jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nurlan Bakranov | Б. Ж. Сейтов | Дина Бакранова | Elham Fattahi | Ali Çoruh | Aligholi Niaei
Source: NNC RK Bulletin (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

PHOTOELECTROCHEMICAL WATER SPLITTING SIMULATION: TOWARD A COMPREHENSIVE MODELING FRAMEWORK This study presents a comprehensive computational framework that integrates physics-based modeling and data-driven approaches for analyzing and optimizing photoelectrochemical (PEC) water splitting systems. Utilizing COMSOL Multiphysics 6.1 and MATLAB, we simulate key electrochemical behaviors such as linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS). COMSOL’s multiphysics environment allows for the direct incorporation of electrolyte parameters, semiconductor photophysics, and current distribution, while MATLAB enables custom modeling of impedance behavior and predictive LSV analysis using artificial neural networks (ANNs). By coupling computational fluid dynamics (CFD), machine learning, and experimental validation, the proposed methodology provides an in-depth understanding of light-driven hydrogen generation on semiconductor electrodes such as ZnO/BiVO 4 . Comparative analysis of simulation results shows that COMSOL and MATLAB produce consistent outputs, yet COMSOL demonstrates superior flexibility, accuracy, and ease of use, especially for systems influenced by variable physical and chemical conditions. The study further explores two-phase flow modeling, mesh independence testing, and the influence of gas bubbles on electrolyte conductivity. The findings contribute to the development of efficient, scalable PEC systems for clean hydrogen production and offer a foundation for future integration of hybrid simulation and AI techniques in renewable energy research. Multiphysics ; Linear sweep voltammetry ; Modeling and simulation ; MATLAB ; Dielectric spectroscopy ; Scalability ; Water splitting ; Electrolyte Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Hybrid Renewable Energy Systems
Authors: Muhammad Saad Khan | Umair Baig | Abdul Waheed | Isam H. Aljundi
Source: Results in Engineering (article, 2025-12-26)
Field: Materials Science — Surfaces, Coatings and Films

Experimental and machine learning insights into the influence of aniline monomer concentration on the in situ polymerization of polyaniline on alumina supports for oil-in-water emulsion separation • Polyaniline-coated ceramic membranes, engineered with contrasting super-wettability. • Highly efficient separation of surfactant stabilized crude oil-in-water emulsion. • Membranes showed remarkable separation effectiveness (∼98.5 %) with high flux. • An experimental and machine learning study. In this study, the concentration of the aniline monomer was varied to develop an efficient ceramic-supported polyaniline (PANI) membrane with contrasting superwettability surface features for the highly efficient separation of oil-in-water (O/W) emulsions. Aniline monomers (1, 2, and 4 wt %) were used to alter the PANI surface characteristics during in situ emulsion polymerization over an alumina support. The most homogeneous surface morphology was achieved using 4 wt % aniline monomer during in situ emulsion polymerization. The membranes were characterized using various techniques, including scanning electron microscopy, energy dispersive X-ray spectroscopy, elemental mapping, Fourier-transform infrared spectroscopy, and X-ray diffraction. The 4 wt % PANI-coated alumina membrane exhibited contact angles of ∼0° and ∼151.4° in water–air and underwater oil contact angles (final). The 4 wt % PANI-coated alumina membranes produced the highest water and crude O/W emulsion flux among the fabricated membranes (8471.7 and 4115.5 L/m² h, respectively) at 2 bar pressure. The separation efficiency of the 4 wt % PANI-coated alumina membrane was significantly higher than that of the other two membranes, reaching up to 98.2 % using an O/W emulsion at a crude oil concentration of 100 ppm. Furthermore, the long-term experimental data validated the results; a comparatively lower flux drop was observed for the 4 wt % PANI-coated alumina membrane, with a separation efficiency of ≥98 % after 5.5 h of continuous operation. Moreover, the experimental data were optimized using state-of-the-art machine learning (ML) models. The ML modelling strategy highlighted that the trained ML models, particularly the supervised vector machine model, obtained an outstanding R 2 of ∼0.96 for the separation efficiency and permeate flux models. This study demonstrates that PANI-coated alumina membranes can handle oily wastewater streams, which has important operational and environmental implications. Polyaniline ; Membrane ; Emulsion ; Aniline ; Monomer ; Contact angle ; Polymerization Surface Modification and Superhydrophobicity ; Membrane Separation Technologies ; Enhanced Oil Recovery Techniques
Authors: R. Ito | T. Ishiyama | K. Nozawa | J. Kikkawa | T. Suemasu | K. Toko
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

High-quality multilayer graphene on insulators <i>via</i> metal catalyst alloying in layer exchange with Bayesian optimization The use of alloy catalysts in metal-induced layer exchange synthesis, supported by machine learning techniques, has been demonstrated to contribute to the improvement of multilayer graphene quality directly formed on a glass substrate. This advancement facilitates low-temperature synthesis and broadens the potential applications of multilayer graphene in next-generation devices. Layer (electronics) ; Graphene ; Bayesian optimization ; Catalysis ; Metal ; Bayesian probability Graphene research and applications ; Theoretical and Computational Physics ; Thermal properties of materials
Authors: Luis Josimar Vences-Reynoso | Daniel Villanueva | R. Alejo | Federico López | Sonia Martínez‐Gallegos | E. E. Granda-Gutiérrez
Source: Modelling—International Open Access Journal of Modelling in Engineering Science (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Evaluation of Neural Networks for Improved Computational Cost in Carbon Nanotubes Geometric Optimization Geometric optimization of carbon nanotubes (CNTs) is a fundamental step in computational simulations, enabling precise studies of their properties for various applications. However, this process becomes computationally expensive as the molecular structure grows in complexity and size. To address this challenge, this study utilized three deep-learning-based neural network architectures: Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (BiLSTM), and 1D Convolutional Neural Networks (1D-CNNs). Simulations were performed using the CASTEP module in Material Studio to generate datasets for training the neural networks. While the final geometric optimization calculations were completed within Material Studio, the neural networks effectively generated preoptimized CNT structures that served as starting points, significantly reducing computational time. The results showed that the 1D-CNN architecture performed best for CNTs with 28, 52, 76, and 156 atoms, while the MLP outperformed others for CNTs with 84, 124, 148, and 196 atoms. Across all cases, computational time was reduced by 39.68% to 90.62%. Although the BiLSTM also achieved reductions, its performance was less effective than the other two architectures. This work highlights the potential of integrating deep learning techniques into materials science; it also offers a transformative approach to reducing computational costs in optimizing CNTs and presents a way for accelerated research in molecular systems. Carbon nanotube ; Artificial neural network ; Nanotechnology ; Materials science ; Computer science ; Artificial intelligence Carbon Nanotubes in Composites ; Graphene research and applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Connor Ganley | Howard E. Katz | Paulette Clancy
Source: Research Square (preprint, 2025-02-07)
Field: Materials Science — Materials Chemistry

Bayesian optimization of solution-processed thermoelectric polymers using a database of ab initio electronic structure data Ab initio ; Bayesian probability ; Thermoelectric effect ; Materials science ; Polymer ; Electronic structure ; Ab initio quantum chemistry methods ; Computer science ; Computational chemistry ; Database ; Thermodynamics ; Chemistry ; Artificial intelligence ; Physics ; Quantum mechanics ; Molecule ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Andrea Angeletti
Source: University of Vienna (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for defects dynamics in materials Die elektronischen und dynamischen Eigenschaften verschiedener Defekte in unterschiedlichen Materialien wurden mithilfe innovativer, computerunterstützter Methoden untersucht, die Dichtefunktionaltheorie-Berechnungen mit ab-initio-Molekulardynamik kombinieren, beschleunigt durch maschinelle Lernpotenziale auf atomarer Ebene. Diese Vorgehensweise führte zu mehreren wegweisenden Ergebnissen: In Phosphoren konnten die Diffusion und Verschmelzung von Monovakanzen erstmals korrekt modelliert und erklärt werden, womit zuvor umstrittene experimentelle Beobachtungen geklärt wurden. Darüber hinaus wurden neuartige, experimentell beobachtete Defekte umfassend charakterisiert und ihre strukturellen sowie elektronischen Eigenschaften beleuchtet. In N-dotiertem Lutetiumhydrid konnte die spontane Bildung und Stabilität von Wasserstoffmolekülen nachgewiesen werden, was ein tieferes Verständnis metastabiler und ungeordneter Phasen in der Supraleitung eröffnet und zugleich den Einfluss von Stickstoff hervorhebt. Ergänzend wurde die Kinetik von Wasserstoff in Magnesium mit einem innovativen und effizient Active-Learning-Verfahren erfolgreich beschrieben, wodurch wesentliche Einblicke in die Festkörper-Wasserstoffspeicherung bei unterschiedlichen Temperaturen und Konzentrationen gewonnen wurden. Die Resultate stimmen stark mit experimentellen Daten überein und bestätigen die Zuverlässigkeit der Methode. Insgesamt unterstreicht diese Arbeit die Wirksamkeit der Integration von ab-initio-Methoden mit maschinellen Lernpotenzialen zur Modellierung komplexer Defektdynamiken und etabliert ein leistungsfähiges rechnergestütztes Rahmenwerk für zukünftige Forschungen und praktische Anwendungen in der Materialwissenschaft. Die (integrated circuit) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Hydrogen Storage and Materials
Authors: Zhu, Xiangzhou
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Data for Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks figures.zip : Contains all data and plotting scripts used to generate the figures in the paper models.zip: Includes the trained machine-learning models used in the active-learning workflow data_train_mace.zip: Dataset used for training the MACE machine-learning force field. Contains structures in .extxyz format with force and energy label. data_train_deeph_round0.zip: Dataset used for training the initial DeepH Hamiltonian model. Contains the processed DFT Hamiltonians, graph representations, and structural data used in DeepH-E3. data_train_deeph_active.zip: Dataset used for training the active learning rounds. Contains the processed DFT Hamiltonians, graph representations, and structural data. Workflow ; Scripting language ; Graph ; Artificial neural network ; Training set ; Thermal ; Graph theory ; Pattern recognition (psychology) ; Experimental data Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Surface and Thin Film Phenomena
Authors: Sojeong Yang | Se‐Hun Kim | Takuji Oda
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Sticking, Reflection, and Abstraction Behavior of Hydrogen Irradiated on (110) Tungsten Surfaces at 0.1-100 eV by Molecular Dynamics Simulations Using a Machine Learning Potential Tungsten ; Abstraction ; Reflection (computer programming) ; Irradiation ; Molecular dynamics ; Hydrogen ; Materials science ; Dynamics (music) ; Hydrogen atom abstraction ; Chemical physics ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Nuclear physics ; Metallurgy ; Programming language Machine Learning in Materials Science ; Nuclear Materials and Properties ; Electron and X-Ray Spectroscopy Techniques
Authors: Zixiao Yang | Hanyu Gao | Xian Kong
Source: ChemRxiv (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

Integrating Equivariant Architectures and Charge Supervision for Data-Efficient Molecular Property Prediction Understanding and predicting molecular properties remains a central challenge in scientific machine learning, especially when training data are limited or task-specific supervision is scarce. We introduce the Molecular Equivariant Transformer (MET), a symmetry-aware pretraining framework that leverages quantum-derived atomic charge distributions to guide molecular representation learning. MET combines an Equivariant Graph Neural Network (EGNN) with a Transformer architecture to extract physically meaningful features from three-dimensional molecular geometries. Unlike previous models that rely purely on structural inputs or handcrafted descriptors, MET is pretrained to predict atomic partial charges, which are quantities grounded in quantum chemistry. This enables MET to capture essential electronic information without requiring downstream labels. We show that this pretraining scheme improves performance across diverse molecular property prediction tasks, particularly in low-data regimes. Analyses of the learned representations reveal chemically interpretable structure–property relationships, including the emergence of functional group patterns and smooth alignment with molecular dipoles. Ablation studies confirm that the EGNN encoder plays a crucial role in capturing transferable spatial features, while the Transformer layers adapt these features to specific prediction tasks. This architecture draws direct analogies to quantum mechanical basis transformations, where MET learns to transition from coordinate-based to electron-based representations in a symmetry-preserving manner. By integrating domain knowledge with modern deep learning techniques, MET offers a unified and interpretable framework for data-efficient molecular modeling, with broad applications in computational chemistry, drug discovery, and materials science. Charge (physics) ; Equivariant map ; Property (philosophy) ; Computer science ; Physics ; Mathematics ; Pure mathematics ; Quantum mechanics ; Philosophy ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hao Wang | Dan Wang | Hao Long | Jun Li | Hua-Yun Geng
Source: Physical review. B./Physical review. B (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

High-pressure melting and elastic behavior of vanadium and niobium based on <i>ab initio</i> calculations and machine learning molecular dynamics Under high pressure, the group-VB transition metals vanadium (V) and niobium (Nb) exhibit simple crystal structures but complex physical behaviors, such as anomalous compression-induced softening and heating-induced hardening (CISHIH). Meanwhile, the impact of lattice thermal expansion-induced softening at elevated temperatures on HIH is yet to be investigated. Therefore, this study utilized ab initio (AIMD) and machine learning molecular dynamics (MLMD) to investigate the melting and abnormal mechanical softening-hardening behaviors of V and Nb under high pressure. Simulations reveal that the high-temperature Pnma phase of Nb reported in previous experimental studies is highly susceptible to mechanical instability and reverts to the body-centered cubic (BCC) phase. This discovery prompted a revised determination of the high-pressure melting line of Nb. The melting temperature of Nb significantly exceeds the existing theoretical and experimental estimate compared with that of V. AIMD simulations demonstrate that atomic thermal displacements have a greater influence on the HIH of V and Nb than pure electron temperature effects. In addition, the temperature-dependent anomalous elastic properties of V and Nb were investigated within a pressure range of 0-250 GPa using MLMD. The mechanical properties of V and Nb transitioned from HIH to heating-induced softening, elucidating the competition between thermal-expansion-induced softening and HIH. This study advances fundamental understanding of V and Nb physics, providing crucial theoretical foundations for establishing accurate equations of state and constitutive models for these metals. Niobium ; Softening ; Vanadium ; Molecular dynamics ; Crystal structure ; Hardening (computing) ; Melting temperature ; Phase transition ; Melting point Machine Learning in Materials Science ; High-pressure geophysics and materials ; Nuclear Materials and Properties
Authors: Chenchen Qi | Wenwei Wang | Kexin Lv | Dongyang Li | Yameng Liu | Pengfei Yu | Juhong Yu | Yong Liu | Yangyang Song | Liang Zhang | Shiyu Du
Source: AI & Materials (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Catalytic applications of 2D materials aided by machine learning Feature (linguistics) ; Catalysis ; Key (lock) ; Artificial neural network Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Surface Chemistry and Catalysis
Authors: Rashid Hashmi | SK Singh | Abhishek Kumar Singh
Source: Macromolecular Symposia (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

AI‐Driven Sustainable Polymers: A Pathway to Environmental Resilience ABSTRACT Artificial intelligence (AI) is emerging as a disruptive material science technology, and the move toward sustainable materials is critical in mitigating environmental degradation. In offering a pathway toward greater environmental resilience, this research explores the potential of AI in designing and optimizing sustainable polymers. Accurate molecular design, improved recyclability, and reduced dependence on fossil fuels are facilitated by AI‐led innovation in polymer chemistry. AI integration increases predictive modeling, accelerating performance optimization and material discovery. The report critically assesses the recent innovations in AI technologies for polymer sustainability, including AI‐assisted polymer synthesis, machine learning algorithms for biodegradability prediction, and innovative recycling approaches. The research employs a mixed‐methods approach to assess the potential of AI in sustainable material development, combining data‐driven analysis and case studies from the real world. The research also covers issues of the day, such as the economic viability of AI‐enabled polymer innovation, AI‐material ethics, and scaling AI‐enabled solutions. Policy guidelines are made public to spur industry take‐up of sustainable AI‐enabled materials. Future studies are the central theme in more precise AI models, blockchain to enable supply chain transparency, and the exploration of quantum computing for polymer science innovations. Resilience (materials science) ; Sustainability ; Supply chain ; Sustainable development ; Benchmark (surveying) ; Public policy ; Life-cycle assessment ; Sustainable design Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Microplastics and Plastic Pollution
Authors: Shengli Jiang (5166782) | Michael A. Webb (1361958)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Physics-GuidedNeural Networks for Transferable PropertyPrediction in Architecturally Diverse Copolymers The architectural, compositional, and chemical complexities of polymers are fundamentally important to their properties; however, these same factors obfuscate effective predictions. Machine learning offers a promising approach for predicting polymer properties, but model transferability remains a major challenge, particularly when data is scarce due to high acquisition costs or the growth of the parameter space. Here, we examine whether integration with polymer physics theory effectively enhances the transferability of machine learning models to predict properties of architecturally and compositionally diverse polymers. To do so, we first generate ToPoRg-18ka data set reporting the moments of the distribution of squared radius of gyration for 18,450 polymers with diverse architectures, molecular weights, compositions, and chemical patterns. We then systematically assess the performance of several different models on a series of transferability tasks, such as predicting properties of high-molecular-weight systems from smaller ones or predicting properties of copolymers from homopolymers. We find that a tandem model, GC-GNN, which combines a graph neural network with a fittable model based on ideal Gaussian chain theory, surpasses both standalone polymer physics and graph neural network models in predictive accuracy and transferability. We also demonstrate that predictive transferability varies with polymer architecture due to deviations from the ideal Gaussian chain assumption. Furthermore, the integration with theory endows GC-GNN with additional interpretability, as its learned coefficients correlate strongly with polymer solvophobicity. Overall, this study illustrates the utility of combining polymer physics with data-driven models to improve predictive transferability for architecturally diverse copolymers, showcasing an extension of physics-informed machine learning for macromolecules. Transferability ; Artificial neural network ; Gaussian ; Polymer ; Deep learning ; Property (philosophy) ; Graph Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization