jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Harkirat Singh | M. Nikhil Raj | Kaushik Bhattacharya
Source: ArXiv.org (preprint, 2025-07-05)
Field: Materials Science — Materials Chemistry

Effective behavior of heterogeneous media governed by strain gradient elasticity Various mechanical phenomena depend on the length scale, and these have inspired a variety of nonlocal and higher gradient continuum theories. Mechanistically, it is believed that the length scale dependence arises due to an interplay between the length scale of heterogeneities in the material, the length scale of the material being probed and the phenomenon under study. In this paper, we seek to understand this interplay in a simple setting by studying the overall behavior of a one-dimensional periodic medium governed by strain gradient elasticity at the microstructural scale. We find through numerical experiments that the overall behavior is not described by a strain gradient elasticity. In other words, strain gradient theories are not invariant under averaging at this scale. We also find that the overall behavior may be described by a kernel-based nonlocal elasticity theory, but the kernel is highly oscillatory with slow decay. So we seek alternate characterization. First, we limit our interest to a range of length scales, and show that the behavior is described well by fractional strain gradient elasticity. Consequently, one can obtain various scaling laws with exponent between zero (classical elasticity) and one (strain-gradient elasticity). Second, we take a data-driven approach, and show that we can describe the overall behavior over a range of scales using a Fourier neural operator. Elasticity (physics) ; Scaling ; Length scale ; Exponent ; Fourier transform ; Scale invariance ; Limit (mathematics) Nonlocal and gradient elasticity in micro/nano structures ; Thermoelastic and Magnetoelastic Phenomena ; Elasticity and Material Modeling
Authors: Sun‐Ho Kang | Heeyoung Lee
Source: Results in Engineering (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Thermal performance optimization of cementitious composites incorporating nanoscale carbon-based materials through a regression-driven inverse-design framework • Inverse design predicts thermal performance of NCM composites. • Ensemble regression achieves the highest model accuracy (R² = 0.99). • Genetic algorithm effectively optimizes mix proportions. • Experimental validation confirms predictive model reliability. A regression-modeling-driven inverse-design framework is proposed to predict and optimize the thermal performance of cementitious composites incorporating nanoscale carbon-based materials (NCMs). An experimental dataset consisting of nine material and processing variables across 172 mix–voltage combinations was used to train five regression models. Among them, the ensemble regression model demonstrated the highest predictive accuracy and was selected as the core forward model. The selected model was integrated with four optimization algorithms—genetic algorithm, Bayesian optimization, particle swarm optimization, and simulated annealing—to identify optimal carbon concentration, nanomaterial length, and supply voltage combinations. Experimental validation across nine mix designs confirmed strong agreement between the predicted and measured temperature variations, with errors ranging between 0.67 and 4.05 °C. The results demonstrate that the proposed data-driven inverse-design workflow provides reliable design-to-target mapping and supports mix-design optimization for NCM-enhanced cementitious composites aimed at controlled heat generation and functional performance. Particle swarm optimization ; Bayesian optimization ; Inverse ; Regression analysis ; Thermal ; Thermal conductivity ; Workflow ; Regression ; Cementitious ; Particle (ecology) Thermal properties of materials ; Smart Materials for Construction ; Fire effects on concrete materials
Authors: Zahraa Sabah Ghnim | Ayat Hussein Adhab | Asha Rajiv | Anurag Yadav | Haider Radhi Saud | Aman Shankhyan | Sachin Jaidka | Kamal Kant Joshi | Morug Salih Mahdi | Aseel Salah Mansoor | Usama Kadem Radi | Nasr Saadoun | Mehrdad Mottaghi
Source: Anais da Academia Brasileira de Ciências (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Polyethylene glycol-nano composite thermal conductivity: Introduction of data-driven models The thermal conductivity of polyethylene glycol (PEG)-nanocomposites is crucial for their use in thermal management systems, underscoring the need for accurate predictive models. This study develops a Gradient Boosting Machine (GBM) model optimized with four algorithms: Batch Bayesian Optimization (BBO), Evolution Strategies (ES), Bayesian Probability Improvement (BPI), and Gaussian Processes Optimization (GPO). A dataset of 229 experimental samples (90% training, 10% testing) was employed, using temperature, PEG molecular weight, nano concentration, and nano type as input variables. K-fold cross-validation minimized overfitting. Model performance was evaluated with R-squared (R²), mean squared error (MSE), average absolute relative error (AARE%), and runtime. Correlation analysis showed nano concentration had the strongest impact on conductivity (0.75), followed by PEG molecular weight (0.56), while temperature (0.33) and nano type (0.24) were weaker predictors. Among the algorithms, GBM-ES achieved the best accuracy (R² = 0.9966 training, 0.9158 testing), outperforming the other methods and traditional linear regression, which showed far lower precision. GPO was the most computationally efficient (153.6 s), while BBO was slowest (274 s). Sensitivity analysis confirmed the dominant role of nano concentration and molecular weight. Overall, the optimized GBM models provide reliable, data-driven tools for predicting PEG-nanocomposite conductivity, reducing dependence on costly experimental work. Thermal conductivity ; Bayesian probability ; Nano- ; Mean squared error ; Polyethylene glycol ; Sensitivity (control systems) ; Thermal ; Gaussian ; Composite number Thermal properties of materials ; Solar-Powered Water Purification Methods ; Phase Change Materials Research
Authors: G. Chandana | Manikandan Muruganandam | Pritom J. Bora | Praveen C. Ramamurthy
Source:  (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization of Fluorine-Free Ti <sub>3</sub> C <sub>2</sub> T <sub>x</sub> Composites for Exceptional Microwave Absorption at Ultra-low Concentration The synthesis of Ti<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> was achieved through a novel fluoride-free, single-pot method utilizing FeCl<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> as the etching agent. The resulting Ti<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> was incorporated into an XPVB matrix to fabricate Ti<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf>/XPVB composites, demonstrating a remarkable synergy between magnetic and dielectric properties. A data-driven approach was implemented to optimize the filler concentration, facilitating precise prediction and tailoring of electromagnetic absorption characteristics. This integrated experimental-computational methodology identified the optimal Ti<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> concentration at 7 wt%, achieving an exceptional reflection loss (RL) of -59.6 dB at 13.024 GHz with a minimal thickness of 2.4 mm. The composite also exhibited an impressive effective absorption bandwidth (EAB) of 4 GHz (12.4–16.4 GHz) in the Ku-band, highlighting superior microwave absorption capabilities at an exceptionally low filler concentration. By integrating innovative synthesis techniques, computational optimization, and experimental validation, this study establishes a new benchmark in the development of electromagnetic absorption materials. Microwave ; Reflection loss ; Absorption (acoustics) ; Composite number ; Dielectric ; Dielectric loss ; Reflection (computer programming) ; Electromagnetic radiation MXene and MAX Phase Materials ; Microwave-Assisted Synthesis and Applications ; Intermetallics and Advanced Alloy Properties
Authors: Luhang Shen | Chun Guo | Zhu Liu | Na Ying | Huahua Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Physics-Aware Diffusion for Structure-Consistent Defect Generation Physics ; Diffusion ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advancements in Photolithography Techniques
Authors: Chamanthi Denisha Jayaweera | Ivaylo Plamenov Hitsov | Maxime Van Haeverbeke | Kimberly Solon | Cristian Camilo Gomez Cortes | Tom Depover | Thomas Diekow | Arne Verliefde | Ingmar Nopens
Source: Electrochimica Acta (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Model development and model selection for simultaneous optimization of corrosion inhibitor dosages for a cooling water system Corrosion ; Corrosion inhibitor ; Dose ; Selection (genetic algorithm) ; Water cooling ; Materials science ; Computer science ; Metallurgy ; Thermodynamics ; Pharmacology ; Physics ; Medicine ; Artificial intelligence Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Benjamin Laubach | Vincenzo Lordi | Rebecca Lindsey
Source: Journal of Chemical Information and Modeling (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification. Workflow ; Set (abstract data type) ; Novelty ; Chebyshev filter ; Data set ; Training set ; Key (lock) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Graph Neural Networks
Authors: Shasha Lv | Chong Tian | Jie Gao | Dongyue Chen | Zhengcao Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

Multi-Head Attention Network for Prediction of Irradiation Embrittlement in Reactor Pressure Vessel Steels Embrittlement ; Flux (metallurgy) ; Irradiation ; Reactor pressure vessel ; Feature (linguistics) ; Point (geometry) ; Pressure vessel ; Artificial neural network Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Yan Li | Yu Zheng | Wenjin Yao | J.P. Chen | Chenjing Yu | Chuanyun Tao | Guixiang Yin | Hong Tang | Wei Ge | Ziyun Guo
Source: Latin American Journal of Solids and Structures (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Fusion of Ensemble Learning and Terminal Ballistics: A Multiscale Predictive Framework for Penetration Depth Estimation Abstract In order to solve the problems related to the prediction of penetration depth of composite materials, a new method combining physical mechanism and data driving is proposed in this paper. This method integrates 53 groups of ballistic experimental data and 278 groups of LS-DYNA simulation data to construct a data set with 23 characteristic parameters. Previous studies have shown that the ratio of the radius of the circular arc of the warhead to the diameter of the projectile (CRH) has a significant influence on the penetration depth of the projectile. This paper uses the hyperbolic tangent function (tanh(2CRH)) to calculate its saturation effect and conduct in-depth analysis. An adaptive noise injection method of concrete type is used in data processing, which can reduce the data distribution difference between C80 and C150 to 42%. At the same time, because of the modified sequential forward selection algorithm, the input dimension of the model is reduced by 56% and its important physical characteristics are retained. Finally, a Bayesian-optimized Bagging integrated model is constructed, which realizes the high accuracy prediction of RMSE (Root mean square error)=0.23 m and R2=0.90 on the test set. The test data show that when the fiber content reaches about 1%, the penetration resistance of the material is obviously improved. This discovery provides a new direction for optimizing protective materials. Compared with the traditional Forrestal theoretical model, this model not only reduces the prediction error by 92%, but also verifies the effectiveness and universality of the "physical mechanism-guided + data-driven correction" hybrid modeling method in complex penetration problems, providing a generalizable research paradigm for similar engineering mechanics problems that are difficult to fully parameterize. Tangent ; Mean squared error ; Projectile ; Fusion ; Test data ; Warhead High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Machine Learning in Materials Science
Authors: Farhan Ashraf | Nicolò Grilli | Chen Liu | Catrin M. Davies | C. E. Truman | Mahmoud Mostafavi | David Knowles
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Investigating Creep Damage Initiation at the Mesoscale Using High-Resolution Electron Microscopy, Crystal Plasticity Modelling and a Classification Algorithm Crystal plasticity ; Mesoscale meteorology ; Creep ; Materials science ; Plasticity ; Crystal (programming language) ; Electron microscope ; High resolution ; Crystallography ; Algorithm ; Computer science ; Chemistry ; Composite material ; Geology ; Physics ; Optics ; Meteorology ; Remote sensing Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Microstructure and Mechanical Properties of Steels
Authors: Sofía González-Núñez (22090030) | Zeynep Sumer (3128595) | Carlos Amador (22090033) | Prakash Madhav (22090036) | Ajay Muralidharan (9095236) | Claire S. Adjiman (1356249) | Mariano Martín (1485265)
Source: Figshare (article, 2025-08-20)
Field: Chemistry — Organic Chemistry

Design ofSurfactant Molecules Under Performance Constraints The industrial applications of surfactant solutions are both numerous and extremely diverse, demonstrating the importance of these systems in everyday life and driving the need for a systematic approach to designing sustainable surfactant molecules adapted to the specific requirements of each application. Given the very large space of possible molecules, the identification of candidate surfactants that achieve a balance between the optimal physicochemical properties of the product and minimal environmental and health impacts is extremely challenging. In this work, a formulation and solution framework based on Computer-Aided Molecular Design is proposed for surfactant design. A novel multistage methodology is developed based on the initial generation of promising candidates for the two constituents of a surfactant, the hydrophilic head and the hydrophobic tail, followed by the multiobjective optimization of surfactant molecules. This decomposition results in an effective solution strategy. In addition to constraints that ensure the generation of feasible molecules, specific structural constraints can be incorporated in the formulation, accelerating the discovery and optimization process. Data-driven predictive models for the most relevant surfactant properties, such as critical micelle concentration, Krafft point, surface tension, toxicity, and biodegradability, are developed and implemented in the optimization formulation. Two case studies are tackled, successfully generating novel surfactant molecules. The proposed framework could be extended to more complex structures, such as two-headed or Gemini surfactants. Pulmonary surfactant ; Micelle ; Critical micelle concentration ; Identification (biology) ; Decomposition ; Response surface methodology ; Component (thermodynamics) Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization ; Environmental Chemistry and Analysis
Authors: Hans-Jörg Schneider (2632867)
Source: Figshare (article, 2025-04-11)
Field: Chemistry — Physical and Theoretical Chemistry

ProblematicAttributions of Entropic and HydrophobicEffects in Drug Interactions The ΔG affinity of drugs with biopolymers and the underling noncovalent interactions play an essential role in drug discovery. Supramolecular complexes can be designed for the identification and quantification of specific interactions, including their dependence on the medium; they also secure the additivity of ΔΔG increments. Such analyses have helped to clarify hydrophobic effects in intermolecular associations, which are barely measurable with small alkyl groups, but large in the presence of curved surfaces in which the replacement of hydrogen bond-deficient water molecules by a ligand leads to sizable enthalpy gain. Difficult to predict entropy contributions TΔS to ΔG vary between 5% and over 90%, particularly in drug associations, as is obvious from literature data. As illustrated with several drug complexes, many so-called hydrophobic effects involve in fact van der Waals or dispersive interactions. Measurements with supramolecular porphyrin complexes allowed us to derive dispersive binding contributions for many groups, which exhibit a correlation with polarizability. In consequence, heteroatoms or π-systems always lead to enhanced van der Waals contributions, while for hydrophobic effects the opposite is expected. Binding contributions from supramolecular complexes can in the future also help artificial intelligence approaches in drug discovery, by expansion of hybrid databases with potential ligands containing groups with desired binding contributions. van der Waals force ; Non-covalent interactions ; Supramolecular chemistry ; Intermolecular force ; Hydrogen bond ; Hydrophobic effect ; Entropy (arrow of time) ; Heteroatom ; Ligand (biochemistry) Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mehrab Mahdian | Ferenc Ender | Tamás Párdy
Source:  (article, 2025-12-01)
Field: Materials Science — Biomaterials

Inverse Design of Electrospun Nanofibers: An XGBoost and PSO Framework for Targeted Diameter Control Electrospinning is a high-throughput technique for producing nanofibers. The diameter of such nanofibers governs key properties such as surface area, porosity, and mechanical strength. Precise diameter control is therefore crucial for applications from filtration to tissue engineering, yet optimizing processing conditions typically relies on slow, costly trial-and-error experiments. This study presents a data-driven inverse-design framework that replaces traditional trial-and-error optimization with predictive modeling to achieve precise diameter control. Eleven regression models were trained on a dataset of 96 poly(vinyl alcohol) (PVA) experiments, with Extreme Gradient Boosting (XGBoost) emerging as the best surrogate (test R 2 = 0.896). SHAP analysis confirmed applied voltage and solution concentration as the most influential parameters, consistent with physical principles. In the optimization stage, Particle Swarm Optimization (PSO) achieved the highest accuracy (R 2 = 0.994, MAE ≈ 1.16 nm), outperforming a brute-force grid search. This framework enables rapid, efficient design of nanofibers with specified properties and is readily adaptable to other materials and fabrication processes. Particle swarm optimization ; Inverse ; Electrospinning ; Boosting (machine learning) ; Response surface methodology ; Filtration (mathematics) ; Grid ; Inverse problem ; Nanofiber ; Fabrication Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Advanced Multi-Objective Optimization Algorithms
Authors: Anrong Chen | Zhenghong Zhou | Yang Han | Bo Yang | Yingzi Li | Feng Zhu
Source: Angewandte Chemie (article, 2025-08-18)
Field: Chemistry — Organic Chemistry

Pd‐Catalyzed Stereospecific and Ligand‐Controlled Regiodivergent Suzuki–Miyaura Cross‐Coupling for the Synthesis of 3‐<i>C</i>‐Glycals and 2,3‐Unsaturated <i>C</i>‐Glycosides Abstract The selective modification of carbohydrates to achieve structural complexity has emerged as a crucial strategy in carbohydrate‐based drug development. However, the intricate stereochemistry and densely packed functional groups of carbohydrates pose significant challenges for precise stereoselectivity and regioselectivity control. Herein, we report a palladium‐catalyzed, stereospecific, and ligand‐controlled regiodivergent glycosyl Suzuki–Miyaura coupling of 3‐boryl‐glycals, establishing a robust platform for glycoside diversification with versatile C1 and C3 modification. Notably, stable 3‐boryl‐glycals with 1,1,2,2‐tetraethylethylene glycol protection were first synthesized and fully characterized. Operating under mild conditions, this reaction enables the efficient synthesis of diverse 2,3‐unsaturated aryl/alkenyl C ‐glycosides with exceptional regio‐ and stereoselectivity. Importantly, this ligand‐controlled regiodivergent C ‐glycosylation offers a concise and efficient strategy for accessing 3‐ C ‐glycals (C3‐arylated/alkenylated glycals), circumventing time‐consuming and labor‐intensive multi‐step sequences. To elucidate the origins of regioselectivity control, we employ a data‐driven approach integrating DFT‐assisted multivariate regression analysis, identifying key ligand parameters that govern regioselectivity. Leveraging this predictive model, we pursued rational ligand design to enhance C3‐glycosylation selectivity and yield. Additionally, density functional theory calculations (DFT) provide critical insights into the fundamental principles dictating regioselectivity and tunability. These findings establish a solid foundation for advancing ligand‐controlled regiodivergent glycosylation reactions and significantly expand the synthetic toolkit for glycochemical synthesis. Stereospecificity ; Chemistry ; Ligand (biochemistry) ; Catalysis ; Palladium ; Stereochemistry ; Coupling (piping) ; Combinatorial chemistry ; Organic chemistry ; Materials science ; Biochemistry Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis