jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sebastián Consuegra-Jiménez | Cesar Tovio-Gracia | Ricardo Vivas‐Reyes
Source: Results in Chemistry (article, 2025-07-15)
Field: Chemistry — Physical and Theoretical Chemistry

Unsupervised learning techniques for clustering analysis of physicochemical properties in the periodic table elements The Periodic Table of Elements, a cornerstone of chemical sciences, has traditionally been structured based on the physicochemical properties of its elements. This study employs K-means clustering, a machine-learning technique, to analyze patterns in elemental properties, including electronegativity, atomic radius, ionization energy, electron affinity, and atomic mass. By leveraging unsupervised machine learning methods, we identified novel elemental groupings that complement traditional periodic classification while uncovering previously unrecognized relationships across different groups and periods. Based on six key physicochemical properties, the clustering process yielded four distinct groups that broadly align with classical periodic divisions: (1) alkali and alkaline earth metals, (2) transition metals, (3) internal transition metals (lanthanides and actinides), and (4) nonmetals, including halogens, noble gases, and selected p-block elements. Outliers such as hydrogen were also consistently separated. These clusters demonstrate both compactness and separation, supported by a high Rand index of 0.81, indicating substantial agreement with the traditional periodic table. Validation metrics, including a silhouette coefficient of 0.72 and a Davies-Bouldin index of 1.03, confirm strong cluster cohesion and reliability. The optimal number of clusters was determined using the elbow method, with an inflection point at k = 4. This data-driven approach highlights the potential of machine learning to reveal quantitative patterns in elemental behavior, offering new insights for materials discovery and predictions of chemical reactivity while preserving coherence with foundational periodic trends. Cluster analysis ; Table (database) ; Unsupervised learning ; Computer science ; Conceptual clustering ; Artificial intelligence ; Data mining ; Fuzzy clustering ; CURE data clustering algorithm History and advancements in chemistry ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Zhenjiang Cao | Pengfei Li | Yujia He | Kai Jia | Yuxia Guan | Ning Gao | Juntao Gao | Jin Li | Rui Gao | Shujiang Ding | Kai Xi
Source: Chemical Communications (review, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Beyond macroscopic performance: nanoscale charge transfer dynamics in energy storage/conversion devices <i>via</i> scanning electrochemical cell microscopy The performance of electrochemical energy storage and conversion devices is fundamentally governed by nanoscale charge transfer dynamics at buried interfaces, which remain elusive to conventional macroscopic characterization techniques. Scanning electrochemical cell microscopy (SECCM) uniquely combines single-point probing with areal scanning to resolve localized electrochemical activity and bulk-scale architectural evolution, enabling cross-scale correlations between nanoscale charge transfer processes (<100 nm resolution) and macroscale electrode behavior (>100 μm). This capability establishes SECCM as a transformative tool for <i>operando</i> interrogation of interfacial phenomena, including metal ion deposition/insertion, stripping/extraction, and the distribution of active sites in electrocatalysts and the mechanism of degradation-induced failure, with millisecond temporal resolution. This review highlights recent advances in nanoscale charge transfer dynamics, focusing on energy storage material interfaces and electrochemical reaction mechanisms. Finally, we discuss emerging directions for SECCM, including <i>operando</i> visualization of multivalent metal dissolution/deposition (Li/Na/Zn anodes, Li-S conversion interfaces) and atomic-scale tracking of solid-state electrolyte degradation, particularly when integrated with machine learning, to accelerate the commercialization of next-generation sustainable energy technologies. Nanoscopic scale ; Scanning electrochemical microscopy ; Materials science ; Charge (physics) ; Energy storage ; Electrochemical energy conversion ; Nanotechnology ; Microscopy ; Electrochemistry ; Scanning probe microscopy ; Chemical engineering ; Optoelectronics ; Electrode ; Chemistry ; Optics ; Physics ; Physical chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Anton Soria-López | Jesús Simal‐Gándara | Juan C. Mejuto
Source: Journal of Molecular Liquids (article, 2025-08-26)
Field: Chemistry — Spectroscopy

Viscosity mixing rules and machine learning-based models for predicting the viscosity of liquid binary mixtures of aliphatic alkanes Viscosity ; Mixing (physics) ; Binary number ; Thermodynamics ; Relative viscosity ; Materials science ; Chemistry ; Chemical engineering ; Chromatography ; Physics ; Mathematics ; Engineering Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Phase Equilibria and Thermodynamics
Authors: Rajib Sahu | A. Gupta | Divyansh Mittal | Pradip K. Chatterjee | Shikhar Krishn Jha
Source: Journal of Materials Science (article, 2025-03-23)
Field: Materials Science — Materials Chemistry

Unsupervised Graph-GAN model for stress–strain field prediction in a composite Materials science ; Solid mechanics ; Composite number ; Stress field ; Field (mathematics) ; Strain (injury) ; Stress (linguistics) ; Stress–strain curve ; Composite material ; Structural engineering ; Finite element method ; Deformation (meteorology) ; Mathematics Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites
Authors: Zhengyu Zhang | Raja Shekar Bhupal Dandu | Dennis Boakye | Jun Yeop Lee | Hugh Shortt | Xuesong Fan | Lia Amalia | Zongyang Lyu | Jonathan D. Poplawsky | Chuang Deng | Peter K. Liaw | Wenjun Cai
Source: Acta Materialia (article, 2025-10-09)
Field: Materials Science — Metals and Alloys

Microstructure-agnostic deep learning for mechanistic discovery of corrosion-resistant Co-Cr-Fe-Ni MPEAs Corrosion ; Passivation ; Deep learning ; Ranking (information retrieval) ; Density functional theory ; Key (lock) ; Work (physics) ; Surface (topology) Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Francisco J. Caparrós | Paula Gomes | Manuel García‐Algar | M. Rivero | Samantha Grand | Miquel Borràs | Juan Sagales | Sara Gómez‐de Pedro
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Versatile methodology for the synthesis of stable magnetic SERS-encoded clusters for sensing applications Surface-enhanced Raman scattering (SERS) substrates are garnering increasing interest for ultrasensitive high-throughput sensing. Notably, SERS-encoded nanostructures stand out due to their potential for nearly unlimited codification with excellent optical properties. In this paper we report a simple, versatile and cost-effective method for preparing SERS-encoded clusters. These clusters consist of encoded silver nanoparticles assembled onto magnetic microparticles, which are externally coated with oxide-based structures. We propose and compare diverse shell materials, including SiO<sub>2</sub>, ZnO and TiO<sub>2</sub>. This design results in a stable and robust system with excellent magnetic and optical properties, suitable for being used in multiple media and conditions. To enhance usability, the external coating was functionalized with dopamine, facilitating further modifications. Additionally, we developed a data analysis method based on machine learning and artificial neural networks, utilizing self-organizing maps to automate particle identification. This study provides valuable information for selecting the most appropriate magnetic SERS-encoded cluster for multiplex sensing applications. Nanotechnology ; Materials science ; Combinatorial chemistry ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Jun Qiang Chan | S. M. Kayser Azam | Wong Jee Keen Raymond | Hazlee Azil Illias | Mohamadariff Othman
Source: Ain Shams Engineering Journal (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Compensation strategies for improved real-world deployment of deep learning-based partial discharge localization from digital twin data Measurement errors caused by real-time electromagnetic (EM) noise or the sensitivity of measuring equipment significantly affect Partial Discharge (PD) localization accuracy in open-space substations. This study proposes a Deep Neural Network (DNN) approach combined with an augmented Time Difference of Arrival (TDOA) dataset to improve PD coordinate estimation. A digital twin of a 10 m × 10 m × 2 m space was used to generate over one million synthetic data points, substantially reducing data collection time. The trained DNN demonstrated excellent localization performance in the digital environment, with more than 90% of errors below 5%, as validated by 3D scatter plots. Despite relying on augmented TDOA data from the digital twin, the DNN model exhibited high confidence when applied to real-world measurements, achieving an average localization error of 0.3610 m across 21 test points, outperforming Random Forest Regression (RFR), Gaussian Process Regression (GPR) and 1-dimensional Convolutional Neural Networks (1DCNN). Additionally, an in-depth analysis of the augmented TDOA dataset synthesis was conducted to optimize the DNN model. Key factors investigated included the impact of dataset size on localization accuracy, training time and performance across different training durations. Finally, a benchmarking comparison with existing methods was summarized in tabular form, highlighting the advantages of the proposed work over conventional iterative algorithms and other machine learning (ML) models. • Partial Discharge (PD) localization using Deep Neural Network (DNN) for air-insulated substation. • PD signals measured using four ultra-high frequency (UHF) sensors under dynamic noise environment. • PD localization performances were first analyzed within digital twin environment. • A DNN model trained with augmented dataset bridge the gap between simulated training and field applications. • Localization accuracy surpassed other reported works with 0.3610 m average errors in terms of speed and accuracy. Compensation (psychology) ; Software deployment ; Computer science ; Real world data ; Artificial intelligence ; Real-time computing ; Data science ; Psychology High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; ECG Monitoring and Analysis
Authors: Giovanni Beltrame | Erika Michela Dematteis | Vitalie Stavila | Paola Rizzi | Marcello Baricco | Mauro Palumbo
Source: Metals (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction. Hydrogen storage ; Hydride ; Generalizability theory ; Random forest ; Support vector machine ; Key (lock) ; Cluster analysis ; Entropy (arrow of time) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Emine Sezer | Emre Dokuzparmak | Hilal Özçeli̇k | Esra Yaşar | Tanju Kaya | Timuçin Güner | Sinan Akgöl
Source: ACS Omega (article, 2025-06-13)
Field: Chemistry — Electrochemistry

Harnessing Machine Learning to Revolutionize Electrochemical Detection of Vitamin E Acetate in E-Liquids This study presents the development of a novel molecularly imprinted electrochemical sensor for the sensitive and selective detection of vitamin E acetate (VEA) in e-cigarette liquids, a critical step in addressing the rising public health concern of e-cigarette, or vaping, product use-associated lung injury. VEA-imprinted polymeric nanoparticles, intended to serve as the recognition element on the sensor surface, were synthesized using surfactant-free emulsion polymerization. The synthesized polymer was characterized using Fourier Transformed Infrared Spectroscopy, scanning electron microscope, and zeta potential analyses. The sensor, fabricated using VEA-imprinted poly-(HMA-<i>co</i>-PA)/Nafion on screen-printed carbon electrodes, demonstrated a limit of quantification (LoQ) of 112.3 μg/mL (3.3× S/N) with a wide linear range extending to 3.0 mg/mL (10× S/N). While the sensor exhibited limitations in detecting VEA at concentrations below the LoQ, the integration of machine learning algorithms effectively mitigated these challenges. Machine learning models successfully classified the presence of VEA, even at subdetection limit concentrations, significantly enhancing the sensor's analytical capabilities. Rigorous testing on real-world e-cigarette liquid samples yielded high recovery rates (96.83% ± 2.79-102.56% ± 3.84), validating the sensor's accuracy and selectivity in complex matrices. This research not only establishes a promising platform for the rapid and sensitive detection of VEA in e-cigarette liquids but also underscores the transformative potential of integrating artificial intelligence with sensor technologies for addressing critical public health challenges. Electrochemistry ; Computer science ; Chemistry ; Nanotechnology ; Materials science ; Biomedical engineering ; Medicine ; Electrode Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Ignacio Pickering | Jonathan A. Semelak | Jiangeng Xue | Adrián E. Roitberg
Source: The Journal of Physical Chemistry B (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

TorchANI-Amber: Bridging Neural Network Potentials and Classical Biomolecular Simulations In this work we introduce TorchANI-Amber, an interface for routine molecular dynamics simulations of biomolecular systems using ANI-style machine learning potentials. TochANI-Amber incorporates the ANI neural network potentials into the Amber software suite, supporting Amber's two engines: <i>sander</i> and <i>pmemd</i>. In addition to implementing all published ANI models, the interface is extensible to other energy predicting potentials through a simple mechanism requiring no knowledge of Amber's codebase. To illustrate this versatility, we implement extensions to the AIMNet2 and Nutmeg potentials. The interface is integrated with Amber's neighborlists, and it also supports an optimized CUDA implementation for computing the ANI models' feature vectors, enabling simulations of systems with hundreds of thousands of atoms at the neural network's level of theory (approaching DFT accuracy). The interface is designed so that all amber capabilities can be used with ANI potentials instead of force fields. To evaluate the energy conservation, stability, and performance of the ANI potentials as used through the interface, we run MD simulations on different biomolecular systems, including ubiquitin and Trp-cage proteins in explicit solvent. Additionally, we demonstrate the use of these potentials in the context of enhanced sampling techniques, such as temperature replica-exchange molecular dynamics. Interface (matter) ; Artificial neural network ; Software ; Molecular dynamics ; Context (archaeology) ; Bridging (networking) ; Energy (signal processing) ; Potential energy Machine Learning in Materials Science ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: YongJun Cho | Matthew Witman | Hyung-Ki Park | Won‐Seok Ko | Brandon C. Wood | Vitalie Stavila | Eun Seon Cho
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven multi-element substitution of TiFe alloys for tunable thermodynamics and enhanced activation behaviour for hydrogen storage TiFe alloys offer high volumetric hydrogen storage but face activation and tunability limits. Using machine learning, we propose a multi-element substitution strategy that predicts hydride stability and enables near room-temperature activation. Hydrogen storage ; Substitution (logic) ; Thermodynamics ; Materials science ; Hydrogen ; Metallurgy ; Chemistry ; Computer science ; Physics ; Alloy ; Organic chemistry Hydrogen Storage and Materials ; Metallurgical and Alloy Processes ; Catalysis and Hydrodesulfurization Studies
Authors: Ujwal D. Patil | Shraddha Mahakal | D.M. Nerkar | Hemanth Noothalapati | Santosh T. Shinde | Shobha A. Waghmode | Dinesh Amalnerkar
Source: Inorganic Chemistry Communications (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Nanoarchitectured heteroatom-doped graphene as an adsorbent for dyes and heavy metals: Historical account, recent advancement and future prospects Heteroatom ; Graphene ; Adsorption ; Doping ; Materials science ; Nanotechnology ; Heavy metals ; Chemistry ; Environmental chemistry ; Organic chemistry ; Optoelectronics Graphene research and applications ; Graphene and Nanomaterials Applications ; Nanomaterials for catalytic reactions
Authors: Omid Mokhtari | Emmanuelle Bignon | Hamed Khakzad | Yasaman Karami
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-18)
Field: Materials Science — Materials Chemistry

DynaRepo: The repository of macromolecular conformational dynamics Abstract Proteins, RNA, and DNA are central to virtually all cellular processes, often assembling into macro-molecular complexes to perform their functions. While these molecules are inherently dynamic, most methods for studying their mechanisms focus on static structures. Recent deep learning advances in protein structure prediction highlight the potential of data-driven approaches, yet dynamic behavior that is critical for interactions such as antibody–antigen recognition, intrinsically disordered proteins, and protein–nucleic acid binding, remains underexplored. To address this gap, we present DynaRepo, a repository of macromolecular conformational dynamics comprising ~450 complexes and ~270 single-chain proteins from PDBbind, the Structural Antibody Database (SAbDab), and benchmark sets. Each complex was simulated in triplicate for 500 ns, totaling &gt;1100 µ s of molecular dynamics data, with extensive pre-calculated analyses. DynaRepo provides a foundation for dynamics-aware deep learning frameworks and is freely available at: https://dynarepo.inria.fr/ . Dynamics (music) ; Macromolecule ; Chemistry ; Biophysics ; Physics ; Biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Juliana Rincón‐López | Eliana Álvarez-Valdés | Daniela Velez-Arango | Estefanía Rojas Zuleta | Leopoldo Soto | Liliana Maria Lellesch | Víctor Alonso García Londoño | Milton Rosero‐Moreano | Gonzalo Taborda Ocampo
Source: Discover Sustainability (article, 2025-06-13)
Field: Chemistry — Analytical Chemistry

Versatile machine learning algorithms for FTIR spectroscopy: differentiating crosslinked and non-crosslinked gelatin samples From the food and pharmaceutical industries there is a growing demand for biomaterials from industrial waste, which are biodegradable, non-toxic, non-carcinogenic and biocompatible, as is the case of the gelatin. This is a mixture of amino acid polymers with a variable molecular weight and a diversity of secondary structures (α, β and γ chains). The functional and mechanical properties of the gelatin depend on the chemical and structural characteristics. To improve these properties, crosslinking methodologies have been implemented, which form covalent and/or non-covalent interactions within the gelatin structure. Fourier transform infrared spectroscopy (FTIR) has been presented as an alternative to evaluate structural differences and monitor the cross-linking process of four types of gelatins. A corresponds to a standard gelatin with a high level of cross-linking, B, C and D are pharmaceutical gelatins with different degrees of cross-linking used in the formulation of soft capsules. For the FTIR results, machine learning classification models developed in the Python language were used to distinguish between cross-linked and non-cross-linked gelatin samples and two dimensionality reduction techniques (PCA, PLS) and four classification models (NCA-KNN, SVM, LDA, DT) were incorporated, all effectively classifying spectra across gelatin types in adjustment, training, test stages, and predictions, with higher precision observed for gelatins A, C, and D. FTIR analysis, being rapid, efficient, non-destructive and adaptable for in-line operations, has potential for early warning systems in cross-linking processes to avoid further denaturation of gelatin. Fourier transform infrared spectroscopy ; Gelatin ; Spectroscopy ; Algorithm ; Materials science ; Polymer science ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food