jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haodong Liu | Zheng Liu | Liang Tu | Jinlong Liang | Yuhao Zhang
Source: IEEE Transactions on Reliability (article, 2025-05-02)
Field: Materials Science — Ceramics and Composites

Fatigue Life Prediction and Reliability Assessment of Cracked CFRP Laminates in WTBs Application: A Combined Approach Using Paris-XFEM and ALK Techniques Wind turbine blades (WTBs) are crucial load-bearing components that ensure the safe and efficient operation of wind power systems. Carbon fiber–reinforced polymer (CFRP) composites, known for their low density and high strength-to-weight ratio, hold great promise for constructing larger and lighter blades. However, inherent manufacturing defects may cause crack propagation under fluctuating loads, thereby compromising the structural integrity of these components. Additionally, the complex fabrication processes and heterogeneous nature of CFRP composites result in variability in material properties, ultimately impacting their mechanical performance. This study examines the fatigue behavior of CFRP laminated plates with preexisting cracks, presenting a novel methodology to evaluate fatigue life and model reliability under conditions of uncertain material properties and loading scenarios. Static and fatigue tests were performed to quantify the effects of varying load levels and crack lengths on fatigue life. A simulation-based analysis, applying Paris’ law combined with the extended finite element method, offered a comprehensive evaluation of the fatigue crack propagation process. Fatigue life data were initially generated using finite element simulations, and an active learning Kriging surrogate model was developed, achieving an exceptional predictive accuracy of 99.96%. This model was subsequently employed to evaluate the fatigue reliability and conduct a sensitivity analysis of cracked CFRP laminates, laying the groundwork for the safety assessments and quality evaluations of wind turbine blades. Reliability (semiconductor) ; Structural engineering ; Reliability engineering ; Materials science ; Computer science ; Forensic engineering ; Engineering Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques ; Concrete Corrosion and Durability
Authors: Jennie Martin (22486661) | Michele Ceriotti (1301079) | Graeme M. Day (1297449)
Source: Figshare (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

An Adapted SimilarityKernel and Generalized ConvexHull for Molecular Crystal Structure Prediction We adapted an existing approach to identifying stabilizable crystal structures from prediction setsthe Generalized Convex Hull (GCH)to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to define the similarity of molecular crystal structures in a more physically motivated way. The use of the adapted similarity kernel was assessed for several organic molecular crystal landscapes, demonstrating improved interpretability of the resulting machine-learned descriptors. We also demonstrate that the adapted kernel results in improved performance in predicting lattice energies using Gaussian process regression. Our overall findings highlight a sensitivity of similarity kernel-based landscape analysis methods to kernel construction, which should be considered when applying these methods. Kernel (algebra) ; Similarity (geometry) ; Convex hull ; Interpretability ; Crystal structure prediction ; Kernel method ; Gaussian ; Lattice (music) ; Crystal structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Sangsoo Shin | Huijeong Jeong | Jiwoo Kim | Byeong Jin Kim | Jin Hong Lee | Sungyup Jung | Jaegeun Lee
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Influence of ethylene thermal decomposition on carbon nanotube growth: insights from a two-zone reactor study Gas-phase decomposition of the carbon precursor is a critical yet not fully understood step in the catalytic chemical vapor deposition growth of carbon nanotubes (CNTs). Here, we present a systematic investigation of how the thermal decomposition of C<sub>2</sub>H<sub>4</sub> influences CNT growth. Using a custom-designed two-zone reactor with independently controlled preheating (decomposition) and CNT growth zones, we decoupled the effects of gas-phase decomposition from the growth temperature. Standard synthesis conditions were first established using Bayesian optimization, with the CNT growth zone temperature (<i>T</i><sub>g</sub>) fixed at 700 °C. CNTs were then synthesized at preheating zone temperatures (<i>T</i><sub>p</sub>) of 500, 600, 700, 800, and 900 °C while maintaining <i>T</i><sub>g</sub> at 700 °C under standard conditions. Carbon yield and <i>I</i><sub>G</sub>/<i>I</i><sub>D</sub> remained stable at 500, 600, and 700 °C but showed significant changes at 800 and 900 °C. To elucidate these variations, we analyzed the gas-phase composition at various temperatures using micro-GC. Significant changes in the gas-phase composition were observed above 700 °C, correlating with changes in carbon yield and crystallinity. Based on these results, we propose a mechanism by which differences in gas-phase chemistry lead to changes in the carbon yield and the <i>I</i><sub>G</sub>/<i>I</i><sub>D</sub> ratio. This study provides valuable insight into the role of gas-phase decomposition in CNT growth and highlights the potential of tuning gas-phase chemistry for controlled growth of CNTs. Carbon nanotube ; Decomposition ; Ethylene ; Thermal decomposition ; Materials science ; Thermal ; Chemical engineering ; Carbon fibers ; Pyrolysis ; Nanotube ; Nanotechnology ; Catalysis ; Composite material ; Chemistry ; Thermodynamics ; Organic chemistry ; Composite number ; Engineering Carbon Nanotubes in Composites ; Graphene research and applications ; Nanotechnology research and applications
Authors: Jenna Fromer (21246936) | Runzhong Wang (21246939) | Mrunali Manjrekar (21246942) | Austin Tripp (21246945) | José Miguel Hernández-Lobato (4762530) | Connor W. Coley (1496653)
Source: Figshare (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Batched BayesianOptimization by Maximizing the Probabilityof Including the Optimum Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation. This often involves optimizing nonadditive batch acquisition functions, necessitating approximation via myopic construction and/or diversity heuristics. In this work, we propose an acquisition strategy for discrete optimization that is motivated by pure exploitation, qPO (multipoint Probability of Optimality). qPO maximizes the probability that the batch includes the true optimum, which is expressed as the sum over individual acquisition scores and thereby circumvents the combinatorial challenge of optimizing a batch acquisition function. We differentiate the proposed strategy from parallel Thompson sampling and discuss how it implicitly captures diversity. Finally, we apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it is competitive with and complements other state-of-the-art methods in batched Bayesian optimization. Bayesian optimization ; Bayesian probability ; Sampling (signal processing) ; Data acquisition ; Batch processing ; Bayes' theorem ; Design of experiments Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Mrinalini Mulukutla | Robert Robinson | Danial Khatamsaz | Brent Vela | Trevor Hastings | Nhu Vu | Raymundo Arróyave
Source: Materialia (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Supply risk-aware alloy discovery and design: A case study on the MoNbTiVW system Materials science ; Alloy ; Risk analysis (engineering) ; Metallurgy ; Business Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Additive Manufacturing Materials and Processes
Authors: Liang Chen | Diptesh Das | Jiang Guo | Ryo Tamura | Zetian Mao | Koji Tsuda
Source: ArXiv.org (preprint, 2025-04-09)
Field: Materials Science — Materials Chemistry

CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still ineffective due to symmetry agnostic encoding of atomic coordinates. We introduce CRYSIM, an algorithm that encodes the space group, the Wyckoff positions combination, and coordinates of independent atomic sites as separate variables. This encoding reduces the search space substantially by exploiting the symmetry in space groups. When CRYSIM is interfaced to Fixstars Amplify, a GPU-based Ising machine, its prediction performance was competitive with CALYPSO and Bayesian optimization for crystals containing more than 150 atoms in a unit cell. Although it is not realistic to interface CRYSIM to current small-scale quantum devices, it has the potential to become the standard CSP algorithm in the coming quantum age. Ising model ; Symmetry (geometry) ; Quantum ; Encoding (memory) ; Space (punctuation) ; Bayesian optimization ; Crystal (programming language) ; Symmetry breaking ; Quantum computer Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Chengyi Hou | RuiXuan Zhao | Huijun Zhang | ChuBin Wan | Keyuan Chen | P. B. Pan | Zhenjun Ma | Xiao‐Yu Hu | Ping Qian | Xin Ju
Source: Nuclear Engineering and Technology (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

A general-purpose machine-learning interatomic potential for FeCr steel: Atomistic insights into high-temperature mechanical behavior FeCr alloys are promising for cladding due to their thermal stability and radiation resistance, but their atomic-scale mechanical behaviors under varying temperatures is not yet well understood. Traditional empirical potentials are unreliable at high temperatures due to oversimplified assumptions. The deep potential (DP) model offers a more accurate and efficient alternative for predicting high-temperature alloy behavior. Here, we develop a deep potential model for FeCr alloys using a dataset obtained from density-functional theory (DFT) and the DP-GEN active learning framework. Molecular dynamics(MD) simulations based on the DP model show that a typical Fe 3 Cr alloy has a tensile strength of 15 GPa at 1200 K with a 25% reduction in stress. This difference is attributed to the pinning effect of Cr atoms on dislocation slip and the strengthening induced by short-range ordering in Fe 3 Cr bonds. Compared to the MEAM potential, the DP model predicts a fracture strain of 32% for FeCr alloys, which is in agreement with ductile characteristics observed in experiments. These results elucidate the microscopic mechanical behavior and failure mechanisms of FeCr alloys, paving the way for the development of high-performance FeCr alloys for high-temperature applications. Alloy ; Interatomic potential ; Ultimate tensile strength ; Cladding (metalworking) ; Slip (aerodynamics) ; Dislocation ; Thermal ; Thermal stability Fusion materials and technologies ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Lu Lu | Anming Bai | Haodong Wang | Wenjia Xi | Shan Yun | Shan Gao | Mingming Wang | Xiaoxia Sun
Source: Case Studies in Construction Materials (article, 2025-08-06)
Field: Chemistry — Spectroscopy

Pareto-driven gradient-boosted Bayesian optimization for multi-objective inverse design of Aerogel-cement-EPS panels in small-sample regimes This study addresses the challenges of escaping local optima in traditional engineering design methods and data scarcity during research and development. It proposes a novel multi-objective optimization approach integrating data augmentation, data expansion, gradient-boosted Bayesian optimization, and Pareto front analysis. By constructing a Pareto front solution set, this method successfully resolves the multi-objective optimization and inverse design challenges in the formulation design of aerogel-cement-expanded polystyrene building insulation panels under small-sample conditions. The integration of data augmentation and data expansion techniques significantly enhances the scale and diversity of the dataset, effectively mitigating the data scarcity issue. The gradient-boosted Bayesian optimization model rapidly establishes complex mappings between material composition and performance, enabling more efficient global optimum search. It elevates the prediction accuracy of small-sample learning to 0.99. Furthermore, the introduction of a Pareto front exploration strategy within the Bayesian optimizer balances the exploitation of known information and the exploration of unknown design space. This strategy overcomes the limitation of traditional Bayesian optimization, which tends to converge towards a single-objective optimum. Experimental results demonstrate that, while ensuring other performance metrics comply with standard requirements, the thermal conductivity was successfully reduced to 0.0374 W/(m·K), representing an approximately 12% reduction compared to similar materials. At a thermal conductivity of 0.0397 W/(m·K), the material maintains optimal states in compressive strength, volumetric water absorption, and total thermal resistance. Simultaneously, the aerogel dosage is only 6.8 kg/m³, demonstrating the best balance between performance and cost. This achievement provides a novel design strategy for large-scale industrial production. Pareto principle ; Sample (material) ; Inverse ; Bayesian probability ; Cement ; Aerogel ; Mathematical optimization ; Sample size determination ; Multi-objective optimization ; Materials science ; Computer science ; Mathematics ; Statistics ; Composite material ; Chromatography ; Chemistry Aerogels and thermal insulation ; Building Energy and Comfort Optimization
Authors: Yiwen Zheng (284338) | Vikas Varshney (1458727) | Aniruddh Vashisth (5578466)
Source: Figshare (article, 2025-04-21)
Field: Materials Science — Polymers and Plastics

AcceleratedReaxFF Simulations of Vitrimers with DynamicCovalent Adaptive Networks Vitrimers are a novel class of sustainable polymers with dynamic covalent adaptive networks driven by bond-exchange reactions between different constituents, making vitrimers reprocessable and recyclable. Current modeling approaches of bond-exchange reactions fall short in realistically capturing the complete reaction pathways, which limits our understanding of the viscoelastic properties of vitrimers. This research addresses these limitations by extending and employing the Accelerated reactive molecular dynamics (ReaxFF) technique, thus enabling a more accurate representation of vitrimer viscoelastic behavior at the molecular level. Bayesian optimization is employed to select force field parameters within the Accelerated ReaxFF framework, and an empirical function is proposed to model temperature dependence, thereby controlling the reaction probabilities under varying temperatures. The extended framework is employed to simulate nonisothermal creep behavior of vitrimers under different applied stress levels, heating rates, and numbers of reactions. The simulation results agree with experimental findings in the literature, validating the robustness of the framework. ReaxFF ; Viscoelasticity ; Molecular dynamics ; Robustness (evolution) ; Creep ; Force field (fiction) ; Field (mathematics) Polymer composites and self-healing ; Vibration Control and Rheological Fluids ; Hydrogels: synthesis, properties, applications
Authors: Wenrui Li | Yong Huang | Qi Zhang | Zhen Zhang
Source: Results in Engineering (article, 2025-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational bioinspired structural design for sustainable and secure high-performance energy storage materials • Rapid advancements in energy storage demand the development of high-performance materials. • Bioinspired structural designs enhance energy storage by nature’s hierarchical architectures. • Bayesian optimization reduced experimental trials by 35% and improved charge retention by 15%. • The results supports development of next-generation, high-efficiency energy storage materials. • It also enables potential applications in batteries, supercapacitors, and renewable energy systems. As energy storage technologies advance rapidly, high-performance materials with higher electrochemical efficiency, mechanical robustness, and long-term durability are needed. Energy storage materials may benefit from bioinspired structural designs. These designs mimic natural hierarchies and biological processes. However, current research does not give a full foundation for autonomously modifying these structures to improve performance. Biomechanics and computational optimization are used to build and evaluate bioinspired energy storage materials. Examples include Monte Carlo Simulations, Multi-scale Statistical Modeling, Bayesian Optimization, and Frame Analysis. According to multi-scale statistical modeling, hierarchical structures have 25% more energy storage capacity, 30% more ion diffusion, and 20% more mechanical toughness than standard materials. Bayesian optimization cut experimental trials by 35%, making the design more efficient and cost-effective. Additionally, charge retention increased by 15%, and efficiency increased. Energy storage capacity increased by 20% in materials with fractal dimensions of 1.5 to 2.5.Monte Carlo simulations validated the probabilistic durability of the optimized materials, demonstrating a 98% confidence level in their mechanical stability over 10,000 charge cycles. Energy storage ; Computer science ; Sustainable energy ; Architectural engineering ; Nanotechnology ; Materials science ; Systems engineering ; Construction engineering ; Engineering ; Renewable energy ; Physics ; Electrical engineering Supercapacitor Materials and Fabrication ; Hydrogen Storage and Materials ; MXene and MAX Phase Materials
Authors: Ayana Ghosh | Maxim Ziatdinov | Sergei V. Kalinin
Source: APL Machine Learning (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Active deep kernel learning of molecular properties from structural embeddings As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular properties. This paper presents an active learning approach for molecular discovery using deep kernel learning (DKL), demonstrated on the QM9 dataset. DKL links structural embeddings directly to properties, creating organized latent spaces that prioritize relevant property information. By iteratively recalculating embedding vectors in alignment with target properties, DKL uncovers concentrated maxima representing key molecular properties and reveals unexplored regions with potential for innovation. This approach underscores DKL’s potential in advancing molecular research and discovery. Embedding ; Leverage (statistics) ; Property (philosophy) ; Kernel (algebra) ; Key (lock) ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michal Dabros (10343989) | Hagen Münkler (22060247) | Florence Yerly (18068440) | Roger Marti (1496101) | Michaël Parmentier (2156500) | Anikó Udvarhelyi (1553398)
Source: Figshare (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Quantum Descriptor-BasedMachine-Learning Modelingof Thermal Hazard of Cyclic Sulfamidates Cyclic sulfamidates are commonly used building blocks in organic synthesis. Correct classification of their thermal criticality is crucial for the safe use of these compounds in process development and scale-up. In this study, building on our earlier work (Ferrari et al., 2022), we focused on modeling the reaction enthalpy of a family of 5-membered cyclic sulfamidates toward strong bases. The key challenge for the modeling task was the sparse availability of measured reaction enthalpies, with only 29 measurements available. To address this challenge, we used descriptors based on the quantum-chemical properties of the molecules, as they are more closely related to reaction enthalpies than typical cheminformatics-based descriptors. This approach allowed us to avoid relying solely on data-to-fit models and to focus instead on modeling reaction enthalpies using chemistry-aware techniques, which are more appropriate for small data sets. Three models were constructed using the quantum-chemical descriptors: the first one combining Partial Least Squares (PLS) regression with a Genetic Algorithm (GA), the second one based on the Least Absolute Shrinkage and Selection Operator (LASSO) method, and last, a Gaussian Process Regression (GPR) model. The three models achieved coefficients of determination of 0.78, 0.67, and 0.74, respectively. Although the absolute prediction error values were close to 100 J/g, it is noteworthy that all three techniques provided similar results and accurately classified nearly all compounds into their respective thermal criticality classes. This highlights the methodology’s effectiveness in providing a reliable framework for preliminary safety assessment and decision-making in process development. Criticality ; Process (computing) ; Gaussian ; Quantum ; Hazard ; Work (physics) ; Regression ; Partial least squares regression ; Linear regression Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Synthesis and Catalytic Reactions
Authors: Li Ma | Xiao-Fei Duan | Keyuan Chen | C. T. Hou | Hanqing Li | Jueyi Ye | You Wu | Ju Rong | Xiaohua Yu | Wei Yan | Jing Feng
Source: Chemical Engineering Science (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Deep potential model-based study on the thermodynamic, kinetic, and mechanical properties of PtNi alloys Phonon ; Molecular dynamics ; Thermal ; Density functional theory ; Aerospace ; Dispersion (optics) ; Lattice (music) ; Potential energy ; Melting point ; Lattice constant Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Nuclear Materials and Properties