mrow> <mml:mn mathvariant="bold-italic">0.98</mml:mn> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">Ni</mml:mi> </mml:mrow> <mml:mrow> <mml:mn mathvariant="bold-italic">0.02</mml:mn> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi mathvariant="bold-italic">O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn mathvariant="bold-italic">3</mml:mn> </mml:mrow> </mml:msub> <mml:mspace width="1em"/> </mml:mrow> </mml:math> compounds: Effects of temperature and Sr substitution level Electrical resistivity and conductivity ; Scalable Vector Graphics ; Metal ; Condensed matter physics ; Materials science ; Computer science ; Physics ; Metallurgy ; World Wide Web ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Kihyun Lee | Sol Lee | Yangjin Lee | Kwanpyo Kim
Source: Advanced Materials (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Deep Learning Analysis of Localized Interlayer Stacking Displacement and Dynamics in Bilayer Phosphorene The interlayer displacement has recently emerged as a crucial tuning parameter to control diverse physical properties in layered crystals. Transmission electron microscopy (TEM), an exceptionally powerful tool for structural analysis, directly observes the interlayer stacking and strain fields in various crystals. However, conventional analysis methods based on high-resolution phase-contrast TEM images are inadequate for recognizing spatially varying unit-cell patterns and their associated structure factors, hindering precise determination of interlayer displacements. Here, a deep learning-based analysis is introduced for atomic resolution TEM images, enabling unit-cell pattern recognition and precise identification of interlayer stacking displacement in bilayer phosphorene. The deep learning model applied to bilayer phosphorene accurately determines stacking displacement, with an error level of 3.3% displacement within the unit cell and a spatial resolution approaching the individual unit-cell level. Additionally, the model successfully processes a large set of in situ TEM data, capturing spatially varying, time-dependent interlayer displacement dynamics associated with edge reconstruction, demonstrating its potential for processing large-scale microscopy datasets. Materials science ; Phosphorene ; Stacking ; Bilayer ; Displacement (psychology) ; Dynamics (music) ; Chemical physics ; Condensed matter physics ; Nanotechnology ; Membrane ; Nuclear magnetic resonance ; Graphene ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Abbas A. Abdullahi | Ahmad Abbas Dalhatu | Abdul-Rahman Faisal Al-Betar | Syed Shaheen Shah | Syed Masiur Rahman | Tawfik. A. Saleh | Md. Abdul Aziz
Source: Journal of Materials Science (article, 2025-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A machine learning approach to optimize electrochemical performance of electrodeposited polyaniline-based supercapacitors Bayesian optimization ; Fabrication ; Supercapacitor ; Boosting (machine learning) ; Gradient boosting ; Voltage ; Random forest ; Energy (signal processing) ; Energy storage Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Anodic Oxide Films and Nanostructures
Authors: Dongyang Huang | Jiangtao Fu | Chenghao Yu
Source: Frontiers in Physics (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Machine learning discovery of the dielectric properties of strontium-containing condensed matter The dielectric constant is one of the most important physical properties of dielectrics. The pursuit of materials with high dielectric constants has long been a central focus in both fundamental research and practical applications. Traditional theoretical predictions or first-principles calculations of dielectric constants are often challenging and require significant time and computational resources. Machine learning techniques can establish models that link composition and properties, facilitating the study of dielectric properties in condensed matter and enhancing the efficiency of discovering new dielectrics. Strontium-containing dielectrics constitute a diverse class of materials, some of which exhibit exceptionally high dielectric constants, thereby showing great potential for practical applications. In this work, machine learning models were successfully developed to capture the relationship between composition and dielectric properties of strontium-containing dielectrics using different algorithms, with hyperparameter optimization performed via grid search. The optimal model achieved a correlation coefficient of 0.868 and demonstrated a certain degree of generalization ability on the test set. This model serves as a valuable reference and guide, improving the efficiency of dielectric material selection and the discovery of novel high-performance dielectrics. Strontium ; Dielectric ; Materials science ; Physics ; Nuclear physics ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Bernard Mahoney | Kingsley Yeboah Gyabaah | Patrick Mensah | Anthony Kwasi Martey | Guoqiang Li
Source: Cleaner Materials (article, 2025-10-18)
Field: Materials Science — Polymers and Plastics

Challenges and opportunities of vitrimers for aerospace applications: a roadmap for industrial adoption Vitrimer, a class of dynamically crosslinked polymers, exhibits a unique combination of processability and mechanical robustness, positioning them as a compelling alternative to conventional thermosets. By facilitating network rearrangement through exchangeable covalent bonds, these materials maintain their structural integrity while enabling reshaping, reprocessing, recycling, and damage self-healing. This capability addresses key limitations in aerospace composites, where heal-ability, recyclability, and thermal stability are critical design requirements. Despite these advantages, several challenges must be overcome before vitrimer can be fully integrated into aerospace applications. Mechanical strength, stiffness, and toughness, long-term durability under extreme operating conditions, and compatibility with automated composite manufacturing processes such as Automated Fiber Placement remain areas of active research. Recent studies indicate that vitrimer-based carbon fiber composites demonstrate improved performance metrics, particularly in impact resistance and damage tolerance, suggesting their viability for structural applications. However, further investigations are required to optimize resin formulations, refine processing parameters, incorporate multifunctionalities, and establish industry-standard testing protocols. Addressing these factors could enable the broader adoption of vitrimer, advancing the development of sustainable, high-performance aerospace materials. Artificial intelligence (AI) and machine learning (ML) could be a powerful tool to help design and discover new multifunctional vitrimer for aerospace applications. The use of vitrimer in aerospace applications offers significant potential for reducing material waste, enhancing recyclability, and lowering lifecycle energy consumption, which aligns with the principles of cleaner materials and cleaner production and sustainable material engineering. This work bridges the knowledge gap between cleaner material design and system-level sustainability. Aerospace ; Durability ; Automotive industry ; Work (physics) ; Compatibility (geochemistry) ; Industrial production ; Efficient energy use Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications
Authors: Adam Lahouari | Jutta Rogal | Mark E. Tuckerman
Source: Journal of Chemical Theory and Computation (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs remains difficult because it requires generating high-quality data sets, preprocessing atomic structures, and carefully training and validating models. In this work, we introduce an Automated Machine Learning Pipeline (AMLP) that unifies the entire workflow from data set creation to model validation. AMLP employs large-language-model agents to assist with electronic-structure code selection, input preparation, and output conversion, while its analysis suite (AMLP-Analysis) based on ASE supports a range of molecular simulations. The pipeline is built on the MACE architecture and validated on acridine polymorphs, where with a straightforward fine-tuning of a foundation model mean absolute errors of 1.7 meV/atom in energies and 7.0 meV/Å in forces are achieved. The fitted MLIP reproduces DFT geometries with sub-Å accuracy and demonstrates stability during molecular dynamics simulations in the microcanonical and canonical ensemble. Pipeline (software) ; Suite ; Workflow ; Preprocessor ; Range (aeronautics) ; Stability (learning theory) ; Interatomic potential ; Set (abstract data type) ; Training set Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Kazuma Torii | Kyoka Hara | Daiki Hatanaka | Takuto Kojima | Kentaro Kutsukake | Hiroaki Kudo | Ryoji Katsube | Noritaka Usami
Source: Journal of Applied Physics (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

A machine learning approach to reveal microstructures contributing to dislocation clusters in multicrystalline silicon We report on our attempts to leverage a machine learning model to investigate the crystallographic features contributing to the dislocation clusters in multicrystalline silicon. The developed model predicts the presence of dislocation clusters at the center of optical images and enables us to obtain their spatial distribution in a wafer by scanning the position for prediction. Information about the crystal orientation distribution was incorporated into the model by using multiple optical images of the textured wafers taken under illumination from various angles of incidence. The area under the curve, an indicator of prediction accuracy, of the optimized model was over 0.99, which is much higher than that of the previous model using optical images taken at a single illumination angle. In addition, the regions of interest in the predictions were evaluated using gradient-based class activation mapping. The results were analyzed using crystal orientation information, and we revealed crystallographic features of the regions surrounding the dislocation clusters. We then proposed a design guideline to produce multicrystalline silicon wafers with a low density of dislocation clusters based on our findings. The prediction of dislocation cluster distribution in the virtual multicrystalline silicon wafer following the guideline indicated that the number of dislocation clusters could be much lower than that in the conventional real multicrystalline silicon wafers. Accordingly, the technique proposed in this study should be effective for rapidly analyzing the relationship between the defects and the microstructure in multicrystalline materials and understanding the physics behind microstructure and defect evolution in the fabrication processes. Silicon ; Dislocation ; Materials science ; Microstructure ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Metallurgy ; Optoelectronics ; Crystallography ; Composite material ; Engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Silicon and Solar Cell Technologies ; Microstructure and mechanical properties
Authors: Yangjie Liu | Yao Guo | Shengdong Zhang | Lihong Xu | Junxiang Chen | Xiangyun Hu | Zhenhai Wen
Source: Angewandte Chemie (article, 2025-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orchestrating Penta‐Element Interplay in Gradient‐Porous Carbons for Advanced Sodium‐Ion Hybrid Capacitors Abstract Sodium‐ion hybrid capacitors (SIHCs) offer a cutting‐edge synergy between battery‐level energy density and supercapacitor‐like power density, yet face critical challenges in balancing the kinetic and capacity mismatch between Faradaic anodes and capacitive cathodes. Herein, we present a penta‐element doped gradient‐porous carbon (PE‐GPC) with a nanosphere architecture, engineered with high‐entropy principles and a gradual pore density variation to enhance mass transport and charge storage. Operando spectroscopy and machine learning potentials unveil a concerted penta‐element interplay: thiophene‐like S configurations mediate dynamic redox processes, enabling pseudocapacitive Na⁺ and anion storage, while fluorine functionalities foster a self‐rejuvenating NaF‐rich solid electrolyte interphase (SEI), stabilizing long‐term cycling. Meanwhile, the synergistic N/B/P triad engineers a hierarchical defect network that enhances electronic conductivity and fine‐tunes ion adsorption energetics. This orchestrated interplay empowers the SIHC full cell with a high energy density of 196 Wh kg −1 , a formidable power density (10.4 kW kg −1 ), and an impressive 88.2% capacity retention after 9000 cycles. By establishing a high‐entropy stabilization paradigm, this work paves the way for multi‐ion storage architectures, offering a universal strategy to bridge the charge‐transfer imbalance in advanced energy devices. Capacitor ; Porosity ; Sodium ; Ion ; Chemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Voltage ; Electrical engineering ; Engineering Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Aswin Karkadakattil
Source: International Journal of Advanced Research and Interdisciplinary Scientific Endeavours (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Physics-Augmented Neural Network for Predicting Corrosion Inhibition Efficiency of Green Inhibitors Material degradation, particularly corrosion, poses a persistent engineering challenge that impacts infrastructure, transportation, energy, and manufacturing sectors. In India alone, corrosion-related losses are estimated to exceed several billion dollars annually, affecting pipelines, bridges, marine vessels, and industrial plants. This study presents a physics-augmented artificial neural network (ANN) framework for predicting the performance of eco-friendly corrosion inhibitors in acidic environments. The approach integrates experimentally measured process parameters concentration, temperature, immersion time, and pH together with adsorption equilibrium constants obtained from independent literature sources, thereby enhancing mechanistic consistency and interpretability. A curated dataset of 60 inhibitor–condition combinations was compiled from peer-reviewed studies. The ANN, implemented in MATLAB and trained using the Levenberg–Marquardt algorithm with L2 regularization and early stopping, achieved high predictive fidelity (R² ≈ 0.99 across training, validation, and testing). Predictive robustness was further assessed through repeated random splits and bootstrapped ensembles. Beyond corrosion, the framework is generalisable to other degradation phenomena such as coating breakdown, oxidation, and galvanic corrosion relevant to aerospace, marine, and automotive sectors. Real-time deployment via a lightweight graphical interface enables engineers to rapidly estimate inhibitor performance, reduce reliance on time-consuming experiments, and support integration with digital twin platforms for predictive maintenance. The method offers a pathway to accelerate green materials selection, cut experimental screening costs substantially, and enhance the sustainability and resilience of critical infrastructure. Artificial neural network ; Corrosion ; Physics ; Artificial intelligence ; Computer science ; Materials science ; Metallurgy Corrosion Behavior and Inhibition