jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhenquan Shen | Xiaochen Zhou | Ming Zhao | Yafei Li
Source: Biomimetics (article, 2025-03-28)
Field: Materials Science — Biomaterials

A Structural Optimization Framework for Biodegradable Magnesium Interference Screws Biodegradable magnesium alloys have garnered increasing attention in recent years, with magnesium alloy-based biomedical devices being clinically used. Unlike biologically inert metallic materials, magnesium-based medical devices degrade during service, resulting in a mechanical structure that evolves over time. However, there are currently few computer-aided engineering methods specifically tailored for magnesium-based medical devices. This paper introduces a structural optimization framework for Mg-1Ca interference screws, accounting for degradation using a continuum damage model (CDM). The Optimal Latin Hypercube Sampling (OLHS) technique was employed to sample within the design space. Pull-out strengths were used as the optimization objective, which were calculated through finite element analysis (FEA). Both Response Surface Methodology (RSM) and Kriging models were employed as surrogate models and optimized using the Sequential Quadratic Programming (SQP) algorithm. The results from the Kriging model were validated through FEA, and were found to be acceptable. The relationships between the design parameters, the rationale behind the methodology, and its limitations are discussed. Finally, a final design is proposed along with recommendations for interference screw design. Finite element method ; Latin hypercube sampling ; Magnesium ; Kriging ; Materials science ; Computer science ; Optimal design ; Sequential quadratic programming ; Response surface methodology ; Mechanical engineering ; Quadratic programming ; Structural engineering ; Engineering ; Mathematical optimization ; Mathematics ; Monte Carlo method ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Welding Techniques Analysis
Authors: Fanglei Hu | Rong Zhou | KenHee Ryou | Rujing Zha | Stephen R. Niezgoda | Tianju Xue | Jian Cao
Source: Journal of Applied Mechanics (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

An Efficient Graphical Processing Unit-Accelerated Calibration of Crystal Plasticity Model Parameters by Multi-Objective Optimization With Automatic Differentiation-Based Sensitivities Abstract Accurate and efficient determination of crystal plasticity (CP) material parameters is essential for predictive simulations that link microstructures, manufacturing processes, and material properties. This study presents a graphical processing unit (GPU)-accelerated pipeline for calibrating CP material parameters, integrating automatic differentiation (AD)-based sensitivities with gradient-based optimization, built upon our open-source jax-cpfem package. This method eliminates reliance on finite differences in gradient-based approaches while improving efficiency over gradient-free optimization. The effectiveness of the pipeline is demonstrated through five case studies covering various crystal structures and boundary conditions. First, the AD-based sensitivity analysis achieves over 10 × speedup compared to finite difference while maintaining accuracy for complex, nonlinear constitutive laws. Second, a comprehensive analysis of initial starting points on gradient-based optimization demonstrates that using appropriate bounds mitigates potential issues. Across both single-crystal and polycrystalline cases calibrating six material parameters, our pipeline requires approximately 7 × fewer iterations and achieves 3 × higher efficiency over popular gradient-free methods like Bayesian optimization, regardless of geometry complexity. Furthermore, the successful calibration of 12 parameters in a dual-phase steel model highlights the capability of the pipeline to handle high-dimensional optimization problems, which is challenging for gradient-free optimization. Finally, the robustness of our pipeline is validated using noisy synthetic data and experimental tensile data for wrought IN625 over a finite strain range. These results illustrate the applicability of our pipeline to real-world scenarios and its potential for high-dimensional optimization and promising applications in integrated computational materials engineering workflows. Pipeline (software) ; Robustness (evolution) ; Calibration ; Finite element method ; Sensitivity (control systems) ; Speedup ; Nonlinear system ; Boundary value problem Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Model Reduction and Neural Networks
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-06)
Field: Chemistry — Analytical Chemistry

Automated Quality Control of Glucose-Based Pharmaceutical Formulations via HyperSpectral Image Analysis and Bayesian Optimization **Abstract:** This paper introduces a novel real-time quality control system for glucose-based pharmaceutical formulations utilizing hyperspectral imaging (HSI) and Bayesian optimization. Current quality control methods rely on destructive sampling and offline analysis, introducing delays and potential inconsistencies. Our system leverages the power of HSI to non-invasively assess formulation homogeneity, particle size distribution, and crystal morphology. A Bayesian optimization framework is then applied to dynamically adjust process parameters in response to HSI feedback, enabling self-regulating formulation production with significantly improved quality consistency and reduced waste. This system offers a 10x improvement in detection sensitivity compared to conventional methods and enables near-instantaneous feedback control, facilitating a paradigm shift towards continuous manufacturing of glucose-based pharmaceuticals. Hyperspectral imaging ; Bayesian probability ; Sensitivity (control systems) ; Consistency (knowledge bases) ; Process (computing) ; Bayesian optimization ; Quality (philosophy) ; Sampling (signal processing) ; Control (management) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Protein purification and stability
Authors: Niko Krebs | Martin Demleitner | Rodrigo Q. Albuquerque | Bernhard Schartel | Holger Ruckdäschel
Source: EPub Bayreuth (University of Bayreuth) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Bayesian Optimization of flame-retardant performance in a high-Tg epoxy resin system Polymeric materials are widely used due to their mechanical properties and cost-effectiveness, but their inherent flammability requires effective flame-retardant additives to meet safety standards. Optimizing multi-component flame-retardant formulations is challenging due to the vast experimental space. This study applies Bayesian Optimization (BO) to optimize flame-retardant formulations in high glass transition temperature (Tg) epoxy resins. Aluminum diethyl phosphinate (AlPi) was systematically combined with three synergists: zinc stannate (ZnSt), a silicone-based additive (DowSil), and low-melting glass frits (Ceepree). BO-guided experimental design expanded from 16 initial formulations to a total of 28, minimizing the Maximum Average Rate of Heat Emission (MARHE) under the constraint of Total Smoke Production (TSP) &lt; 17 m2 using the epsilon-constraint method. BO revealed non-linear synergistic interactions: ZnSt significantly reduced smoke production while AlPi effectively lowered heat release. The optimized formulation (BO7) achieved the lowest MARHE (122 kW/m2) while maintaining acceptable smoke levels, establishing a new Pareto front. The results demonstrate the effectiveness of BO in accelerating the development of synergistic, halogen-free flame-retardant polymer systems, offering a scalable and sustainable approach to polymer formulation design. Bayesian optimization ; Epoxy ; Smoke ; Polymer ; Pareto principle ; Aluminium ; Flammability ; Glass transition Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer composites and self-healing
Authors: Virgile Rouffeteau | Clara Perrier | Maximilian Fleck | Geoffrey Gontard | Maxime R. Vitale | Laurence Grimaud
Source: Comptes Rendus Chimie (article, 2025-11-19)
Field: Chemistry — Organic Chemistry

Electro-induced carbamoylation of arenes optimized by a machine learning model In this article, we report the electro-induced C–H functionalization of aromatic compounds by isocyanides and water, to produce arylcarboxamides. This controlled oxidation of electron-rich aromatic compounds leads to an eco-efficient synthesis of secondary benzocarboxamides, along with cyanide group formation. The optimization process was guided by a Bayesian optimization-based model, and good yields were quickly obtained. Bayesian optimization ; Surface modification ; Process (computing) ; Cyanide ; Bayesian probability Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: R. Brindha | Rajdeep Dutta | Maciej Koperski | Vijila Chellappan | J. Senthilnath
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-Property Optimization for Designing Carbon Electrodes Bayesian optimization ; Dwell time ; Pyrolysis ; Process (computing) ; Yield (engineering) ; Response surface methodology ; Carbon fibers ; Electrode ; Nonlinear system ; Symbolic regression Supercapacitor Materials and Fabrication ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Zhao Xi Lin | Liu Liyin
Source:  (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Research on Polymer Material Property Prediction Based on Wavelet Transform and Transformer The structure-property relationship of polymer materials is complex and influenced by multiple scale factors. Traditional experimental methods are not only costly and time-consuming, but also difficult to support high-throughput prediction requirements. To address this issue, this paper proposes a polymer thermal property prediction method based on Morgan fingerprints, wavelet multi-scale transform, and Transformer networks. First, a sample set is constructed using 2000 sets of polymer structure data with theoretical or measured glass transition temperatures (Tg) from the PolyAskInG database. A 1024-dimensional Morgan fingerprint is generated using RDKit to characterize the basic topological structure. In the feature processing stage, Haar wavelets are introduced to perform multi-scale decomposition of the fingerprint features at levels 1-4, extracting approximation coefficients and detail coefficients to construct a fused feature vector. In model construction, a Transformer encoder, multi-head attention mechanism, and regularization strategy are employed to improve feature representation ability and generalization performance. Simultaneously, Bayesian optimization is used to automatically tune key hyperparameters such as learning rate, hidden dimension, and number of attention heads. Experimental results show that compared with the model without wavelet transform, the prediction accuracy of the wavelet- Transformer framework is significantly improved. The MAE on the test set decreased from 13.21 to 10.96, the RMSE decreased from 19.84 to 16.92, and the R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> increased from 0.7425 to 0.8041. Furthermore, ablation experiments further validated the core contributions of wavelet decomposition and the attention mechanism. The results indicate that multi-scale feature fusion and deep sequence modeling are effective ways to improve the prediction accuracy of polymer properties, providing key technical support for the intelligent design and performance characterization of materials. Wavelet ; Pattern recognition (psychology) ; Wavelet transform ; Wavelet packet decomposition ; Discrete wavelet transform ; Hyperparameter ; Transformer ; Test set Machine Learning in Materials Science ; Polymer composites and self-healing ; Injection Molding Process and Properties
Authors: Fangkun Zhang | Chuan Li | Aozhe Meng | Baoming Shan | Xuezhong Wang | Qilei Xu
Source: Powder Technology (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Optimal control of batch cooling crystallization under considering nucleation uncertainty Nucleation ; Crystallization ; Optimal control ; Product (mathematics) ; Process control ; Optimization problem ; Crystal (programming language) ; Process optimization Crystallization and Solubility Studies ; Process Optimization and Integration ; Solidification and crystal growth phenomena
Authors: Spirande, Ekaterina | Miryashkin, Timofei | Kolmakov, Andrei | Shapeev, Alexander
Source: arXiv (Cornell University) (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

Automated Prediction of Thermodynamic Properties via Bayesian Free-Energy Reconstruction from Molecular Dynamics Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anharmonicity and liquids, while molecular dynamics (MD) is computationally demanding, neglects low-temperature quantum effects, and often requires manual planning and post-processing of simulations. We present a unified workflow that reconstructs the Helmholtz free-energy surface from MD data using Gaussian Process Regression (GPR), augmented with zero-point energy corrections from harmonic/quasi-harmonic theory. The framework propagates statistical uncertainties, mitigates finite-size effects, and employs active learning to optimize sampling in the volume-temperature space. It applies seamlessly to both crystalline and liquid phases. We demonstrate the methodology by computing heat capacities, thermal expansion, isothermal and adiabatic bulk moduli, and melting properties for nine elemental FCC and BCC metals using 20 classical and machine-learned interatomic potentials, with all predictions accompanied by quantified confidence intervals. Automated, general, and uncertainty-aware, the workflow advances high-throughput thermodynamics and provides a systematic benchmark for interatomic potentials. Molecular dynamics ; Adiabatic process ; Interatomic potential ; Anharmonicity ; Benchmark (surveying) ; Helmholtz free energy ; Thermodynamic integration ; Gaussian process ; Workflow ; Gaussian Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Model Reduction and Neural Networks
Authors: Ekaterina Spirande | Timofei Miryashkin | Andrei Kolmakov | Alexander V. Shapeev
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated Prediction of Thermodynamic Properties via Bayesian Free-Energy Reconstruction from Molecular Dynamics Molecular dynamics ; Adiabatic process ; Interatomic potential ; Anharmonicity ; Benchmark (surveying) ; Helmholtz free energy ; Thermodynamic integration ; Gaussian process ; Workflow ; Gaussian Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Tuan Nguyen‐Sy | Hieu Do-Trung | Nam Nguyen-Hoang | Duc Toan Truong | My-Kristyna Nguyen-Thao
Source: Journal of Computer-Aided Molecular Design (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

LiteBoost: a lightweight and explainable boosting model for predicting polymer density from SMILES data Interpretability ; Boosting (machine learning) ; Gradient boosting ; Hyperparameter ; Workflow ; Benchmark (surveying) ; Mean squared error ; Training set ; Model selection ; Predictive modelling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Source:  (article, 2025-06-28)
Field: Chemistry — Physical and Theoretical Chemistry

AN ESCAPE ROOM ACTIVITY IN ANALYTICAL CHEMISTRY This proposal outlines an escape room activity that integrates key concepts from courses offered by the Department of Analytical Chemistry at the University of Valencia.Designed as an interactive learning experience, the activity encourages students to develop hypotheses, ask questions, and make decisions based on clues and experimental results obtained through analytical instrumentation.By engaging in this hands-on approach, students can apply the theoretical knowledge gained during their academic studies.Escape room-style activities are rooted in active learning, placing students at the center of the experience while fostering the development of theoretical, practical, and behavioral skills.Research supports their effectiveness in complementing traditional teaching methods, aiding exam preparation, and enhancing laboratory practice.Additionally, these activities boost student motivation through engaging challenges and promote teamwork in problem-solving scenarios.In this case, the proposed escape room takes place in a laboratory setting designed to simulate the workspace of a missing scientist.Students must complete a series of analytical tests to progress toward their ultimate goal: retrieving a code to unlock the scientist's laptop and uncover the mystery behind their disappearance.The challenges include fluorescence spectroscopy for analyzing tonic drinks, infrared spectroscopy for organic substance identification, and X-ray fluorescence for examining inorganic salts.Each test provides analytical results that lead to the next stage, ultimately guiding students toward the final solution.This initiative makes use of the available facilities and resources at the University of Valencia's Faculty of Chemistry.The required equipment and materials are readily accessible and easily adaptable.The activity is tailored for laboratory courses with an instrumental focus, such as the Analytical Chemistry II Laboratory and the Applied Instrumental Analysis Laboratory, typically taken in the third and fourth years of the Chemistry Degree program. Computer science ; Chemistry ; Nanotechnology ; Materials science Various Chemistry Research Topics
Authors: Fan Yang (1413) | Wataru Idehara (22575022) | Kenya Tanaka (6020225) | Keisuke Shinokita (5545031) | Haiyan Zhao (436179) | Kazunari Matsuda (1297329)
Source: Figshare (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Autonomous RoboticMechanical Exfoliation of Two-DimensionalSemiconductors Combined with Bayesian Optimization Simple mechanical exfoliation of layered materials is the most frequently employed method for producing high-quality monolayers of two-dimensional semiconducting materials. However, mechanical exfoliation by human hands is a microscopically sophisticated process with a large number of microscopic parameters, which requires significant operator efforts and limits the reproducibility in achieving high-quality and large-area monolayer semiconducting materials. Herein, we have proposed a strategy for mechanical exfoliation by combining a developed robotic system and Bayesian optimization. We demonstrated that it is possible to explore the optimized experimental conditions among a large number of parameter combinations for mechanical exfoliation in a relatively small number of experimental trials. The entire mechanical exfoliation process, from preparation to detection of monolayer semiconductors, can be performed by a developed robotic system. Moreover, the robot system can autonomously explore the optimized experimental conditions among a huge number of parameter sets implanted on the Bayesian optimization algorithm. The optimized experimental condition was determined through only 30 trials of mechanical exfoliation experiments, representing 0.25% of all experimental parameter conditions. As a result, the critical parameters for the efficient fabrication of large-area monolayer WSe<sub>2</sub> were elucidated. Exfoliation joint ; Monolayer ; Bayesian optimization ; Fabrication ; Process (computing) ; Semiconductor device fabrication ; Robot 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Chasheng He (10873529) | Guihua Luo (11178013) | Weike Su (555668) | Hongliang Duan (1347471) | Yuanyuan Xie (662418) | Guijun Zhang (5197520) | An Su (5575574)
Source: Figshare (article, 2025-05-28)
Field: Chemistry — Inorganic Chemistry

Ruthenium Complexesfor Asymmetric Hydrogenation andSelective Dehalogenation Revealed via Bayesian Optimization In this study, we report the discovery of ruthenium complexes that enable efficient asymmetric hydrogenation (AH) of ketones and selective dehalogenation. Using Bayesian optimization-assisted multiparameter screening, we identified ruthenium catalysts exhibiting distinct temperature-dependent behaviors. Below 129 °C, these catalysts achieve AH of aryl ketone halides with up to 99% conversion and >99% enantiomeric excess (ee), without hydrodehalogenation. Above this temperature, catalyst decomposition generates a ruthenium species that selectively promotes base-free dehalogenation using ethanol as a hydrogen donor, achieving yields up to 95%. Unlike conventional hydrodehalogenation catalyzed by metals such as Pd, Ni, or Rh, this method avoids the need for strong bases and hydrogen. These diverse catalytic activities were applied to synthesize the chiral fluoroiodobenzyl alcohol <b>2C</b>, a key intermediate of Lorlatinib, in 60% yield with 94% ee, and to selectively dechlorinate an Ipragliflozin intermediate with a 64% yield. This work highlights the potential of ruthenium complexes for sustainable and selective catalysis. Ruthenium ; Catalysis ; Halogenation ; Aryl ; Ketone ; Asymmetric hydrogenation ; Noyori asymmetric hydrogenation ; Halide ; Alcohol Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Haixia Cheng | Yanzhou Wang | Keke Song | Minhui Song | Yaqing Hou | Zhongnan Bi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potential for Fe–Co–X (X = Re, W) alloys: Ductility–brittleness behavior at finite temperatures Interatomic potential ; Ductility (Earth science) ; Softening ; Work (physics) ; Molecular dynamics ; Stability (learning theory) ; Limiting ; Stiffness ; Ab initio Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Microstructure and mechanical properties
Authors: Shubham Deolka (5080421) | David Dalmau (221136) | Aleria Garcia Roca (21013239) | Serhii Vasylevskyi (10144623) | Mohammad H. Samha (21013236) | Matthew S. Sigman (1306671)
Source: Figshare (article, 2025-09-16)
Field: Chemistry — Organic Chemistry

Development of a HeterolepticPalladium Catalyst forthe Fluorination of Arylboronic Acids using Data Science Tools We report the development of a rare example of metal-catalyzed arylboronic acid fluorination that utilizes heteroleptic palladium complexescatalysts that utilize two distinct ligands to achieve effective catalysis. Reaction optimization was achieved using high-throughput experimentation (HTE) for data collection, accelerated using multiobjective Bayesian optimization (EDBO+) for selection of experiments. Substrate scope was selected employing data science tools, and evaluation of these substrates revealed excellent functional group tolerance. Extensive mechanistic studies suggest that the two ligand types play unique roles in promoting the reaction wherein the Pd-terpyridine complex promotes facile transmetalation with the aryl boronic acid, while the addition of a second bidentate ligand is required for effective oxidation of this intermediate to form a high-valent Pd–F species that undergoes reductive elimination. This data science-driven workflow presents an illustrative example of exploiting heteroleptic catalysts to advance new fluorination reactions. Transmetalation ; Palladium ; Catalysis ; Ligand (biochemistry) ; Aryl ; Reductive elimination ; Substrate (aquarium) Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Fluorine in Organic Chemistry