jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Aghili | A. M. Bruce | Dubravko Sabo | Sanya Murdeshwar | Kevin Bachelor | Ionut Mistreanu | Ashwin Lokapally | Razvan Marinescu
Source: The Journal of Physical Chemistry B (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

A Standardized Benchmark for Machine-Learned Molecular Dynamics Using Weighted Ensemble Sampling The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comparison between simulation approaches is often hindered by inconsistent evaluation metrics, insufficient sampling of rare conformational states, and the absence of reproducible benchmarks. To address these challenges, we introduce a modular benchmarking framework that systematically evaluates protein MD methods using enhanced sampling analysis. Our approach uses weighted ensemble (WE) sampling via <i>The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis</i> (WESTPA), based on progress coordinates derived from <i>Time-lagged Independent Component Analysis</i> (TICA), enabling fast and efficient exploration of protein conformational space. The framework includes a flexible, lightweight propagator interface that supports arbitrary simulation engines, allowing both classical force fields and machine learning-based models. Additionally, the framework offers a comprehensive evaluation suite capable of computing more than 19 different metrics and visualizations across a variety of domains. We further contribute a data set of nine diverse proteins, ranging from 10 to 224 residues, that span a variety of folding complexities and topologies. Each protein has been extensively simulated at 300 K for one million MD steps per starting point (4 ns). To demonstrate the utility of our framework, we perform validation tests using classic MD simulations with implicit solvent and compare protein conformational sampling using a fully trained versus under-trained CGSchNet model. By standardizing evaluation protocols and enabling direct, reproducible comparisons across MD approaches, our open-source platform lays the groundwork for consistent, rigorous benchmarking across the molecular simulation community. Benchmark (surveying) ; Benchmarking ; Molecular dynamics ; Sampling (signal processing) ; Modular design ; Set (abstract data type) ; Protein structure prediction ; Variety (cybernetics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Shingo Urata
Source: Journal of the American Ceramic Society (article, 2025-12-16)
Field: Materials Science — Ceramics and Composites

Simulations of mechanical responses in v‐B <sub>2</sub> O <sub>3</sub> glass under indentation and shear deformation Abstract The boroxol ring is a planar three‐membered ring and represents a characteristic microstructure in vitreous borate glass (v‐). Molecular dynamics simulations of indentation were conducted using a machine‐learning force field to investigate the mechanical response of v‐ models containing different concentrations of boroxol rings. According to indentation simulations performed with a V‐shaped indenter, plastic deformation was observed regardless of the boroxol ring population in the glass models. A certain fraction of boroxol rings was disrupted during loading, and the ring structures were rarely reformed upon unloading the indenter. In addition, irreversible distortion of B–O–B and O–B–O bond angles was observed as a signature of plastic deformation. In contrast, increased boron coordination due to loading was largely recovered upon unloading. Furthermore, boron atoms rarely changed their coordination number under shear deformation, indicating that changes in boron coordination are not responsible for plastic deformation under shear deformation. Analysis of the cross‐correlation function of boroxol ring orientations has revealed that boroxol rings are unable to align their orientations when they are brought closer than approximately 3Å due to shear deformation. Consequently, we conclude that the slip motion of boroxol rings under shear deformation does not occur in v‐, which is in contrast to sodium borate glass, (, where slip motion has been suggested based on an experimental indentation study. Indentation ; Shear (geology) ; Plasticity ; Boron ; Molecular dynamics ; Deformation (meteorology) ; Microstructure ; Slip (aerodynamics) ; Rotation around a fixed axis Glass properties and applications ; Structural Analysis of Composite Materials ; High-pressure geophysics and materials
Authors: Revista, Zen | IA, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Structural Projection Networks: Advancing Analogical Abstraction for Generative Intelligence This paper introduces Structural Projection Networks (SPNs), a novel architectural paradigm designed to significantly advance analogical abstraction capabilities for generative artificial intelligence. Traditional deep learning models often struggle with robust generalization and creative synthesis when faced with novel problems requiring the transfer of relational structures from disparate domains. SPNs address this limitation by explicitly learning and projecting underlying structural invariants across different representational spaces. The core innovation lies in a dual-pathway architecture: one pathway focuses on extracting high-level conceptual features, while the other concurrently learns and disentangles the abstract relational graph characterizing the input. These two pathways are then integrated through a projection mechanism that maps relational structures from a source domain to a target domain, enabling the generation of outputs that exhibit deep analogical reasoning. We detail the theoretical foundations of SPNs, their architectural components including graph convolutional encoders, disentangled variational autoencoders, and cross-domain attention-based projectors, and propose a comprehensive training regimen involving structured datasets with known analogies. Empirical evaluation, even hypothetical in this conceptual paper, suggests SPNs can outperform existing generative models in tasks requiring complex analogical transfer, demonstrating superior performance in generating novel problem solutions, creative content, and scientific hypotheses that preserve structural consistency and semantic coherence. This work represents a significant step towards developing truly intelligent systems capable of human-like creative problem-solving and abstract thought. Abstraction ; Generative grammar ; Graph ; Generalization ; Domain (mathematical analysis) ; Dependency (UML) ; Graph rewriting ; Projection (relational algebra) Machine Learning in Materials Science ; Artificial Intelligence in Games ; Design Education and Practice
Authors: Qi Li (67548) | Junlei Zhao (1603582) | Na Lin (69470) | Xiufeng Cheng (1448872) | Xian Zhao (133743) | Zhaojun Liu (238486) | Zhitai Jia (6311210) | Mengyuan Hua (6619955)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Edge-DependentStep-Flow Growth Mechanism in β‑Ga<sub>2</sub>O<sub>3</sub> (100) Facet at the Atomic Level Homoepitaxial step-flow growth of high-quality β-Ga<sub>2</sub>O<sub>3</sub> thin films is essential for the advancement of high-performance Ga<sub>2</sub>O<sub>3</sub>-based devices. In this work, the step-flow growth mechanism of the β-Ga<sub>2</sub>O<sub>3</sub> (100) facet is explored by machine-learning molecular dynamics simulations and density functional theory calculations. Our results reveal that Ga adatoms and Ga–O adatom pairs, with their high mobility, are the primary atomic species responsible for efficient surface migration on the (100) facet. The asymmetric monoclinic structure of β-Ga<sub>2</sub>O<sub>3</sub> induces a distinct two-stage Ehrlich–Schwoebel barrier for Ga adatoms at the [00−1] step edge, contributing to the suppression of double-step and hillock formation. Furthermore, a miscut toward [00−1] does not induce the nucleation of stable twin boundaries, whereas a miscut toward [001] leads to the spontaneous formation of twin boundaries. This research provides meaningful insights not only for high-quality β-Ga<sub>2</sub>O<sub>3</sub> homoepitaxy but also the step-flow growth mechanism of other similar systems. Facet (psychology) ; Nucleation ; Hillock ; Molecular dynamics ; Mechanism (biology) ; Monoclinic crystal system ; Density functional theory Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Copper-based nanomaterials and applications
Authors: Mingyu Ye | Yang Pan | Rui Xu | Zezhou Xu | Jianmin Zhou | Xuan Luo | Yuxin Huang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Geographical Origin Discrimination and Quality Testing of Angelica dahurica Slices Based on Mid-Infrared Spectroscopy and Machine Learning Partial least squares regression ; Preprocessor ; Pattern recognition (psychology) ; Quality (philosophy) ; Medicinal herbs ; Chemometrics ; Principal component analysis ; Identification (biology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Z.-H. Pan | Maodong Li | Di Chen | Yi Yang
Source: ArXiv.org (preprint, 2025-06-20)
Field: Materials Science — Materials Chemistry

Great Restraining Wall in Multidimensional Collective Variable Space Enhanced sampling methods are pivotal for exploring rare events in molecular dynamics (MD), yet face challenges in high-dimensional collective variable (CV) spaces where exhaustive sampling becomes computationally prohibitive. While techniques like metadynamics (MetaD) and path-CV enable targeted free energy surface (FES) reconstruction, they often struggle with confinement stability, hyperparameter sensitivity, and geometric flexibility. This work introduces the Great Restraining Wall (GW) method, a robust framework for efficient FES sampling within predefined CV subspaces, addressing these limitations through a novel kernel density estimation (KDE)-derived restraining potential. GW operates by constructing a bias potential that confines sampling to user defined regions ranging from multidimensional masks to 1D pathways via asymptotically half-harmonic barriers. Unlike MetaD variants requiring iterative bias deposition, GW potential is derived from a cumulative distribution function, ensuring confinement without manual hyperparameter tuning. GW provides a versatile, stable, and efficient framework for targeted FES sampling, particularly beneficial for complex biomolecular systems with intricate CV landscapes. Its integration with existing enhanced sampling protocols opens avenues for studying ligand binding, conformational transitions, and other rare events with unprecedented precision. Future work will explore GW extension to adaptive regions and machine learning-guided CV discovery. Metadynamics ; Hyperparameter ; Sampling (signal processing) ; Variable (mathematics) ; Kernel (algebra) ; Rare events ; Work (physics) ; Importance sampling ; Space (punctuation) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Kil Mo Koo | Soo Min Park | Chul Hoon Park | Kwang Sik Yoon
Source:  (article, 2025-07-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

System Modeling of Multi-parameter Measurements by Magnetostrictive Sensors for Preliminary Analysis of IVR Phenomena A multi-parameter measurement system was modeled to analyze the in-vessel retention (IVR) phenomenon before the ex-vessel retention (EVR) of the reactor pressure vessel (RPV) as part of severe accident conditions (SAC) in nuclear power plants, utilizing the ultrasonic characteristics of magnetostrictive sensors (MS). The ultrasonic thermometry system (UTS), utilizing the magnetostrictive effect, is designed to simultaneously derive multiple parameters by extending the basic principle of material structure integrity assessment along with ultrasonic temperature measurement. The parameters used here are defined as the range of the mathematical logic function and will be extensively applied during the learning process of the machine learning model in the future. Through this system modeling, it is possible to predict real-time safety diagnostics and infer the stages of change based on variations in the physical parameters of a pressure vessel's internal state. This is achieved by analyzing the relationship between temperature, time delay, and distance (vessel thickness) inside the vessel in the event of a pressure vessel accident. Moreover, the ultrasonic signal affected by ultra-high temperature thermal disturbance noise is processed and analyzed by a high-speed digital signal processing (DSP) module as a signal synthesized with various types of noise patterns. In this paper, a multi-parameter measurement system is designed to enable expanded multi-parameter analysis as a preliminary experiment for the in-vessel retention (IVR) phenomenon in a small research reactor simulation pressure vessel. This is achieved by modeling a single sensor as a basic module and extending it into a multi-parameter measurement system based on an ultrasonic system. Systems modeling ; System dynamics ; Numerical modeling ; Work (physics) ; Magnetostriction Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Non-Destructive Testing Techniques
Authors: Alexa Canchola (9137734) | Keyuan Li (22349674) | Kunpeng Chen (8430642) | Alejandro Borboa-Pimentel (22349677) | Catherine Chou (20911545) | Roberto Dela Rama (22349680) | Chi-Yun Chen (2413396) | Xinyue Chen (439708) | Michael Strobel (17481825) | Jim E. Riviere (1368789) | Nancy A. Monteiro-Riviere (1747501) | Mingxun Wang (1327497) | Fan Zhang (46132) | Zhoumeng Lin (2399779) | Wei-Chun Chou (523810)
Source: Figshare (article, 2025-10-01)
Field: Materials Science — Biomaterials

Meta-Analysis andMachine Learning Prediction of ProteinCorona Composition across Nanoparticle Systems in Biological Media A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000–2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1–1400 nm), and ζ-potentials (−70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ζ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ζ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Nanoparticle ; Corona (planetary geology) ; Component (thermodynamics) ; Support vector machine ; Complement (music) ; Surface charge ; Predictive modelling Nanoparticle-Based Drug Delivery ; Proteins in Food Systems ; Polymer Surface Interaction Studies
Authors: Vignesh Jagajeevan | Vidhya Lakshmi Sivakumar | A Kandasamy | Pachaivannan Partheeban | Pooja Damodaran | M Nanthini
Source:  (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

High-Fidelity Prediction of Corrosion Kinetics in Organic Coatings: An Optimized Stacking Ensemble Framework The accurate prediction of corrosion kinetics in organic coatings is critical for the structural health monitoring of industrial infrastructure, yet traditional electrochemical modeling often fails to capture the non-linear degradation behavior of complex nanocomposites. This study proposes a robust machine learning framework to predict the corrosion current density (i_corr) of epoxy-based coating systems, specifically differentiating between pure epoxy, silicareinforced (Epoxy-SiO2), and silane-modified (Epoxy-SiO2APTES) nanocomposites. A comparative analysis was conducted across diverse predictive architectures, ranging from single-learner algorithms (SVR, XGBoost) to advanced ensemble configurations. The results establish the Sklearn_Stacking_Ridge architecture as the optimal predictive paradigm, achieving a superior Coefficient of Determination ($\mathbf{R}^{\mathbf{2}}$) of 0.9550 and a Root Mean Square Error (RMSE) of 0.0544. Unlike standalone models which exhibited heteroscedastic residual errors, the optimized stacking ensemble demonstrated exceptional structural stability, effectively isolating deterministic degradation patterns from stochastic noise. Permutation feature importance analysis revealed a distinct physical hierarchy, identifying temporal exposure as the dominant governing variable, followed immediately by coating material composition, confirming the model’s ability to distinguish superior barrier properties in APTES-modified matrices. While the stacking architecture incurs a threefold increase in computational latency compared to single learners, the cost-benefit analysis validates its deployment for high-fidelity service life prediction, offering a rigorous, data-driven approach to corrosion management in variable soil environments. Stacking ; Residual ; Corrosion ; Coating ; Feature (linguistics) ; Ensemble forecasting ; Ensemble learning Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Gaurav Prashar | Hitesh Vasudev
Source: Journal of Failure Analysis and Prevention (article, 2025-10-23)
Field: Materials Science — Ceramics and Composites

Process-Driven Phase Stability and Microstructural Integrity in Environmental Barrier Coatings: Implications for Failure Prevention Thermal barrier coating ; Coating ; Surface roughness ; Delamination (geology) ; Catastrophic failure ; Ceramic ; Environmental stress cracking ; Residual stress ; Surface finish ; Failure mode and effects analysis Advanced ceramic materials synthesis ; Advanced materials and composites ; High-Temperature Coating Behaviors
Authors: Minoosh Lalinia | Nahid Hassanzadeh Nemati | Tu C. Le | Nancy E. Dávila-Guzmán
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Optimizing the Design of Metal Organic Frameworks for Drug Delivery Applications Through Machine Learning Integration Drug delivery ; Computer science ; Drug ; Nanotechnology ; Metal-organic framework ; Biochemical engineering ; Risk analysis (engineering) ; Artificial intelligence ; Knowledge management ; Business ; Process management ; Engineering management ; Engineering ; Medicine ; Materials science ; Pharmacology ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Yongheng Li | Qiuchun Lu | Bin Wei | Cong Lu | Xingang Jiang | Taishun Manjo | Daisuke Ishikawa | Caofeng Pan | Alfred Q. R. Baron | Jiawang Hong
Source: Advanced Materials (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Incipient Ionic Conductors: Ion‐Constrained Lattices Achieving Superionic‐Like Thermal Conductivity Through Extreme Anharmonicity Phonon liquid-like thermal conduction in the solid state enables superionic conductors to serve as efficient thermoelectric device candidates. While liquid-like motion of ions effectively suppresses thermal conductivity (κ), their high mobility concurrently triggers material degradation due to undesirable ion migration and consequent metal deposition, making it a challenge to balance low κ and high stability. Here, phonon liquid-like thermal transport is reported alongside restricted long-range ion migration in CsCu<sub>2</sub>I<sub>3</sub> with incipient ionic conduction, using synchrotron X-ray diffraction, inelastic X-ray scattering, and machine-learning potential-based simulations. The Cu ions are revealed to exhibit confined migration between CuI<sub>4</sub> tetrahedra at high temperatures, displaying extreme anharmonicity of dominated phonons beyond conventional rattling and comparable to that in superionic conductors. Consequently, a glass-like κ (≈0.3 W m<sup>-1</sup> K<sup>-1</sup> at 300 K) following the relationship of κ ≈ T <sup>0.17</sup>, is achieved along the x-direction, where Cu ion migration is three orders of magnitude lower than in superionic conductors. These results highlight the advantage of incipient ionic conductors in simultaneously maintaining both low κ and high stability, elucidating the thermal transport mechanism via ion migration constraints, and paving an effective pathway toward ultralow thermal conductivity in ionic conductors. Anharmonicity ; Phonon ; Ionic conductivity ; Fast ion conductor ; Thermal conduction ; Ion ; Thermal conductivity ; Ionic bonding Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: P. Monigle | T. S. Hedayati | Frank J. Goulding
Source: AAPG Bulletin (article, 2025-05-01)
Field: Chemistry — Analytical Chemistry

Integrated and improved direct hydrocarbon indicators: A step forward in petroleum risk discrimination ABSTRACT In petroleum exploration, discriminating between success and failure is essential to constructing a successful exploration portfolio. Economic evaluations for individual prospects rely heavily on both chance of success (COS) and materiality to build a commercial portfolio. Where subsurface geophysical response is amenable, incorporating direct hydrocarbon indicator (DHI) observations have been shown to improve risk characterization and success rates. In recent years, industry has explored more challenging geological traps with subtle geophysical responses (i.e., Guyana–Suriname) that require evolving approaches. This paper describes a step change in the DHI evaluation process and integration with geological risk linked to volume estimation. More recently, the DHI evaluation process adopted expectation-based metrics and now leverages machine learning for scoring, which leads to improved accuracy, while mitigating human bias. These adaptations allow the process to be more broadly applicable across a global portfolio of prospects. Discernibility, an innovative metric, describes the expectations and confidence in geophysical observations and helps guide both risking and resource estimation. A significant challenge to predictability involves reconciling contradictory information between geological and geophysical observations. Applying the new integrated workflow, geoscientists integrate geological and geophysical observations through a Bayesian framework guided by discernibility. This framework allows for integration of all observations into a single COS value in a simple, repeatable manner. Benchmarking studies indicate that this new approach is ∼50% better at discriminating success from failure and is ∼30% more accurate over prior workflows. Petroleum ; Hydrocarbon ; Petroleum exploration ; Fossil fuel ; Hydrocarbon exploration Petroleum Processing and Analysis ; Global Energy and Sustainability Research ; Atmospheric and Environmental Gas Dynamics