jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diriba Gonfa Tolasa
Source: European Journal of Biophysics (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Advancements in Thermodynamic Modeling: Bridging Classical Theory and Computational Techniques Thermodynamics, a cornerstone of physics, focuses on the interplay between heat, work, temperature, and the statistical behavior of system s. In recent decades, the field has witnessed significant advancements in modeling techniques, integrating classical theories with modern computational methods. This paper reviews the evolution of thermodynamic modeling, highlighting both the limitations of traditional approaches and the emergence of innovative computational strategies such as molecular dynamics, Monte Carlo simulations, and machine learning. Classical thermodynamics, grounded in macroscopic observations, has established fundamental principles that govern energy and matter. However, traditional models often fall short in accurately predicting the behavior of complex systems, especially at the molecular or atomic level. Computational techniques have surfaced as powerful tools, enabling researchers to simulate intricate systems that were previously intractable, thereby enhancing our understanding of thermodynamic phenomena. The integration of classical and computational approaches has led to the development of hybrid models that leverage the strengths of both domains. These hybrid frameworks facilitate the exploration of complex phenomena, allowing for a more comprehensive understanding of thermodynamic systems and their applications in materials science, energy systems, and biological processes. Furthermore, the advent of machine learning technologies has provided new avenues for optimization and predictive modeling, significantly improving the performance of energy conversion systems. Despite these advancements, challenges remain. Issues such as data quality, system complexity, and interpret ability of machine learning models necessitate ongoing research. This paper employs a comprehensive literature review methodology to synthesize findings from various sources, identifying key themes and trends in thermodynamic modeling. It emphasizes the importance of interdisciplinary approaches that combine thermodynamics with fields like materials science and engineering. Ultimately, this study underscores the significance of bridging classical thermodynamic principles with computational techniques. It posits that continued research in this area will not only deepen our understanding of thermodynamic systems but also pave the way for innovations that address pressing global challenges, including energy efficiency and sustainability. Through this integration, the potential for breakthroughs in understanding the fundamental principles governing energy and matter is immense, setting the stage for future advancements in the field. Bridging (networking) ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Phase Equilibria and Thermodynamics
Authors: Kehan Cai | Chunyi Zhang | Xifan Wu
Source: The Journal of Chemical Physics (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Simulations of dielectric permittivity of water by machine learned potentials with long-range Coulombic interactions The dielectric permittivity of liquid water is a fundamental property that underlies its distinctive behaviors in numerous physical, biological, and chemical processes. Within a machine learning framework, we present a unified approach to compute the dielectric permittivity of water, systematically incorporating various electric boundary conditions. Our method employs a long-range-inclusive deep potential trained on data from hybrid density functional theory calculations. Dielectric response is evaluated using an auxiliary deep neural network that predicts the centers of maximally localized Wannier functions. We investigate three types of electric boundary conditions-metallic, insulating, and Kirkwood-Fröhlich-to assess their influence on correlated dipole fluctuations and dielectric relaxation dynamics. In particular, we demonstrate a consistent methodology for computing the Kirkwood correlation factor, correlation length, and dielectric permittivity under each boundary condition, where long-range electrostatics play a critical role. This work establishes a robust and generalizable machine-learning framework for modeling the dielectric properties of polar liquids under diverse electrostatic environments. Permittivity ; Dielectric ; Dipole ; Electrostatics ; Dielectric permittivity ; Relaxation (psychology) ; Work (physics) ; Boundary (topology) Machine Learning in Materials Science ; Material Dynamics and Properties ; Dielectric materials and actuators
Authors: Viktor Andonovikj | Aleksandar Dimitrov | Azemi Kovaci | Stefan Kuvendžiev | Beti Andonović
Source: Filomat (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning in optimizing carbon nanotubes and graphene production Machine learning plays a key role in this work, designing new technologies for producing carbon nanotubes (CNTs) and graphene by electrolysis in molten salts. The aim is to achieve non-expensive, high-quality materials, making them economically viable for various applications. For the production of multi-walled carbon nanotubes (MWCNTs), experiments employ both non-stationary and stationary current regimes, while for graphene production, constant and reversing cell voltage as well as constant and reversing overpotential methods are considered. The electrolysis process offers ecological and economical advantages with precise control over parameters such as applied voltage, current density, temperature, electrolyte type, and graphite material. To determine the relationship between these parameters and material quality, explainable tree-based Machine Learning (ML) models are employed, trained using labeled data from domain experts. The extracted rules from the ML model guide optimal production, resulting in high-yield materials that are up to ten times more cost-effective than existing technologies. This contributes to the advance of cost-efficient and high-quality carbon nanomaterials for a wide range of applications. Carbon nanotube ; Graphene ; Graphite ; Electrolysis ; Process engineering ; Overpotential ; Carbon fibers ; Nanotechnology ; Materials science ; Computer science ; Electrolyte ; Electrochemistry ; Engineering ; Algorithm ; Composite number ; Chemistry ; Electrode ; Composite material Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Shiru Wu | Guoyang Chen | Si Shen | Jiaxu Yan
Source: Molecules (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Point Defect Detection and Classification in MoS2 Scanning Tunneling Microscopy Images: A Deep Learning Approach Point defects in two-dimensional materials such as MoS<sub>2</sub> can critically impact their electronic and optoelectronic properties. Precise identification of these defects is essential for understanding defect physics and device performance. In this work, we acquire high-resolution scanning tunneling microscopy (STM) images of monolayer MoS<sub>2</sub> and apply the Segment Anything Model (SAM) to automatically segment possible defect regions in the STM images. Each segmented region is then classified by a convolutional neural network (CNN) architecture into defect categories. This deep learning pipeline is trained on augmented STM image data and evaluated against manual annotations. The model achieves a classification accuracy of 95.06% on a modest dataset comprising merely 198 samples, demonstrating its robustness despite limited data availability. We also perform density functional theory (DFT) calculations of representative defect structures to support interpretation of the STM features. Charge density isosurfaces of the DFT models reveal localized mid-gap states associated with sulfur vacancies, consistent with STM observations. The integration of SAM segmentation, CNN classification, and DFT modeling provides a comprehensive approach to quantify defect populations in MoS<sub>2</sub>. These results show the potential of combining data-driven image analysis with physics-based modeling to accelerate defect characterization in 2D materials. Convolutional neural network ; Scanning tunneling microscope ; Artificial intelligence ; Computer science ; Density functional theory ; Robustness (evolution) ; Pattern recognition (psychology) ; Segmentation ; Deep learning ; Pipeline (software) ; Monolayer ; Materials science ; Computer vision ; Nanotechnology ; Chemistry ; Computational chemistry 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Nanowire Synthesis and Applications
Authors: Hui Jing Hong | Mohd Adnan | Mitesh Patel | Sreenivasan Sasidharan
Source: Microchimica Acta (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Reproducibility roadblocks and standardization in carbon dot synthesis: a critical review of current practices, challenges, and future directions Standardization ; Bottleneck ; Field (mathematics) ; Characterization (materials science) ; Key (lock) ; Scientific literature ; Systematic review Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Dongdong Li | Taixiong Zheng | Aihua Hao | Ying Yao | Jiang-Bo She
Source: Photonics (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Deep Learning Fluorescence Imaging Based on CNN-BiLSTM Hybrid Neural Network for Anti-Counterfeiting Application KY3F10:Yb3+, Tm3+ upconversion microparticles (UCMPs) with varying Mn2+ doping concentrations were synthesized via a hydrothermal method. Under 980 nm laser excitation, the sample with 3 mol% Mn2+ doping demonstrated markedly enhanced luminescence performance, exhibiting a significant intensity increase compared to undoped samples. The as-synthesized UCMPs were successfully incorporated into an anti-counterfeiting ink. Target information was encrypted using a hash function to generate a QR code, which was then screen-printed onto substrate materials. Under 980 nm laser irradiation, the printed QR code exhibited visible blue fluorescence with high stability, confirming its anti-counterfeiting capability. Furthermore, an image recognition system for anti-counterfeiting, based on a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture, was developed on the Matlab platform. The system achieved 100% recognition accuracy for the luminescent QR code patterns, providing valuable insights for the development of deep learning-based image anti-counterfeiting technologies. Luminescence ; Laser ; Fluorescence ; Convolutional neural network ; Substrate (aquarium) ; Deep learning ; Artificial neural network ; Hash function Luminescence Properties of Advanced Materials ; Mineralogy and Gemology Studies ; Pharmaceutical Quality and Counterfeiting
Authors: Hanhui Li | Jiao Yang | Jun Yao | Chunhua Sheng
Source: Frontiers in Materials (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Digitized material design and performance prediction driven by high-throughput computing Introduction The advancement of digitized material design has revolutionized the field of materials science by integrating computational modeling, machine learning, and high-throughput simulations. Traditional material discovery heavily relies on iterative physical experiments, which are often resource-intensive and time-consuming. Recent developments in high-throughput computing offer an efficient alternative by enabling large-scale simulations and data-driven predictions of material properties. However, conventional predictive models frequently suffer from limited generalization, inadequate incorporation of domain knowledge, and inefficient optimization of material structures. Methods To address these limitations, we propose a novel framework that combines physics-informed machine learning with generative optimization for material design and performance prediction. Our approach consists of three major components: a graph-embedded material property prediction model that integrates multi-modal data for structure–property mapping, a generative model for structure exploration using reinforcement learning, and a physics-guided constraint mechanism that ensures realistic and reliable material designs. Results By embedding domain-specific priors into a deep learning framework, our method significantly improves prediction accuracy while maintaining physical interpretability. Extensive experiments demonstrate that our approach outperforms state-of-the-art models in both predictive performance and optimization efficiency. Discussion These findings highlight the potential of digitized design methodologies to accelerate the discovery of novel materials with desired properties and to drive next-generation material innovation. Throughput ; Computer science ; Supercomputer ; Materials science ; Computational science ; Parallel computing ; Operating system Machine Learning in Materials Science ; Neural Networks and Applications ; Manufacturing Process and Optimization
Authors: Qiang Zou | Yong-Chen Pei | Bin-Cheng Yang | W. L. Yuan | Huiqi Lu
Source: Mechanical Systems and Signal Processing (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

An adaptive monitoring method for SMA wires by integrating dual resistance signals and machine learning SMA* ; Dual (grammatical number) ; Computer science ; Artificial intelligence ; Engineering ; Algorithm Shape Memory Alloy Transformations ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Taeyeop Kim | Dongwoo Lee
Source: npj Computational Materials (article, 2025-10-10)
Field: Materials Science — Metals and Alloys

Unveiling key descriptors for electrical resistivity of alloys using high-throughput experiments and explainable AI This study examines the electrical resistivity of metals and binary, ternary alloy thin films across a broad range of compositions and microstructures through data-driven approaches. Electrical resistivity values for over 70,000 alloy compositions were measured through high-throughput experiments on combinatorially synthesized specimens. A machine learning prediction model was developed, and an explainable artificial intelligence (XAI) algorithm was utilized to identify the key features influencing electrical resistivity. The results demonstrate that the average valence electron concentration (VECavg) is the most significant descriptor governing the electrical resistivity of these alloys. Electronegativity difference (ΔEN) and mixing entropy (ΔS) were identified as collaborative features contributing to resistivity. The relationships between these features and resistivity are discussed in the context of traditional theoretical frameworks to provide a comprehensive understanding of the electrical behavior of alloys. Electrical resistivity and conductivity ; Electronegativity ; Ternary operation ; Alloy ; High entropy alloys ; Entropy (arrow of time) ; Valence (chemistry) Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: J.L. Ruíz Cerdá | Johan F. Triana
Source: Chaos An Interdisciplinary Journal of Nonlinear Science (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Machine-learning potential energy surfaces implications in photodissociation process Multi-state quantum molecular dynamics is one of the most accurate methodologies for predicting rates and yields of different chemical reactions. However, the generation of potential energy surfaces (PES), transition dipoles, and non-adiabatic couplings from ab initio calculations become a challenge, especially because of the exponential growth of computational cost as the number of electrons and molecular modes increases. Thus, machine learning (ML) emerges as a novel technique to compute molecular properties using fewer resources. Yet, the validity of ML methodologies continues in constant development, particularly for high-energy regions where conventional ab initio sampling is reduced. We test the accuracy of the potential energy surfaces interpolated with machine learning (ML) techniques in the solution of the time-dependent Schrödinger equation for the conventional IR+UV bond-breaking process of semi-heavy water. We perform a statistical analysis of the differences in expectation values and dissociation probabilities, which depend on the number of ab initio points selected to generate the machine learning potential energy surface (ML-PES). The energy differences of the electronic excited state modify population transfer from the ground state by driving with a UV laser pulse. We consider as the exact solution the photodynamics implemented with analytical expressions of the electronic ground X~1A1 and excited A~1B1 states. The results of the mean bond distance and dissociation probabilities suggest that ML-PES is suitable for dynamics calculations around the Franck-Condon region, and that standard interpolation methods are more efficient for multistate dynamics that involve dissociative and repulsive energy regions of the electronic states. Our work contributes to the continued inclusion of ML tools in molecular dynamics to obtain accurate predictions of dissociation yields with fewer computational resources and non-written rules to follow in multi-state dynamics calculations. Ab initio ; Excited state ; Potential energy surface ; Potential energy ; Population ; Dissociation (chemistry) ; Chemistry ; Atomic physics ; Ab initio quantum chemistry methods ; Ground state ; Statistical physics ; Quantum mechanics ; Physics ; Physical chemistry ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Ömer Tayfuroğlu | Seda Keskın
Source: ChemRxiv (preprint, 2025-05-02)
Field: Materials Science — Materials Chemistry

Modeling CO2 Adsorption in Flexible MOFs with Open Metal Sites via Fragment-Based Neural Network Potentials Metal-organic frameworks (MOFs) with open metal sites (OMS) are among the most promising porous materials for gas adsorption and separation, owing to their strong and selective interactions with guest molecules. However, simulating adsorption in such systems with high accuracy and efficiency remains a key challenge due to the need to model complex guest–MOF interactions and framework flexibility. Classical force fields often lack the precision to capture these effects, while ab initio methods are computationally prohibitive for large-scale, long-timescale simulations. In this work, we develop a neural network potential (NNP) trained on highly accurate DFT (PBE-D4/def2-TZVP) level data derived from a single representative fragment of the Mg-MOF-74 framework, a prototypical OMS-containing MOF, with CO2 molecules. Despite the limited training domain, the NNP accurately captured both intra- and intermolecular interactions in the CO2–Mg-MOF-74 system, including those involving the open metal sites. We integrated this NNP into a hybrid molecular dynamic (MD) and grand canonical Monte Carlo (GCMC) simulation workflow, enabling accurate modeling of CO2 adsorption in flexible MOFs. This approach allows us accounting for both framework dynamics and complex host–guest interactions with chemical accuracy and computational efficiency. Our results highlight the crucial role of framework flexibility in adsorption behavior and demonstrate that fragment-based NNP, when combined with advanced simulation techniques, offer a powerful and efficient approach for realistically modeling adsorption processes in MOFs with open metal sites. Fragment (logic) ; Adsorption ; Artificial neural network ; Metal-organic framework ; Metal ; Materials science ; Chemistry ; Nanotechnology ; Computer science ; Artificial intelligence ; Physical chemistry ; Metallurgy ; Algorithm Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Arpit Verma | Alka Rani | B. C. Yadav
Source: Nanoscale Advances (review, 2025-12-10)
Field: Materials Science — Materials Chemistry

Two-dimensional layered metal oxides (2D LMOs) for next-generation electronic devices Two-dimensional layered metal oxides (2D LMOs) have emerged as a rapidly growing class of materials that combine the advantages of reduced dimensionality with the functional diversity of transition metal oxides. Their high surface-to-volume ratio, structural anisotropy, tunable bandgap, and variable oxidation states endow them with unique electrical, optical, and catalytic properties. Recent advances in atomic layer deposition, vapor-phase synthesis, and liquid-phase exfoliation have enabled the scalable fabrication of high-quality 2D LMOs with controlled stoichiometry and thickness. This review provides a comprehensive overview of their structure-property relationships, charge transport mechanisms, and interfacial phenomena, emphasizing how defect engineering, quantum confinement, and interlayer coupling can be exploited to tailor their performance. The integration of 2D LMOs into van der Waals heterostructures further enhances band alignment, charge transfer, and excitonic control, unlocking new opportunities for transistors, sensors, and spintronic and optoelectronic devices. Current challenges such as environmental stability, phase control, and large-scale processability are critically assessed. Finally, emerging computational and machine learning-guided approaches are discussed as pathways to accelerate the rational design of 2D LMOs for flexible, energy-efficient, and multifunctional electronic applications. Exfoliation joint ; Fabrication ; van der Waals force ; Heterojunction ; Spintronics ; Coupling (piping) ; Charge (physics) ; Electronic structure 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Nanowire Synthesis and Applications
Authors: Garam Bae | Jae Hee Cho | Do‐Hyung Lee | Wooseok Song | Ki‐Seok An
Source: Surfaces and Interfaces (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Surface defect–mobility correlation in graphene FETs unveiled by Raman spectroscopy and machine learning Correlating surface defect characteristics with charge transport behavior is essential for evaluating the performance of graphene-based electronic devices. Here, we present a spectroscopy-informed machine learning framework that predicts electrical transport properties in graphene field-effect transistors (GFETs) by correlating Raman spectral features with device performance metrics. By extracting peak parameters—including intensity, position, full width at half maximum (FWHM), and area—from the D, G, D′, and 2D bands, a neural network regression model was trained to predict the field-effect mobility and charge-neutral Dirac point (CNDP) of chemically functionalized GFETs. Feature importance and partial dependence analyses revealed that the D′-band FWHM, a sensitive indicator of surface defect density, exhibits the strongest correlation with the predicted mobility values. Furthermore, spatial mapping of predicted mobility over device areas shows a high degree of correspondence with the D’-band width distribution, highlighting the capability of this approach for non-invasive, spatially resolved evaluation of device performance with high sensitivity to interfacial and surface defect variations. This work demonstrates a robust and scalable strategy for linking surface spectral fingerprints with charge transport behavior in 2D material-based transistors. Materials science ; Graphene ; Raman spectroscopy ; Spectroscopy ; Nanotechnology ; Surface (topology) ; Optoelectronics ; Chemical physics ; Optics Graphene research and applications ; Advanced Memory and Neural Computing ; Advancements in Semiconductor Devices and Circuit Design