jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Do Hung Manh | Chi H. Lee | Sang Uck Lee
Source: Rare Metals (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Bridging theory and practice: scalable prediction of alloy nanoparticle morphology and stability via machine‐learned moment tensor potentials Abstract Alloy nanoparticles exhibit unique properties that significantly differentiate them from their bulk counterparts, making them crucial for catalytic applications. However, the design of functional nanoparticles is often constrained by the challenge of accurately predicting their stability, especially at large scales, where traditional ab initio methods are limited by computational demands. To overcome this, we introduce a novel method for predicting the thermodynamic stability and chemical ordering of alloy nanoparticles, utilizing the machine‐learned moment tensor potential (MTP) integrated with active learning techniques. This approach allows precise assessment of the energetics of alloy nanoparticles regardless of morphology and remains scalable to experimental sizes, maintaining an optimal balance between computational efficiency and accuracy. The effectiveness of our method was demonstrated through its ability to predict the ground‐state configurations of alloy spherical nanoparticles (SNPs), which is consistent with experimental results. In particular, the machine‐learned MTP model enables a thorough investigation of the thermodynamic stability of SNPs and accurate prediction of the chemical ordering of SNPs depending on the type and composition ratio of the binary alloy systems. Consequently, this work not only highlights the superior performance of our approach over traditional computational methods for modeling large nanoparticles but also establishes a systematic approach for evaluating the stability of nanoparticles at full scale. We believe that our findings will pave the way for more reliable and scalable predictions in nanoparticle research and potentially accelerate the development of novel nanoparticle‐based technologies. Bridging (networking) ; Materials science ; Moment tensor ; Nanoparticle ; Stability (learning theory) ; Moment (physics) ; Alloy ; Tensor (intrinsic definition) ; Morphology (biology) ; Scalability ; Nanotechnology ; Composite material ; Computer science ; Classical mechanics ; Machine learning ; Physics ; Geometry ; Mathematics ; Geology Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Fan Yang | Wataru Idehara | Kenya Tanaka | Keisuke Shinokita | Haiyan Zhao | Kazunari Matsuda
Source: ACS Nano (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Autonomous Robotic Mechanical Exfoliation of Two-Dimensional Semiconductors 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 2D Materials and Applications ; Machine Learning in Materials Science ; Nanowire Synthesis and Applications
Authors: SHANG Chunlei | JIANG Tongbo | H. Wu | HOU Faguo | Shuize Wang | Gao Junheng | Haitao Zhao | Chaolei Zhang | Xinping Mao
Source: Corrosion Communications (article, 2025-12-05)
Field: Materials Science — Metals and Alloys

Efficient design of hydrogen-resistant ultra-high-strength steels via active learning and multiscale characterization • An active learning strategy is developed to efficiently design steel. • VC precipitates act as hydrogen capture sites to reduce HE sensitivity. • Pareto front analysis is optimized for multi-objective optimization. High-strength martensitic steels are extensively used in aerospace, automotive manufacturing, marine engineering, and other demanding applications due to their exceptional mechanical properties. In this work, an active learning strategy integrated with a genetic algorithm is proposed to accelerate the efficient design of ultra-high-strength steels (UHSS) with enhanced resistance to hydrogen embrittlement (HE). After three iterative optimization cycles, alloy compositions meeting the targeted performance metrics are successfully identified. Mechanical properties of selected alloys are subsequently validated through hydrogen-charged slow strain rate tensile (SSRT) testing. Concurrently, high-resolution transmission electron microscopy (HRTEM) and atom probe tomography (APT) are employed to elucidate key microstructural features. According to HRTEM statistical results, volume fraction of VC precipitates is about 0.623%, which acts as a hydrogen capture site and can effectively reduce sensitivity to HE. This methodology not only streamlines design process for UHSS and markedly enhances research and development efficiency, but also provides a solid theoretical foundation for performance optimization and practical application of high-strength steels. Hydrogen embrittlement ; Characterization (materials science) ; Alloy ; Atom probe ; Martensite ; Active learning (machine learning) ; Process optimization ; High-resolution transmission electron microscopy ; Hydrogen Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Stephen Kronenberger | Arthi Jayaraman
Source: Chemistry of Materials (article, 2025-06-23)
Field: Materials Science — Biomaterials

Effect of Methylcellulose Chain Design on Gelation and Fibril Structure Using Coarse-Grained Modeling Aqueous solutions of methylcellulose (MC) undergo thermoreversible gelation making them useful as additives for formulations with desired rheological properties. This gelation occurs at elevated temperatures and concurrently with the formation of stiff, high aspect ratio fibrils composed of several MC chains. The sol–gel transition temperature as well as the fibrillar structure of the gel state can be controlled by the molecular weight of the MC chains, MC concentration, and the design of the MC chains. One such MC design parameter is the degree of substitution (DS) which varies between 0 (cellulose) to 3.0 (fully methylated cellulose chain). The DS of commercially available MC (e.g., International Flavors and Fragrances’ Methocel A) is ∼1.8. The DS as well as the pattern of methylation (i.e., homogeneous or heterogeneous substitution) along the MC backbone can be tailored using variations in the synthesis protocols. To elucidate the effect of these MC chain design parameters on the sol–gel transition and the fibrillar structure of the MC chains, we conducted a multiscale computational study. First, we developed a coarse-grained (CG) model for MC with the model’s bonded parameters informed from atomistic simulations using Boltzmann inversion and nonbonded parameters informed from previous experimental studies of aqueous MC sol–gel phase behavior using surrogate Bayesian optimization. We performed molecular dynamics (MD) simulations of aqueous MC solutions with this CG model and characterized the effect of DS and methylation pattern on the gelation temperature and the MC chains’ fibril structure, specifically fibril diameter and persistence length. Fibril diameters and persistence lengths in our simulated systems agree well with those found from previous small-angle scattering analysis and electron microscopy measurements, providing validation for the results from the CG MD simulations. Using these CG MD simulations, we span a large MC design parameter space and identify these key design rules: (i) Aqueous solutions with MC chains having larger heterogeneity in the DS along the chain backbone, will gel at lower temperatures and assemble into fibrils with a smaller diameter. (ii) As the DS becomes “blockier” along the MC backbone, gelation will occur at lower temperatures, and fibril diameters will decrease. We expect our model development and the above design rules will aid future work focused on synthesis and/or simulation of MC with tailored gel properties for desired applications. Chain (unit) ; Fibril ; Materials science ; Chemistry ; Physics Advanced Cellulose Research Studies ; Material Properties and Processing ; Cultural Heritage Materials Analysis
Authors: Zhen‐Wu Shao | Zhiyuan Zhang | Y. P. Kuang | Chaozhi Xiong | Jiajie Yang | Wenjing Wu | Yuxin Liu | Li Xiong | Xiangping Duan | Chong Liu
Source: Small (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Bayesian Optimized Crystallization of a Hydroxamate‐Functionalized Covalent Organic Framework for Enhanced Uranyl Uptake To address the synthetic challenge of covalent organic frameworks (COFs), especially those with interfering functional groups, a Bayesian optimization (BO) centered approach is developed and implemented. Specifically, the crystallinity index for a well-known TAPB-PDA COF is improved by ≈80% via a one-round proof-of-concept BO. For a more complicated task toward the preparation of hydroxamate-functionalized TpPa COF, where improvement of both crystallinity and selectivity (against a crystalline byproduct) is needed, an efficient protocol comprising 6 BO iterations (with 5 experiments each) from an initial 64-experiment dataset is successfully developed. The functional COF, namely SUM-99 (SUM = Sichuan University Materials), with enhanced crystallinity, is subsequently demonstrated to be an effective, reversible, and selective sorbent for aquatic uranyl uptake. The importance of improved crystallinity, reflecting the power of BO, is showcased by a 23.7% increase in uranyl adsorption capacity. Therefore, the BO protocol and toolkit is presented for the efficient evolution of COF synthetic conditions, toward higher crystallinity and enhanced performances for downstream applications. Crystallinity ; Uranyl ; Crystallization ; Covalent bond ; Sorbent ; Materials science ; Covalent organic framework ; Chemical engineering ; Adsorption ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing
Authors: Cruz Junior | Daniela Andrade Damasceno | Keat Yung Hue | Caetano R. Miranda | Erich A. Müller | Rodrigo A. Vargas–Hernández
Source: ArXiv.org (preprint, 2025-06-27)
Field: Materials Science — Materials Chemistry

Scalable Bayesian Optimization for High-Dimensional Coarse-Grained Model Parameterization Coarse-grained (CG) force field models are extensively utilised in material simulations due to their scalability. Traditionally, these models are parameterized using hybrid strategies that integrate top-down and bottom-up approaches; however, this combination restricts the capacity to jointly optimize all parameters. While Bayesian Optimization (BO) has been explored as an alternative search strategy for identifying optimal parameters, its application has traditionally been limited to low-dimensional problems. This has contributed to the perception that BO is unsuitable for more realistic CG models, which often involve a large number of parameters. In this study, we challenge this assumption by successfully extending BO to optimize a high-dimensional CG model. Specifically, we show that a 41-parameter CG model of Pebax-1657, a copolymer composed of alternating polyamide and polyether segments, can be effectively parameterized using BO, resulting in a model that accurately reproduces key physical properties of its atomistic counterpart. Our optimization framework simultaneously targets density, radius of gyration, and glass transition temperature. It achieves convergence in fewer than 600 iterations, resulting in a CG model that shows consistent improvements across all three properties. Bayesian optimization ; Parameterized complexity ; Convergence (economics) ; Scalability ; Bayesian probability ; Field (mathematics) ; Key (lock) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Composite Material Mechanics
Authors: Ihsan‐ul‐Haq Toor
Source: Sustainability (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Integrating Active Learning in an Undergraduate Corrosion Science and Engineering Course—KFUPM’s Active Learning Initiative Material degradation in the form of corrosion is an important industrial problem that affects asset integrity, reliability, and sustainability in various industries. To equip engineering professionals with the knowledge required for appropriate material selection and corrosion-mitigation design, this subject forms an essential part of the engineering curriculum at both undergraduate and graduate levels across multiple disciplines. This paper presents the design, implementation, and evaluation of an active learning (AL)-based course framework to teach a corrosion science and engineering course at the mechanical engineering department, KFUPM. A combination of AL strategies, including project-based learning (PBL), case-based inquiries, peer instruction, and think–pair–share activities, etc., was systematically integrated into the course to promote collaborative learning, conceptual enrichment, and critical thinking. Positive student feedback (&gt;90% for most of the survey questions) with a response rate of 89% indicated increased motivation, improved understanding of complex corrosion mechanisms, and increased confidence in applying knowledge to solve engineering problems. A Cronbach’s alpha coefficient of 0.75 was obtained, reflecting strong internal reliability of the instrument. These findings suggest that integrating AL pedagogies in the corrosion course contributed towards enhanced learning outcomes and student preparation to support sustainable industrial practices using informed materials selection and corrosion management. Active learning (machine learning) ; Curriculum ; Corrosion ; Sustainability ; Selection (genetic algorithm) ; Asset (computer security) ; Engineering education ; Problem-based learning Material Selection and Properties ; Concrete Corrosion and Durability ; Problem and Project Based Learning
Authors: Anna Díez-Betriu | Beatriz Quintanilla‐Casas | Josep J. Masdemont | Alba Tres | Stefania Vichi | Francesc Guardiola
Source: Foods (article, 2025-11-28)
Field: Chemistry — Organic Chemistry

Deciphering the Complexity of Smoke Point in Virgin Olive Oils to Develop Simple Predictive Models The smoke point marks the onset of thermal degradation in edible oils. Although in this work we validated and improved its determination, it still relies on a subjective visual assessment and remains incompletely understood in relation to oil composition. This limitation reduces its reliability as a criterion for selecting frying oils in both industrial and culinary contexts. This study provides a systematic evaluation of how key chemical attributes of virgin olive oils influence their smoke point and proposes predictive models that could overcome the limitations of direct measurement. Forty-eight virgin olive oils were characterized, and multivariate modeling was applied to identify the most influential predictors. Free fatty acid content was the main determinant of the smoke point, exhibiting a strong inverse relationship, while saturated fatty acids and oxidative stability were shown to increase the smoke point by limiting the formation of volatile lipid oxidation products. Partial least squares models enabled accurate predictions using only routine quality parameters, such as free fatty acid content and saturated fatty acid content. Gaussian process regression further improved predictive performance and achieved high accuracy using free fatty acid content alone or, alternatively, other analytical parameters that are easily and routinely determined in olive oil. These findings offer a potential practical framework for estimating the smoke point without direct testing, with relevant implications for virgin olive oil quality control and the selection of oils for high-temperature applications. Partial least squares regression ; Fatty acid ; Smoke ; Olive oil ; Predictive modelling ; Multivariate statistics ; Oleic acid ; Quality (philosophy) ; Unsaturated fatty acid Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Biodiesel Production and Applications
Authors: Haotian Chen | Xiaoxiao Hu | Yuanyuan Lu
Source: Journal of Chemical Education (article, 2025-10-17)
Field: Chemistry — Electrochemistry

Advancing Voltammetry Education with <i>FreeSim</i> : An Interactive Toolkit for Teaching Electrochemical Simulation Voltammetry is a foundational technique in electrochemistry and is widely used to probe interfacial redox processes in systems ranging from energy storage to catalysis. However, the mathematical complexity of voltammetric simulations often limits their accessibility to students and educators. To address this barrier, we present FreeSim, a free and open-source electrochemical simulation toolkit designed especially for teaching and learning. The platform features an intuitive graphical interface and is accompanied by interactive Google Colab notebooks that guide users through the principles of voltammetry, including diffusion, electrode kinetic modeling, and homogeneous chemical reactions. To further support active learning, the toolkit includes reproducible student experiments that combine wet-lab measurements with simulation-based analysis. By integrating theoretical foundations, coding practice, real data, and graphical tools, FreeSim offers a one-stop educational resource to support deeper understanding and engagement with electrochemical education. Interface (matter) ; Graphical user interface ; Electrochemistry ; Homogeneous ; Coding (social sciences) ; Voltammetry ; Resource (disambiguation) ; Cyclic voltammetry Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Paweł Wiczling
Source: Journal of Chromatography A (article, 2025-10-30)
Field: Chemistry — Spectroscopy

Bayesian multilevel modeling of retention data informed by structural similarity of analytes Analyte ; Similarity (geometry) ; Covariance matrix ; Covariance ; Matrix (chemical analysis) ; Bayesian probability ; Structural similarity ; Pattern recognition (psychology) ; Mixture model Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Nikolaos Patsalidis | Mohsen Doust Mohammadi | Somnath Bhowmick | George Biskos | Vagelis Harmandaris
Source: Journal of Chemical Information and Modeling (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally efficient physics-aware potentials to achieve quantum-level accuracy for complex interfacial systems. Our approach trains physics-aware potentials, with incorporated flexibility and smoothness, on actively sampled density functional theory (DFT) data to describe interactions between undercoordinated atomic silver (Ag) clusters and gaseous pollutants (CO<sub>2</sub>, CO, SO<sub>2</sub>), relevant for environmental applications like sensing. The AL process follows three stages: (1) FFs are trained using adaptable physics aware potentials of semiempirical descriptors, optimized via a Pareto analysis scheme; (2) new candidate structures are generated through the use of the refined FFs in Metropolis Hastings Monte Carlo (MHMC) or stochastic molecular dynamics (sMD) simulations; (3) a subset of candidates is selected for DFT computations based on an outlier score (OS), which utilizes the existing data descriptor distributions, ensuring diverse PES exploration. This framework produces FFs capable of capturing cohesive, physisorption, and chemisorption interactions with admirable accuracy, close to ab initio methods, while retaining the efficiency of semiempirical potentials. To demonstrate, produced FFs are utilized in molecular dynamics (MD) simulations of single Ag clusters embedded in bulk gas phases, examining condensation characteristics. Our methodology is highly versatile, easily accommodating various choices of descriptors, model basis sets, and sampling techniques. Statistical physics ; Molecular dynamics ; Physisorption ; Computer science ; Ab initio ; Physics ; Computational science ; Chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Wenjing Zhao | Libin Duan | Wei Xu | Xing Liu | Guangya Zhang | Tie Jun Xu | Yu Zhang
Source: International Journal of Mechanics and Materials in Design (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Internal energy density homogenization-driven crashworthiness design framework for VRB/OW-GFRP hybrid hat-shaped beam under transverse impacting Crashworthiness ; Solid mechanics ; Transverse plane ; Homogenization (climate) ; Materials science ; Fibre-reinforced plastic ; Structural engineering ; Beam (structure) ; Composite material ; Engineering ; Finite element method High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Cellular and Composite Structures