jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Taiyo Maeda | Muhammad Aiman B Musa | Toshio Osada | Shingo Ozaki
Source: Science and Technology of Advanced Materials Methods (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization The intrinsic brittleness and high defect sensitivity of ceramics result in significant strength variability, presenting substantial challenges for structural reliability assessments. Experimental characterization of strength scatter in ceramic components is both time-consuming and costly. Conventional physics-based (forward) analyses can model strength scatter based on microstructural data; these methods are computationally intensive. This study introduces a deep learning – based surrogate model that directly predicts the Weibull distribution parameters of ceramic bending strength from equivalent crack length distributions, achieving substantial reductions in computational cost without compromising predictive accuracy. Additionally, an inverse analysis framework is developed by integrating the surrogate model with intelligent swarm optimization, enabling the estimation of defect distributions from reference strength measurements. The proposed approach demonstrates high accuracy and efficiency, achieving over 200-fold speedup in forward analysis and 18,000-fold in inverse analysis. It facilitates a rapid and reliable evaluation of the relationship between defect distributions and strength scatter as characterized by Weibull parameters. This methodology provides a robust tool for accelerating the design and development of high-performance ceramic materials. Deep learning ; Brittleness ; Particle swarm optimization ; Ceramic ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced ceramic materials synthesis
Authors: Sudheesh Parathakkatt | Vaisakh Kizhuveetil | Gokul G. K. | Rajashree Ajay Markandewar
Source: eXPRESS Polymer Letters (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Shear banding and flow instabilities in wormlike micelles: Modelling and mechanisms – A review Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review provides a comprehensive analysis of recent theoretical advances in modelling WLM rheology, from classical reptation–scission theories to modern stochastic simulations and multi-scale population-balance frameworks. A central challenge addressed is the rheological indistinguishability of competing models under linear conditions, which renders inverse modelling ill-posed and necessitates the integration of experimental data, such as cryogenic transmission electron microscopy (cryo-TEM), small-angle neutron scattering (SANS), and flow birefringence, to constrain theoretical predictions. The article further explores the limitations of conventional models in capturing nonlinear responses, including shear banding and extensional strain hardening, and emphasizes the need for spatially resolved, structurally informed constitutive equations. Emerging tools, including neural networks and hybrid modular frameworks, are identified as promising solutions for bridging microscopic rearrangement dynamics with macroscopic flow behaviour. Ultimately, the development of predictive, physically grounded WLM models will be essential for advancing applications in formulation science, smart materials, and industrial processing. Shear (geology) ; Flow (mathematics) ; Shear flow ; Flow properties ; Instability Surfactants and Colloidal Systems ; Micro and Nano Robotics ; Rheology and Fluid Dynamics Studies
Authors: Zhenyu Feng | Yulong Liu | Chengyi Zhu | Shuai Xu | Yulong Cao | Meijie Zhang
Source: Materials Today Communications (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development and validation of iron loss prediction model for non-oriented electrical steel based on physics-informed machine learning To evaluate the key factors influencing on the iron loss of non-oriented electrical steels (NOES) and predict their properties, Physics-Informed Machine Learning (PIML) theory combined with the XGBoost algorithm was employed to develop a NOES-PIML iron-loss prediction model, which integrates physics-based empirical formulas for calculating resistivity and density. The model was further optimized through inverse optimization using the Particle Swarm Optimization (PSO) algorithm to obtain chemical compositions and annealing temperature combinations to reduce iron loss of NOES. A dataset of 600 samples containing chemical composition, annealing temperature and magnetic properties was used for model training and validation. Incorporating physical features such as resistivity and density significantly enhanced the model performance, and the coefficient of determination R ² is 0.98, which indicated a 5% improvement compared with those without physical features. Experimental results and predicted values were compared before and after the introduction of physical features, and residual distribution analysis confirmed that their physical features improved model convergence and prediction accuracy. The interpretability of the model was further analyzed using SHapley Additive exPlanations (SHAP) theory, which revealed the contribution of individual features to model prediction. Then, NOES used for validating the model were prepared based on the chemical compositions and annealing temperature obtained from the optimized results. The iron loss of the finished steel was 3.82 W/kg with a decrease of 6.4% compared with the predicted value. Both iron loss and magnetic induction of the steels were superior to those of similar products specified in the standards, confirming the accuracy and practical applicability of the prediction model. The results demonstrated that NOES-PIML can provide accurate prediction for iron loss using small datasets and be used to design new products or predict their properties. Interpretability ; Particle swarm optimization ; Electrical steel ; Annealing (glass) ; Electrical resistivity and conductivity ; Residual ; Simulated annealing Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgical Processes and Thermodynamics
Authors: Can Cui | Kaiyuan Zheng | J.H. Liu | ChaoQiang Chen | XiaoHong Sun
Source: Results in Engineering (article, 2026-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-performance chiral metasurface sensors optimized by a target-driven active learning framework • A hybrid target-driven active learning framework is proposed for the efficient inverse design of chiral metasurfaces, combining uncertainty-aware reliability filtering with global optimization. • The core active learning engine uses an ensemble of CNNs to quantify prediction uncertainty, guiding the selection of structures for costly simulation. • This approach reduces the design cycle from months to 10 days . • The framework successfully designs meta-units with strong circular dichroism (CD > 0.7) and achieves excellent regression performance with a validation mean squared error (MSE) of 2.1 × 10 -4 and an R 2 of 0.9969. In recent years, with the rise of artificial intelligence (AI) algorithms, the development of nanophotonics has been significantly promoted, and shown great potential in material design, device optimization and performance prediction. To address the inefficiency of traditional nanophotonic design methods and the challenges of multi-dimensional parameter optimization, we propose a target-driven active learning framework that synergistically integrates uncertainty-aware reliability filtering with efficient global optimization for the inverse design of chiral metasurface sensors. The framework employs an ensemble of Convolutional Neural Networks (CNNs) as a surrogate model and leverages their prediction disagreement as an uncertainty metric to guide the Tree-structured Parzen Estimator (TPE) in proposing high-potential candidates for costly electromagnetic simulations. By integrating this method with nanophotonic design principles, we construct a reverse design model of metal structures, optimize the design of chiral structures with significant resonance peak characteristics, and advocate a chiral metasurface sensor for the highly sensitive detection of chiral molecular enantiomers. By drastically reducing the required number of full-wave electromagnetic simulations to 8000, this target-driven method achieves excellent regression performance with a validation mean squared error (MSE) of 2.1 × 10 -4 and an R 2 of 0.9969, effectively shortening the iterative design cycle to approximately ten days. Furthermore, the optimized sensor exhibits a strictly linear response in the weak handedness regime with a quantified sensitivity slope of S ≈ 0.5 , paving the way for AI-assisted chiral sensing applications in biosensing and new drug development. Nanophotonics ; Metric (unit) ; Reliability (semiconductor) ; Surrogate model ; Bayesian optimization ; Performance metric ; Inverse ; Convolutional neural network ; Estimator Metamaterials and Metasurfaces Applications ; Synthesis and Properties of Aromatic Compounds ; Advanced Memory and Neural Computing
Authors: Djamel Ghernaout Ghernaout | Messaouda Boumaaza | Ahmed Belaadi | Noureddine Elboughdiri | Mhamed Benaissa | Ibrahim M. H. Alshaikh
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Determining Water Absorption in Hybrid Biocomposites Made from <i>Syagrus romanzoffiana</i> Palm Waste Biomass and Fibers: Optimizing with ANN and RSM Methods The water absorption (WA) behavior of hybrid biocomposites reinforced with <i>Syagrus romanzoffiana</i> palm fibers (<i>Sr</i>PFs) and <i>Syagrus romanzoffiana</i> palm waste (biochar, <i>Sr</i>PW) shows a nonlinear relationship with immersion time and biochar content. This study explores the use of response surface methodology (RSM) and artificial neural networks (ANN) for multi-objective optimization and predictive modeling of this behavior. A hybrid model combining ANN with a genetic algorithm (GA) and RSM was developed to predict WA over immersion periods from 24 to 720 hours and <i>Sr</i>PW contents from 0.5% to 2%. The model was further enhanced using a Multi-Criteria Decision-Making (MCDM) approach with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The ANN-GA model predicted WA with reasonable accuracy (2.72% at 672 hours), closely matching experimental results, as confirmed by validation tests. At 615 hours, RSM optimization resulted in a slightly lower WA (2.68%). These findings demonstrate the reliability of the proposed modeling framework in reducing experimental work and guiding material design. The biocomposite exhibits potential for eco-friendly applications, particularly in the automotive sector. Response surface methodology ; Biocomposite ; Absorption of water ; Artificial neural network ; Palm ; Multi-objective optimization ; Biomass (ecology) ; Design of experiments Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Hygrothermal properties of building materials
Authors: Qingsong Song | Guangyu Wang | Yuan Liufu | Hongtao Li | Yunguang Ji | Lixin Wang
Source: Results in Engineering (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Co-optimization of area ratio and material fraction in segmented N-type thermoelectric legs using XGBoost–ANN surrogates • Coupled optimization of area ratio and material fraction via XGBoost–ANN surrogates boosts STEG performance. • Co-optimization achieves 9.91% higher power and 8.69% higher efficiency over conventional designs. • XGBoost-ANN surrogate model enables sub-5% error performance prediction for engineering applications. Spacecraft operate in extreme environments and generate abundant waste heat from power, thermal-control and propulsion subsystems. Thermoelectric generators directly convert this waste heat into electricity, delivering solid-state, reliable, maintenance-free onboard power. However, the energy conversion efficiency and long-term stability of conventional thermoelectric technologies remain constrained under large temperature gradients. Here, a segmented thermoelectric generator is proposed with an optimized n-type leg architecture. By jointly tailoring the cross-sectional geometry and material distribution, the design strengthens carrier transport and boosts both conversion efficiency and output power. Comprehensive multiphysics simulations conducted on the COMSOL platform systematically evaluate the effects of cross-sectional area ratios and material fractions on device performance. The optimized segmented architecture achieves a 9.91% increase in output power and a 8.69% enhancement in thermoelectric conversion efficiency compared with conventional designs. Furthermore, eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN) models are developed to predict thermoelectric performance. XGBoost effectively captures the nonlinear relationships between input parameters and output metrics, yielding prediction errors as low as 0.42% and 0.51%, while ANN demonstrates strong performance in multi-output modeling, with coefficients of determination (R 2 ) of 0.9577 and 0.9603, respectively. These results show that combining segmented-structure optimization with surrogate modeling can markedly enhance thermoelectric energy conversion. This integrated strategy offers a practical route to high-efficiency onboard energy management in advanced spacecraft systems. Thermoelectric effect ; Fraction (chemistry) ; Thermoelectric materials ; Work (physics) ; Material properties Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Silicon Carbide Semiconductor Technologies
Authors: Jonas Sá | Brahim Aïssa | Kamal H. Mroué | Michael Jones | Richard Woollam | Richard Barker
Source:  (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Mechanistic CO2 Corrosion Model Optimization Supported by Machine Learning Abstract In this work, we used a deep neural network (DNN) model to identify outliers generated by a CO2 corrosion multi-physics (MP) model and guide the process of improving its response. Parametric studies were conducted over an extensive range of bulk pH, fluid velocity, temperature, pipe diameter and CO2 partial pressure. The output response in terms of corrosion rate was fed into an established DNN model to identify outliers in the dataset. Considerable discrepancies were observed between the results generated by both models, particularly in the low fluid velocity regime and lower Reynolds number range. To address this, the mechanistic model was revised, updating its mesh structure and optimizing solver parameters to enhance numerical convergence and solution stability. The output from the revised mechanistic model and the DNN model were subsequently compared, the improvement in model convergence was detailed and the source of improvement was discussed. Comparative analysis of the original and revised multi-physics models revealed that the original model exhibited a bias toward underpredicting corrosion rates. This was a consequence of the relatively higher surface pH, which suppresses cathodic reactions. Solver ; Convergence (economics) ; Corrosion ; Artificial neural network ; Outlier ; Range (aeronautics) ; Parametric statistics ; Process (computing) Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Michael Kim | Wei Cai
Source: arXiv (Cornell University) (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Accelerated Markov Chain Monte Carlo Simulation via Neural Network-Driven Importance Sampling Atomistic simulations provide valuable insights into the physical processes governing material behavior. However, their applicability is fundamentally constrained by the limited time scales accessible to brute-force simulations. This bottleneck often stems from complex energy landscapes where the systems stay trapped in metastable states for long periods of time. Yet, the long-term evolution is controlled by the transitions between the metastable states, which are rare events and difficult to observe. We present an importance sampling method designed to accelerate the time scale of Markov chain Monte Carlo (MCMC) simulations. By employing a bias potential, our approach enhances the sampling of rare transition events while preserving the relative probabilities of distinct transition pathways. The bias potential is represented by a neural network which enables the flexibility needed for high-dimensional systems. We propose a rigorous formulation to obtain the original transition rates between metastable states using transition paths obtained from the biased simulation. We further use a branching random walk (BRW) technique to enhance efficiency and to reduce variance. The proposed methodology is validated on 2-dimensional and 14-dimensional systems, demonstrating its accuracy and scalability. Markov chain Monte Carlo ; Bottleneck ; Rare events ; Markov chain ; Metastability ; Monte Carlo method ; Flexibility (engineering) ; Importance sampling ; Sampling (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Material Dynamics and Properties
Authors: Enrico Trizio | Giorgia Rossi | Michele Parrinello
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Ceci n'est pas un committor, yet it samples like one: efficient sampling via approximated committor functions Atomistic simulations are widely used to investigate reactive processes but are often limited by the rare event problem due to kinetic bottlenecks. We recently introduced an enhanced sampling approach based on the committor function, machine-learned following a variational principle. This method combines a transition-state-oriented bias potential, expressed as a functional of the committor, with a metadynamics-like bias along a committor-based collective variable, enabling uniform exploration of reaction pathways. In its original formulation, the committor is represented by a neural network that takes physical descriptors as input and is trained by minimizing a functional involving gradients with respect to atomic coordinates, which can be computationally demanding in some cases. Here, we propose a simplified learning criterion formulated entirely in the descriptor space, which bypasses the need for explicit and costly coordinate gradients and provides a relaxed upper bound to the original variational principle. Although this approach does not formally target the exact committor, we show that it retains robust sampling performance while significantly reducing computational costs, thus enabling the study of processes that would be practically unfeasible using the original formulation. Sampling (signal processing) ; Upper and lower bounds ; Artificial neural network ; Event (particle physics) ; Function (biology) ; Importance sampling Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Michael Kim | Wei Cai
Source: Open MIND (preprint, 2026-01-31)
Field: Materials Science — Materials Chemistry

Accelerated Markov Chain Monte Carlo Simulation via Neural Network-Driven Importance Sampling Atomistic simulations provide valuable insights into the physical processes governing material behavior. However, their applicability is fundamentally constrained by the limited time scales accessible to brute-force simulations. This bottleneck often stems from complex energy landscapes where the systems stay trapped in metastable states for long periods of time. Yet, the long-term evolution is controlled by the transitions between the metastable states, which are rare events and difficult to observe. We present an importance sampling method designed to accelerate the time scale of Markov chain Monte Carlo (MCMC) simulations. By employing a bias potential, our approach enhances the sampling of rare transition events while preserving the relative probabilities of distinct transition pathways. The bias potential is represented by a neural network which enables the flexibility needed for high-dimensional systems. We propose a rigorous formulation to obtain the original transition rates between metastable states using transition paths obtained from the biased simulation. We further use a branching random walk (BRW) technique to enhance efficiency and to reduce variance. The proposed methodology is validated on 2-dimensional and 14-dimensional systems, demonstrating its accuracy and scalability. Markov chain Monte Carlo ; Bottleneck ; Rare events ; Markov chain ; Metastability ; Monte Carlo method ; Flexibility (engineering) ; Importance sampling ; Sampling (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Material Dynamics and Properties
Authors: Jinpeng Zhao | Cheng Qi | Yu Jiang | Liming Zhang | Weidong Dai | Mengya Zhang | Shangqing Xu | Weichang Zhou | Hui Li | Tiefeng Yang | Yipeng Zhao | Yicheng Wang | Xing Xu | Liang Ma
Source: Applied Physics Letters (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Self-powered broadband photodetector with intelligent sensing based on a mixed-dimensional TiS3/WSe2 van der Waals heterojunction Broadband, high-efficiency, and low-power multifunctional integrated photodetectors are essential for advanced applications, including imaging, optical communication, and remote sensing. Photodetectors based solely on two-dimensional materials still encounter challenges such as limited out-of-plane charge transport, relatively low in-plane carrier mobility, and inadequate photocurrent generation. Here, we report a mixed-dimensional TiS3/WSe2 van der Waals heterojunction photodetector with type-II band alignment, enabling efficient carrier separation. The device achieves broadband detection from 405 to 1050 nm, with a responsivity of 1.61 A/W, an external quantum efficiency of 261.21%, and a specific detectivity of 2.31 × 1011 Jones under a bias voltage of 1 V. Under zero bias, the device exhibits a responsivity of 52 mA/W, a specific detectivity of 1.19 × 1011 Jones, and an open-circuit voltage of 145 mV. Furthermore, integrated with a convolutional neural network, the TiS3/WSe2 photodetector enables handwritten digit recognition accuracies of up to 97.0% under biased operation and 88.7% under self-powered sensing conditions. These results establish the TiS3/WSe2 heterojunction as a promising platform for broadband, low-power, and self-powered intelligent optoelectronics. Photodetector ; Responsivity ; Photocurrent ; Heterojunction ; Quantum efficiency ; van der Waals force ; Specific detectivity ; Broadband 2D Materials and Applications ; Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials