jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tuğbey Kocabaş | Murat Keçeli | Tanju Gürel | M. V. Miloševıć | Cem Sevik
Source: Applied Physics Reviews (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Thermal conductivity limits of MoS2 and MoSe2: Revisiting high-order anharmonic lattice dynamics with machine learning potentials Group-VI transition metal dichalcogenides (TMDs), MoS2 and MoSe2, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics, optoelectronics, and thermoelectrics. However, their reported lattice thermal conductivities (κ) remain highly inconsistent, with experimental values and theoretical predictions differing by more than an order of magnitude. These discrepancies stem from uncertainties in measurement techniques, variations in computational protocols, and ambiguities in the treatment of higher-order anharmonic processes. In this study, we critically review these inconsistencies, first by mapping the spread of experimental and modeling results, and then by identifying the methodological origins of divergence. To this end, we bridge first-principles calculations, molecular dynamics simulations, and state-of-the-art machine learning force fields (MLFFs), including recently developed foundation models. We train and benchmark GAP, MACE, NEP, and HIPHIVE against density functional theory and rigorously evaluate the impact of third- and fourth-order phonon scattering processes on κ. The computational efficiency of MLFFs enables us to extend convergence tests beyond conventional limits and to validate predictions through homogeneous nonequilibrium molecular dynamics as well. Our analysis demonstrates that, contrary to some recent claims, fully converged four-phonon processes contribute negligibly to the intrinsic thermal conductivity of both MoS2 and MoSe2. These findings not only refine the intrinsic transport limits of 2D TMDs but also establish MLFF-based approaches as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials. Anharmonicity ; Phonon ; Thermal conductivity ; Molecular dynamics ; Bridging (networking) ; Lattice (music) ; Thermal ; Non-equilibrium thermodynamics ; Graphene Thermal properties of materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Sima Rezvantalab (9871962) | Sara Mihandoost (20420677) | Roger M. Pallares (5433686) | Fabian Kiessling (535383)
Source: Figshare (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Sequential EXtreme Gradient Boosting-Based DescriptorReduction for Size Prediction of Zwitterionic Polymer-Based Nanoparticles An approach based on machine learning (ML) is applied to investigate how structural descriptors affect the size of zwitterionic polymers (ZP) used in drug delivery systems (DDSs). This study examined the structural characteristics of components of ZP-based DDSs to determine how they contribute to the self-assembly phenomenon and, consequently, the final size of such DDSs. A new data set was curated through an extensive literature review, incorporating a wide range of descriptors related to molecular structures and pH. With each molecule initially characterized by 312 descriptors, reducing this extensive set to a manageable number emerged as a crucial step in the research process. To tackle this challenge, we introduced an innovative descriptor reduction strategy based on eXtreme Gradient Boosting (XGB), which we termed Sequential XGB (SXGB). This method was designed to systematically and efficiently identify the most significant descriptors while minimizing information loss. After applying the SXGB reduction process, the data set was streamlined to just 11 key descriptors, which were then used for further analysis. Among these, pH stood out as the most influential parameter, playing a pivotal role in the outcomes of the study. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) were employed to interpret a set of six local samples, allowing for the analysis of the influence exerted by each chosen descriptor. To enhance the robustness and generalizability of the model, we implemented a data augmentation process that combined both synthetic and original data sets for training and testing. This approach ensured that the model was exposed to a diverse range of scenarios, improving its ability to handle variability and uncertainty. In comparison with other evaluated models (supporting vector regression, random forest), the SXGB model demonstrated excellent performance in predicting zwitterionic polymer-based nanoparticle (NPs) size, achieving an accuracy (<i>R</i><sup>2</sup>) of 84.2% and 80.9% in the training and testing sections, respectively. Generalizability theory ; Robustness (evolution) ; Set (abstract data type) ; Boosting (machine learning) ; Reduction (mathematics) ; Training set ; Data set ; Data-driven ; Process (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Manisha R. Bhosle | Aarti V. Belambe | Chhaya S. Pawar | Garima K. Shekhawat | Vinay Kumar Yadav | Sudipta Bhattacharyya
Source: Research on Chemical Intermediates (article, 2025-12-01)
Field: Chemistry — Organic Chemistry

Aqueous micellar catalysis for the efficient synthesis of chromeno-pyrazolo[1,2-b]phthalazines and indazolo[1,2-b]phthalazines, their plausible therapeutic implications and DFT investigations Pharmacophore ; Catalysis ; Molecule ; In silico ; Ligand (biochemistry) ; Density functional theory ; Molecular dynamics ; Small molecule Synthesis and biological activity ; Melanoma and MAPK Pathways ; Multicomponent Synthesis of Heterocycles
Authors: R. Pranav | Bhargav Akkinepally | Mohan Rao Tamtam | Nagaraju Macherla | Jaesool Shim
Source: International Journal of Hydrogen Energy (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Predicting MXenes hybrid functional bandgaps for photocatalytic applications using deep neural networks MXenes ; Band gap ; Electronegativity ; Photocatalysis ; Artificial neural network ; Carbide ; Hybrid functional MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Ignacio Formoso
Source: Fibers and Polymers (article, 2025-04-10)
Field: Materials Science — Polymers and Plastics

Optimizing Melt-Blowing Nozzles for Small-Diameter Fibers: An Artificial Neural Network Framework Materials science ; Artificial neural network ; Nozzle ; Composite material ; Mechanical engineering ; Artificial intelligence ; Computer science Textile materials and evaluations ; Radiative Heat Transfer Studies ; Polymer Foaming and Composites
Authors: Kamalini Devi | K. DevakiDevi
Source: Microchemical Journal (article, 2025-11-02)
Field: Materials Science — Polymers and Plastics

AI-optimized kenaf/flax epoxy composites with tamarind shell for sustainable sensors Epoxy ; Flexural strength ; Ultimate tensile strength ; Izod impact strength test ; Composite number ; Kenaf ; Fourier transform infrared spectroscopy ; Fiber Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-19)
Field: Materials Science — Surfaces, Coatings and Films

Automated Artifact Segmentation and Compositional Analysis in Thermo Apreo 2 SEM Images via Bayesian Hierarchical Modeling and Deep Feature Fusion **Abstract:** This research introduces a novel framework for automated artifact segmentation and compositional analysis in Thermo Apreo 2 SEM images. Current analysis relies heavily on manual inspection, a process prone to subjectivity and limited by throughput. Our approach combines Bayesian Hierarchical Modeling (BHM) with deep feature fusion from pre-trained convolutional neural networks (CNNs) to accurately delineate and classify artifacts within high-resolution SEM micrographs. The system achieves a 25% improvement in segmentation accuracy and a 15% increase in throughput compared to manual analysis, enabling faster and more objective materials characterization. The framework is readily deployable within existing Apreo 2 workflows and holds significant potential for optimizing quality control and accelerating materials discovery. Segmentation ; Convolutional neural network ; Artifact (error) ; Pattern recognition (psychology) ; Feature (linguistics) ; Workflow ; Process (computing) ; Bayesian probability ; Image segmentation Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Wenbo Zhao | Ying Sun | Jiaxiang Li | Peng Yuan | Toshiaki Iitaka | Xin Zhong | Hefei Li | Yue‐Wen Fang | Hanyu Liu | Ion Errea | Yu Xie
Source: npj Computational Materials (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

The impact of ionic anharmonicity on superconductivity in metal-stuffed B-C clathrates Metal-stuffed B–C compounds with sodalite clathrate structure have captured increasing attention due to their predicted exceptional superconductivity above liquid nitrogen temperature at ambient pressure. However, by neglecting the quantum lattice anharmonicity, the existing studies may result in an incomplete understanding of such a lightweight system. Here, using state-of-the-art ab initio methods incorporating quantum effects and machine learning potentials, we revisit the properties of a series of XYB6C6 clathrates where X and Y are metals. Our findings show that ionic quantum and anharmonic effects can harden the Eg and Eu vibrational modes, enabling the dynamical stability of 15 materials previously considered unstable in the harmonic approximation, including materials with previously unreported (XY)1+ state, which is demonstrated here to be crucial to reach high critical temperatures. Further calculations based on the anisotropic Migdal-Eliashberg equation demonstrate that the Tc values for KRbB6C6 and RbB3C3 among these stabilized compounds are 102 and 115 K at 0 and 15 GPa, respectively, both being higher than Tc of 92 K of KPbB6C6 at the anharmonic level. These record-high Tc values, surpassing liquid nitrogen temperatures, emphasize the importance of anharmonic effects in stabilizing B-C clathrates with large electron-phonon coupling strength and advancing the search for high-Tc superconductivity at (near) ambient pressure. Anharmonicity ; Superconductivity ; Ionic liquid ; Ab initio ; Quantum ; Sodalite ; Anisotropy Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; Rare-earth and actinide compounds
Authors: Nayara Carral-Sainz | Toraya Fernández-Ruiz | Jorge Íñiguez | Javier Junquera | Pablo García‐Fernández
Source: ArXiv.org (preprint, 2025-05-28)
Field: Materials Science — Materials Chemistry

Systematic generation of electron models for Second-Principles Density Functional Theory Methods We present a systematic, quasi-automated methodology for generating electronic models in the framework of second-principles density functional theory (SPDFT). This approach enables the construction of accurate and computationally efficient models by deriving all necessary parameters from first-principles calculations on a carefully designed training set. A key feature of our method is the enforcement of space group symmetries, which reduces both the number of independent parameters and the required computational effort. The formalism includes improved treatments of one-electron Hamiltonians, electron-lattice coupling-through both linear and quadratic terms-and electron-electron interactions, enabling accurate modeling of structural and electronic responses. We apply the methodology to SrTiO$_{3}$ and LiF, materials representative of transition-metal perovskites and wide-band-gap insulators, respectively. In both cases, the resulting models reproduce DFT reference data with high fidelity across various atomic configurations and charge states. Our results validate the robustness of the approach and highlight its potential for simulating complex phenomena such as polarons and excitons. This work lays the foundation for extending SPDFT to real-time simulations of optoelectronic properties and further integration with machine-learning methods. Density functional theory ; Formalism (music) ; Robustness (evolution) ; Quadratic equation ; USable ; Polaron ; Electronic structure ; Fidelity Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum many-body systems
Authors: Fabiola Eugelio (12251623) | Marcello Mascini (12251620) | Elettra Marone (8376069) | Federico Fanti (184567) | Sara Palmieri (9321238) | Manuel Sergi (1572616) | Michele Del Carlo (22188698) | Dario Compagnone (1442917)
Source: Figshare (article, 2025-09-05)
Field: Chemistry — Organic Chemistry

Phenolic Profileas a Powerful Machine Learning Toolfor Identification, Traceability, and Quality Control of Olive Cultivars This study investigates the phenolic and fatty acid profiles of olives from four <i>Olea europaea</i> cultivars (Arbequina, Arbosana, Frantene, and Koroneiki), widely grown in the Mediterranean region and collected at different ripening stages in Italy. The aim was to assess the potential of olive chemical profiles as markers for cultivar classification using machine learning algorithms, including Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results showed that phenolic profiling achieved significantly higher classification accuracy than fatty acids across all models. Using phenolic data, RF and SVM achieved 98% accuracy, while for fatty acids, the best-performing model was NB, reaching just 65%. Importantly, minor phenolic compounds such as chlorogenic acid, ferulic acid, and apigenin were crucial for classification. These findings demonstrate that machine learning combined with phenolic profiling can improve cultivar identification, traceability, and quality control in the olive sector. Cultivar ; Support vector machine ; Ferulic acid ; Naive Bayes classifier ; Chlorogenic acid ; Linear discriminant analysis ; Ripening ; Linoleic acid Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Boisseau, Rene Damaso
Source: scholarworks - UTEP (The University of Texas at El Paso) (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

De Novo Design Of Ablative Polymers For Aerospace Thermal Protection Systems Through An Integrated AI-Theory-Experiment Approach Novolac phenolic resin (NPR) is a widely used polymeric ablative material (PAM) for thermal protection systems (TPS) due to its high thermal decomposition temperature (TD, 500â?¯Â°C) and char yield (YC, 55â?¯wt%). However, its drawbacks (including high thermal conductivity, low mechanical properties and water uptake) prompt the need for improved PAMs. This study explores the pyrolysis behavior and char-forming capabilities of NPR and a potential alternative PAM, poly(1,6-dimethyl phenol) (PPO), through both theoretical and experimental approaches. A graph neural network (GNN) was developed to predict structure-property relationships, revealing that benzyl group positioning and oxygen atom placement significantly influence YC. PPO's para-positioned chains and second methyl group also hinder its char formation. Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) confirmed distinct decomposition products between NPR and PPO, correlating with their TD differences. Density functional theory (DFT) showed NPR requires higher energy for initial dissociation (92.4 vs. 76.4â?¯kcal/mol), contributing to its superior thermal resistance and TD. The experimental study and molecular dynamics (MD) simulations revealed that NPR forms large polycyclic structures, while PPO forms smaller ones, limiting char formation. DFT mechanistic analysis indicated that NPR undergoes intramolecular cyclization to tricyclic intermediates regardless of the initiation pathway (bond fission or H-abstraction). On the other hand, PPO forms similar structures only under H-abstraction due to higher bond energy barriers encountered through bond fission. The key to the formation of polycyclic compounds is the radical center to the adjacent aromatic ring distance. Finally, poly(diphenyl phenol) (PPPO), a PPO analog, demonstrated better thermal properties (TD 530â?¯Â°C, YC 45â?¯wt%) by enabling cyclization through phenyl substitution. Overall, the study underscores that char yield depends on a polymer's ability to generate radicals capable of cyclization, providing valuable insights for the rational design of next-generation PAMs. Char ; Thermal decomposition ; Pyrolysis ; Polymer ; Density functional theory ; Yield (engineering) ; Intramolecular force ; Tandem ; Thermal ; Decomposition Thermal properties of materials ; Epoxy Resin Curing Processes ; Gas Dynamics and Kinetic Theory
Authors: Hengwei Xu | Yonggang Lu | Lei Guo | Junrun Li | Xiaowei Feng | Zhengcao Lu
Source: Defence Technology (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Physics-informed neural network model for predicting projectile mass erosion integrating cutting and thermal melting mechanisms High-velocity penetration of projectiles into concrete induces intense thermomechanical interactions that lead to substantial projectile mass erosion, thereby compromising structural integrity and ballistic stability. Accurate mass erosion prediction is therefore critical for warhead lethality assessment. To address this challenge, we develop a physics-informed neural network (PINN) model for predicting projectile mass erosion at velocities ranging from 345 m/s to 1852 m/s. The model integrates physics-based constraints from cutting and thermal melting mechanisms through a composite loss function. Data augmentation and dimensionality reduction techniques are applied to improve model generalization across diverse impact conditions. Training and validation are conducted on an augmented dataset comprising 283 samples, derived from 10 published studies. The model’s generalization capability is rigorously evaluated on an independent set of 13 new penetration experiments with ogive-nosed projectiles made of three distinct materials. Compared to a purely data-driven neural network (NN) model, the PINN model reduces the average relative error on the validation set from 16.3% to 14.5% and improves the proportion of predictions within a 20% relative error margin from 67.3% to 71.2%. On an independent test set, the PINN model demonstrates superior accuracy over both the data-driven neural network model and conventional theoretical models, achieving an average relative error of 11.7%, with 38.5% and 84.6% of the predictions falling within 10% and 20% relative error margins, respectively. By enabling accurate mass erosion prediction under high-velocity conditions, the proposed methodology supports weight-optimized penetrator design and directly contributes to terminal effectiveness improvement. Projectile ; Artificial neural network ; Approximation error ; Generalization ; Penetration depth ; Set (abstract data type) ; Thermal ; Curse of dimensionality ; Reduction (mathematics) High-Velocity Impact and Material Behavior ; Erosion and Abrasive Machining ; Nuclear Materials and Properties
Authors: Ting Su | Chao Rong | Yabin Yan | Fu‐Zhen Xuan
Source: ACS Applied Materials & Interfaces (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Tensile Mechanical Properties and Edge Defect-Driven Degradation in Bilayer Graphene Bilayer graphene attracts significant attention due to its unique electronic structure and excellent physical properties, with its mechanical performance being crucial for understanding deformation mechanisms and assessing application reliability. The mechanical properties of bilayer graphene measured by atomic force microscopy currently exhibit considerable scatter and show clear deviations from theoretical predictions. In situ tensile testing is widely regarded as a more reliable and authoritative approach for evaluating the mechanical properties of two-dimensional materials. Accordingly, the Young's modulus of bilayer graphene is measured to be 873.80 ± 12.68 GPa using a push-to-pull device inside a scanning electron microscope, which is close to the theoretical value. Moreover, the integration of bilayer graphene into device architectures requires micro/nanoscale patterning and shaping, which inevitably introduces edge defects. However, it remains experimentally challenging to precisely control the concentration of these edge defects. To address these limitations, a combined approach of molecular dynamics simulations and machine learning was employed to systematically uncover the effects of edge defects on the mechanical behavior of bilayer graphene. This study provides a theoretical foundation for a deeper understanding and optimization of the mechanical behavior of bilayer graphene, thereby laying important groundwork for its application in microelectronic devices. Materials science ; Graphene ; Ultimate tensile strength ; Degradation (telecommunications) ; Composite material ; Bilayer ; Enhanced Data Rates for GSM Evolution ; Nanotechnology ; Membrane ; Electronic engineering Graphene research and applications ; 2D Materials and Applications ; Graphene and Nanomaterials Applications
Authors: Felix Rummel | Patrick B. Warren | David J. Bray | Zeynep Sumer | Jonathan Booth | Ardita Shkurti | R. L. Anderson
Source: Scientific Data (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

A dataset for aqueous surfactant phase behavior as a function of temperature and composition Presented here is a dataset (PhDat) discretizing the aqueous phase behavior of 143 surfactants as a function of temperature and composition. Across the complete dataset, we classify the discretized state points into 118 distinct possible phase states, comprising both single- and two-phase regions, taking a probabilistic approach to describe phase transitions and narrow biphasic gaps. We also outline the workflow adopted to obtain the digitized phase diagrams. We anticipate this dataset will be useful for machine learning or similar applications, in the practically important field of surfactant formulation. The dataset has been designed to be extensible such that it can accommodate a wider variety of surfactant mixtures or non-surfactant molecule phase diagrams. Pulmonary surfactant ; Phase (matter) ; Aqueous solution ; Workflow ; Discretization ; Function (biology) ; Probabilistic logic ; Variety (cybernetics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization