jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Agil Ramadhani | Sutrisno Sutrisno
Source: International Journal of Marine Engineering and Applications (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

DESIGN OF THE MAIN ENGINE FOUNDATION OF SHIP X TO SUPPORT MAIN ENGINE REPOWERING Ship “X” is one of the motor ships built in 1974 and is of the passenger type. Repowering is planned because the current speed is deemed insufficient to meet the expected operational targets. The ship uses the previous main engine with a capacity of 4 x 1340 HP to 2 x 2600 HP. An important part of the main engine is the foundation located at the bottom, because the foundation is attached to the main engine with a large power, the foundation must effectively ensure the safety of the hull structure to withstand a wide variety of forces that can be provided by loads on the foundation. The engine foundation must be able to withstand the load on it without causing shear stress, as if the engine has become an integral part of the ship itself. Given that this will be done, a new main engine foundation design process will be made. There are two types of materials simulated, namely bki grade A and B standard steel, with two loads during operation, namely a fixed load of 29,577.15 N and a total load of 165,128.41 N. In carrying out the research, modeling and simulation were carried out with Autodesk Inventor software. So, this research produces the most efficient material, namely bki grade A steel, because it has a greater yield strength and tensile strength. The material has simulation results with minimal and maximum difference values in %, Stress XY 0.56%, Stress Z 0, 11%, Stress YY 0.11%, Stress stress and strain of Von Mises Stress 0.05%, 1st Principal Stress 0.18%, 3rd Principal Stress 0.18%, Stress XX 0.03YZ 0.02%, Stress ZZ 0.07, Equivalent Strain 0.44%, 1st Principal Strain 0.46%, 3rd Principal Strain 12.25%, Strain XX 0.02%, Strain XY 0.18%, Strain XZ 0.09, Strain YY 0.22%, Strain YZ 0.36%, Strain ZZ 0.09%. Hull ; Foundation (evidence) ; Stress (linguistics) ; Engine power ; Shear stress ; von Mises yield criterion ; Stress–strain curve Material Selection and Properties ; Engineering Structural Analysis Methods ; Ship Hydrodynamics and Maneuverability
Authors: Jaroon Rungamornrat | Van-Loi Nguyen | Minh-Tu Tran
Source: Tạp chí Khoa học Công nghệ Xây dựng (KHCNXD) - ĐHXDHN (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Static bending and buckling analyses of GPL-FGM microplates placed on Winkler-Pasternak elastic foundation In this article, the static bending and buckling behaviors of microplates resting on a Winkler-Pasternak elastic foundation is studied. The microplate model is composed of metal-ceramic functionally graded material (FGM) reinforced by graphene nanoplatelets (GPLs), forming a GPL-FGM microplate. The FGM matrix properties vary according to the power-law distribution model, whereas the GPLs are distributed following five patterns (D1, D2, D3, D4 and D5) across the thickness. The governing equations for static bending and buckling analyses of the microplate model are derived utilizing the modified couple stress theory (MCST), four-unknown refined plate theory, and the Ritz method. The proposed solution is verified through comparison with existing results from the literature, and influence of some key factors on the non-dimensional deflection and the critical buckling load of the microplate are studied. Buckling ; Deflection (physics) ; Bending ; Ritz method ; Critical load ; Foundation (evidence) ; Pure bending Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Composite Material Mechanics
Authors: Aradhna Mishra
Source: International Journal of Materials Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Atomistic insights into electron-phonon coupling in carbon-based nanomaterials Carbon-based nanomaterials, such as carbon nanotubes (CNTs) and graphene nanoribbons (GNRs), exhibit unique electronic properties influenced by electron-phonon (e-ph) coupling. This study provides atomistic insights into e-ph interactions in a (10, 0) zigzag CNT and a10-armchair GNR using full-band tight-binding simulations and phonon dispersion analysis. We quantify scattering rates, mobility, and thermal conductivity, revealing that CNTs achieve lower e-pH. scattering (~1012-1013s?1)due to tubular symmetry, yielding a mean free path of ~100 nm and thermal conductivity of ~2000 W/m· K. GNRs, however, experience enhanced scattering (~1.2 × 1013s?1) from edge-localized phonons, reducing mobility (~60 nm) and conductivity (~1500 W/m·K). At 300 K, phonon scattering dominates, with GNRs showing greater degradation due to edge effects, as depicted in Fig. 1 (phonon DOS). Lowering temperature to 77 K reduces scattering by 50%, improving transport in both systems, though GNRs remain edge-limited. These findings elucidate the role of atomic structure in e-pH. Coupling, suggesting edge passivation for GNR optimization and highlighting CNTs’ superiority in high-mobility applications. This work offers a framework for tailoring carbon nanomaterials for nanoelectronics and thermal management, addressing efficiency and scalability challenges. Nanomaterials ; Coupling (piping) ; Phonon ; Chemical physics ; Carbon fibers ; Materials science ; Nanotechnology ; Physics ; Condensed matter physics Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications
Source:  (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

STRESS-STRAIN STATE CALCULATIONS FOR THE SOIL BASE OF THE SLAB FOUNDATION OF A HIGH-RISE BUILDING Foundation (evidence) ; Slab ; Base (topology) ; Stress–strain curve ; Structural engineering ; Stress (linguistics) ; State (computer science) ; Geotechnical engineering ; Geology ; Materials science ; Engineering ; Computer science ; Finite element method ; Geography ; Archaeology ; Mathematics ; Philosophy Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Geotechnical and Geomechanical Engineering
Source:  (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

STRESS-STRAIN STATE CALCULATIONS FOR THE SOIL BASE OF THE SLAB FOUNDATION OF A HIGH-RISE BUILDING Foundation (evidence) ; Slab ; Base (topology) ; Geotechnical engineering ; Structural engineering ; Stress–strain curve ; Stress (linguistics) ; Strain (injury) ; State (computer science) ; Geology ; Materials science ; Engineering ; Finite element method ; Computer science ; Mathematics ; Political science ; Mathematical analysis ; Philosophy ; Law ; Biology Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Geotechnical and Geomechanical Engineering
Authors: Khalid Ali Alanezy | Jihad Souissi
Source: Axioms (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Optimal Boundary-Flux Control of a Sharp Moving Interface in the Classical Two-Phase Stefan Problem In this paper, we study the optimal boundary control of solidification governed by the classical two-phase Stefan problem with a sharp moving interface. The main objective is to formulate an optimal control problem for interface motion using boundary heat-flux control. The control acts as a Neumann heat flux on a designated boundary segment and steers the interface through the Stefan condition. Using an enthalpy formulation, we prove well-posedness under boundary control and establish Lipschitz continuity of the control-to-state map and continuous dependence on the initial data. We then derive first-order necessary optimality conditions using a Lagrangian approach and propose a practical algorithm that couples a semismooth Newton method with Sequential Quadratic Programming (SQP) to compute the optimal boundary flux. Stefan problem ; Boundary (topology) ; Optimal control ; Lipschitz continuity ; Free boundary problem ; Interface (matter) ; Quadratic equation ; Neumann boundary condition Solidification and crystal growth phenomena ; Advanced Numerical Methods in Computational Mathematics ; Numerical methods in inverse problems
Authors: Md S Alieozaman | Megha Roy
Source: Journal of Current Research in Food Science (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

An overview of the impact of lotus seeds on weight management and brain health Lotus Seeds are commonly referred to as makhana or fox nuts and today many people are aware of the enormous health benefits of lotus seeds ranging from the slimming effect to boosting brain activity. These seeds are enriched with protein, fibre and have low-calorie content making them ideal for any diet, especially for those who are aimed to weight loss. Different studies have shown that this high-fibre food causes fullness feeling and having them as a healthy snack comprises less intake of calories in general. Also, they are low in glycaemic index which makes them to balance the blood sugar level to prevent the feeling of hunger while eating. Many research papers suggested that for boosting brain health, lotus seeds contain essential antioxidant compounds such as Kaempferol and flavonoids which facilitate the protection of the brain from oxidative stress. They also have thiamine, which is important in the functioning of the brain and the assisting of the transmission of signals through the brain that helps in enhancing memory ad concentration. Lotus seeds are useful in lowering inflammation in the brain and body and therefore decrease the risk of neurodegenerative diseases such as Alzheimer’s. These seeds can actually be taken to enhance sleep especially due to stress hence indirectly enhancing mental health. Lotus ; Weight management ; Environmental science ; Medicine ; Biology ; Botany ; Obesity ; Weight loss ; Internal medicine Chromatography in Natural Products ; Ginseng Biological Effects and Applications ; Phytochemical Studies and Bioactivities
Authors: Ivan Solodei | Gherman Zatyliuk | V. M. Konoval
Source: Strength of Materials and Theory of Structures (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Study of the stress–strain state of the «Soil – Foundation -Structure» system with consideration of genetic nonlinearity The study examines the features of modeling the stress–strain state of the “soil–foundation–structure” system with consideration of genetic nonlinearity, which reflects the sequential changes of the calculation scheme during the construction of a building. It is shown that traditional linear calculations do not fully represent the actual processes of interaction between structures and the soil foundation. Three calculation models were considered for the analysis: a traditional scheme with “instantaneous” load application; a scheme with genetic nonlinearity but fixed vertical dimensions of the foundation model; and a scheme with genetic nonlinearity combined with refinement of the deformation properties of the foundation by adjusting the compressible layer thickness at each stage of construction. The results demonstrated that Model 2 leads to overestimated settlement values at the initial stages, resulting in a conservative outcome. The most realistic results were obtained with Model 3, which accounts for changes in the compressible layer thickness at each construction stage. This approach makes it possible to reduce not only the absolute settlement values but also the relative deformations of the foundation slab, thereby decreasing stress levels in the underground structural elements of the building and revealing certain reserves in their performance. Foundation (evidence) ; Settlement (finance) ; Nonlinear system ; Compressibility ; Scheme (mathematics) ; Deformation (meteorology) Structural mechanics and materials ; Thoreau and American Literature ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Taras Svyshch | Oleksii Lanets | Oleksii Vambol | Iryna Derevenko
Source: Ukrainian Journal of Mechanical Engineering and Materials Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

SUBSTANTIATION OF THE ADVANTAGES OF USING PLANE ELASTIC ELEMENTS IN THE RESONANT DESIGN OF A VIBRATING TABLE OF THE ECCENTRIC-PENDULUM TYPE This article presents an analytical and engineering justification for the use of flat elastic elements in eccentric-pendulum-type resonant vibration tables. The study addresses the design, calculation, and structural optimization of resonant elastic nodes, which ensure directional oscillations, define dynamic performance, and determine the durability and reliability of the machine. Stiffness and strength calculation procedures are derived for elastic components of rectangular and circular cross-sections, considering material properties, geometric parameters, and stress constraints under cyclic loading. Analytical results indicate that circular elements experience higher bending stresses than rectangular ones for equivalent stiffness, requiring increased length to achieve comparable durability. Finite-element simulations validate these findings, confirming the superior structural efficiency, lower operating stresses, and enhanced geometric adaptability of flat elements. The research provides a rigorous basis for selecting flat elastic components in the design of high-performance resonant vibration systems. Stiffness ; Vibration ; Bending ; Stress (linguistics) ; Basis (linear algebra) ; Plane (geometry) ; Reliability (semiconductor) ; Type (biology) Structural mechanics and materials ; Engineering and Materials Science Studies ; Mechanics and Biomechanics Studies
Authors: Jonas Elsborg | Arghya Bhowmik | Bhowmik, Arghya
Source: ArXiv.org (preprint, 2025-11-15)
Field: Materials Science — Materials Chemistry

Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles We approach the search for optimal element ordering in bimetallic alloy nanoparticles (NPs) as a reinforcement learning (RL) problem and have built an RL agent that learns to perform such global optimization using the geometric graph representation of the NPs. To demonstrate the effectiveness, we train an RL agent to perform composition-conserving atomic swap actions on the icosahedral nanoparticle structure. Trained once on randomized $Ag_{X}Au_{309-X}$ compositions and orderings, the agent discovers previously established ground state structure. We show that this optimization is robust to differently ordered initialisations of the same NP compositions. We also demonstrate that a trained policy can extrapolate effectively to NPs of unseen size. However, the efficacy is limited when multiple alloying elements are involved. Our results demonstrate that RL with pre-trained equivariant graph encodings can navigate combinatorial ordering spaces at the nanoparticle scale, and offer a transferable optimization strategy with the potential to generalize across composition and reduce repeated individual search cost. Reinforcement learning ; Swap (finance) ; Nanoparticle ; Bimetallic strip ; Alloy ; Graph ; Reinforcement ; Representation (politics) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Electrocatalysts for Energy Conversion
Authors: Haifan Zhou (17701187) | Yue Fang (124565) | Linyan Li (757342) | Pingwei Liu (1757221) | Hanyu Gao (1236072)
Source: Figshare (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

De Novo Designof Polymers with Specified PropertiesUsing Reinforcement Learning Designing polymers with specified properties is crucial for industries such as aerospace, automotive, and construction, where high yield strength is necessary for stability and performance. Traditionally, the design of polymers has relied on decades of trial-and-error experiments that are time-consuming and inefficient. While recent advancements in computational methods have emerged as promising tools for polymer design, they predominantly focus on property prediction or unbiased polymer generation and do not fully progress toward the tailored design of novel polymer structures that meet specific performance criteria. To accelerate the exploration of new high-performance polymers, we proposed Reinforcement Learning for Polymer Generation (RLPolyG), an integrated goal-oriented exploration workflow for de novo polymer design with specified properties. This framework employs a forward model for predicting polymer properties and an inverse model optimized via reinforcement learning to generate polymers with specific yield strength. Our forward model achieved an <i>R</i><sup>2</sup> of 0.84 in predicting yield strength, enabling the inverse model to generate 4991 novel polymer candidates, resulting in a significant 45.20% improvement in average yield strength. We further screened these candidates based on synthetic accessibility (SA) scores and degradability, identifying 3099 polymers with excellent feasibility for synthesis and degradation performance. Finally, we validated the nine top-performing polymers through molecular dynamics (MD) simulations, which showed an average related error of 14.64% between the predicted and MD-validated values. This work demonstrates the potential of using reinforcement learning to transform polymer design, providing a systematic and efficient pathway to explore the vast polymer space and accelerate the discovery of materials tailored to meet specific industrial needs. Reinforcement learning ; Polymer ; Stability (learning theory) ; Workflow ; Yield (engineering) ; Inverse Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Hamza Elkhoukhi | Frédéric Chapelle | Youcef Mezouar | Y. Lapusta
Source: Sensors and Actuators A Physical (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Reinforcement learning-based control of antagonistic shape memory alloy actuators Reinforcement learning ; SMA* ; Displacement (psychology) ; Actuator ; Flexibility (engineering) ; Shape-memory alloy ; Finite element method ; Nonlinear system ; Control theory (sociology) Shape Memory Alloy Transformations ; Aeroelasticity and Vibration Control ; Piezoelectric Actuators and Control
Authors: Aisha Samreen | Muhammad Fikril Azim | Fuyi Chen
Source: AI & Materials (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review Reinforcement learning ; Reinforcement ; Computer science ; Materials science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Y. Liu | Zhifeng Gao | Lirong Wu | Jingbo Zhou | T. D. Xu | Cheng Tan | Yufei Huang | Changxi Chi | Chang Wu Yu | Stan Z. Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

VitaMol: Lightweight Molecular Modality Expansion in Discrete Space for Multi-Task Transfer Learning Modality (human–computer interaction) ; Space (punctuation) ; Transfer (computing) ; Transfer of learning ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Domain Adaptation and Few-Shot Learning
Authors: Johann L. Rapp | Dylan M. Anstine | Filipp Gusev | Filipp Nikitin | Kyeong‐Yeol Yun | Meredith A. Borden | Vittal Bhat | Olexandr Isayev | Frank A. Leibfarth
Source: Angewandte Chemie (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning Abstract The development of high‐performance elastomers for additive manufacturing requires overcoming complex property trade‐offs that challenge conventional material discovery pipelines. Here, a human‐in‐the‐loop reinforcement learning (RL) approach is used to discover polyurethane elastomers that overcome pervasive stress–strain property tradeoffs. Starting with a diverse training set of 92 formulations, a coupled multi‐component reward system was identified that guides RL agents toward materials with both high strength and extensibility. Through three rounds of iterative optimization combining RL predictions with human chemical intuition, we identified elastomers with more than double the average toughness compared to the initial training set. The final exploitation round, aided by solubility prescreening, predicted twelve materials exhibiting both high strength (&gt;10 MPa) and high strain at break (&gt;200%). Analysis of the high‐performing materials revealed structure‐property insights, including the benefits of high molar mass urethane oligomers, a high density of urethane functional groups, and incorporation of rigid low molecular weight diols and unsymmetric diisocyanates. These findings demonstrate that machine‐guided, human‐augmented design is a powerful strategy for accelerating polymer discovery in applications where data is scarce and expensive to acquire, with broad applicability to multi‐objective materials optimization. Elastomer ; Loop (graph theory) ; Reinforcement ; Materials science ; Polymer science ; Reinforcement learning ; Human-in-the-loop ; Composite material ; Nanotechnology ; Computer science ; Human–computer interaction ; Artificial intelligence Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Memory and Neural Computing
Authors: Junhao Liang (9691448) | Caiyuan Ye (22084515) | Xinyi Lin (1383327) | Chunlin Yu (4989818) | Yilimiranmu Rouzhahong (5317541) | Chao Liang (420436) | Huashan Li (1423429)
Source: Figshare (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Machine LearningIntegrating Surface Features and Crystal Similarityfor Exploring 2D Materials The property prediction of two-dimensional (2D) materials has been constrained to specific systems or the highly self-similar Computational 2D Materials Database (C2DB) due to limited data availability and inadequate feature extraction. These challenges impede extrapolated predictions and large-scale exploitation across diverse databases. We developed a model named crystal surface and cluster network (CCSN) that integrates crystal surface and cluster features while leveraging transfer learning from bulk material databases to enhance model performance. Compared to the widely adopted CGCNN model, our approach achieved a 30% reduction in mean absolute error for bandgap prediction on the Materials Cloud 2D crystals database, characterized by low self-similarity and limited data volume. To enable extrapolated predictions, we developed a prediction workflow based on crystal similarity, which selects the most similar database for model training and determines the necessity of applying transfer learning. This method was applied to predict bandgaps of 8,218 crystals without bandgap values in the C2DB database and subsequently validated through our DFT calculations. The proposed prediction workflow, based on the CCSN model, would enable the discovery of 2D materials through rapid property prediction and can be generalized to improve any prediction models dealing with scarce or biased data sets. Workflow ; Dimensionality reduction ; Property (philosophy) ; Crystal (programming language) ; Cluster analysis ; Similarity (geometry) ; Feature (linguistics) ; Surface (topology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications