jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: MA Wei-bin | Ling Li | Yufan Zhang | Minjie Li | Na Song | Peng Ding
Source: Journal of Materials Informatics (article, 2025-05-21)
Field: Materials Science — Polymers and Plastics

Active learning-based generative design of halogen-free flame-retardant polymeric composites It is of significant importance to design flame-retardant polymeric composites (FRPCs) with superior flame retardancy and appropriate mechanical properties. However, discovering such materials is often reliant on serendipity, as the conventional “trial-and-error” approach is inadequate for navigating the vast virtual space. To overcome this challenge, we propose an active generative design framework to accelerate the development of FRPCs within the expansive virtual space. This framework operates as a closed-loop system, integrating machine learning, knowledge-embedded generative model, and experimental exploration. Through this approach, we derived two interpretable linear expressions and identified a key composition threshold that when the mass fraction of zinc stannate is below 2.5% and that of piperazine pyrophosphate exceeds 12.5%, the flame retardancy of polypropylene (PP)-based FRPCs is significantly enhanced. By processing and characterizing 10 FRPCs, we successfully designed two composites with flame retardancy improved by 1% compared to the top-performing reference FRPC in the initial dataset - without compromising mechanical properties. This work effectively resolves the trade-off between flame retardancy and mechanical performance at a low cost, demonstrating a promising pathway for the accelerated discovery of PP-based FRPCs with balanced properties. Fire retardant ; Composite material ; Materials science ; Halogen ; Generative grammar ; Polymer science ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Polymer composites and self-healing
Authors: Xin Shen | Jia She | Xianhua Chen | Chao Duan | Senwei Wang | Lei Shen | Fugui He
Source: Materials & Design (article, 2025-04-23)
Field: Materials Science — Biomaterials

Structural optimization of magnesium alloy vascular scaffolds for resistance to vascular plaque stress damage • A shape optimization strategy was proposed for a thin-walled (110 μm) scaffold. • The scaffold remained biocompatible and safe for 6 months in vivo. • Have better collateral vessel passability of the scaffold. • The radial strength of the optimized thin-walled scaffold (110 μm) can reach 180 kPa. The majority of researchers primarily focused on the scaffold’s stress and strain in the design of biodegradable magnesium alloy scaffolds. However, in clinical applications, the flawed scaffold structure design will result in acute thrombosis and plaque rupture, which are factors that are often ignored in scaffold designs. In this research, we report on a new concept, taking vascular plaque stress damage as the design index of scaffold structure, and the finite element multi-objective neural network algorithm is responsible for the optimal design. Mg-xGd-5Y alloy with uniform degradation behavior is used as the basis of BMgS. Based on the observation of radial strength measurement, push measurement and collateral vessel passability measurement conditions verification, the optimized magnesium alloy scaffold was implanted into the coronary arteries of Bama minipigs. Quantitative optical coherence tomography (OCT) was used for observation at 1, 3, and 6 months of follow-up in vivo. Neither early restenosis nor thrombus were seen. The stress-induced damage of vascular plaque offers a novel methodology for the structural design of magnesium alloy scaffolds. Comprehensive validation of in vitro physical and in vivo biomechanical properties confirms the reliability of this approach, thereby advancing the development of biodegradable magnesium scaffolds. Materials science ; Magnesium alloy ; Alloy ; Magnesium ; Metallurgy Magnesium Alloys: Properties and Applications ; Electrospun Nanofibers in Biomedical Applications ; Esophageal and GI Pathology
Authors: Michael Gyaabeng | Ramadan Ahmed | Nayem Ahmed | Cătălin Teodoriu | Deepak Devegowda
Source: Metals (article, 2025-05-25)
Field: Materials Science — Metals and Alloys

Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Low-Carbon Steel Using a Machine Learning Approach This study aims to advance the understanding of hydrogen embrittlement (HE) in low-carbon and low-alloy steels by developing a predictive framework for assessing fracture toughness (FT), a critical parameter for mitigating HE in hydrogen infrastructure. A machine learning (ML) model was constructed by analyzing data from relevant literature to evaluate the fracture toughness of steels exposed to hydrogen environments. Seven ML modeling techniques were initially considered, with four selected for detailed evaluation based on predictive accuracy. The chosen modeling techniques were k-nearest neighbors (KNN), random forest (RF), gradient boosting (GB), and decision tree regression (DT). The selected models were further evaluated for their predictive accuracy and reliability, and the best model was used to perform parametric studies to investigate the impact of relevant parameters on FT. According to the results, the KNN model demonstrated reliable predictive performance, supported by high R-squared values and low error metrics. Among the variables considered, hydrogen pressure and yield strength emerged as the most influential, with hydrogen pressure alone accounting for 32% of the variation in FT. The model revealed a distinct trend in FT behavior, showing a significant decline at low hydrogen pressures (0–6.9 MPa) and a plateau at higher pressures (&gt;8 MPa), indicating a saturation point. Alloying element contents, specifically those of carbon and phosphorus, also played a notable role in FT prediction. Additionally, the study confirmed that low concentrations of oxygen (&lt;200 ppm) mitigate HE in X70 steel, likely by limiting hydrogen uptake. FT predictions do not show noticeable variations with lower displacement rates (&lt;0.1 mm/min), indicating the need for low-rate measurements for accurate ML model training. Hydrogen embrittlement ; Embrittlement ; Fracture toughness ; Materials science ; Carbon steel ; Carbon fibers ; Environmental stress fracture ; Fracture (geology) ; Toughness ; Metallurgy ; Hydrogen ; Composite material ; Corrosion ; Chemistry ; Composite number Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques
Authors: Himanshu Prasad Mamgain | Jitendra Kumar Pandey | Pravat Ranjan Pati | Ranjeet Brajpuriya
Source: Next Materials (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Predictive modeling and stability analysis of tetradecanoic acid-modified zinc/zinc oxide coatings using machine learning This study investigates the mechanical, thermal, and chemical stability of tetradecanoic acid-modified Zn/ZnO (Zn-MA) coatings using machine learning (ML) for predictive modeling. Abrasion tests showed that Zn-MA coatings retained superhydrophobicity (WCA > 135°) up to 600 cycles. Among ML models, XGBoost best captured nonlinear mechanical degradation (Test R² = 0.9596), polynomial regression (order 3) accurately predicted thermal stability (Test R² = 0.9870), and random forest effectively modeled chemical resistance (Test R² = 0.8666). Integrated ML predictions under combined stresses indicated that Zn-MA coatings maintain hydrophobicity under thermal and chemical challenges, with reduction in mechanical resilience. These findings demonstrate that ML-driven modeling can reliably predict coating performance. Stability (learning theory) ; Coating ; Thermal stability ; Abrasion (mechanical) ; Oxide ; Degradation (telecommunications) ; Thermal ; Chemical stability ; Stress (linguistics) Corrosion Behavior and Inhibition ; Analytical Chemistry and Sensors ; High voltage insulation and dielectric phenomena
Authors: Yagyank Srivastava | Amey G. Gokhale | Ankit Jain
Source: Physical review. B./Physical review. B (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Computational uncertainties in the prediction of lattice thermal conductivity of crystalline solids We report computational uncertainties in Boltzmann transport equation (BTE) based lattice thermal conductivity predictions of 50 diverse semiconductors from the use of different BTE solvers (ShengBTE, Phono3Py, and in-house code) and interatomic forces. The interatomic forces are obtained either using the density functional theory (DFT) as implemented in packages Quantum Espresso and VASP employing commonly used exchange correlation functionals (PBE, LDA, PBEsol, and rSCAN) or using the pretrained foundational machine learning (ML) force fields trained on two different material datasets. We find that the considered BTE solvers introduce minimal uncertainties and, using the same interatomic force constants, all solvers result in an excellent agreement with each other, with a mean absolute percentage error (MAPE) of only 1%. While this error increases to around 10% with the use of different DFT packages, the error is still small and can be reduced further with the use of stringent plane-wave energy cutoffs. On the other hand, the differences in thermal conductivity due to the use of different exchange correlation functionals are large, with a MAPE of more than 20%. The currently available pretrained foundational ML models predict the right trend for thermal conductivity, but the associated errors are high, limiting their applications for coarse screening of materials. Thermal conductivity ; Limiting ; Interatomic potential ; Density functional theory ; Boltzmann equation ; Lattice (music) ; Computation ; Thermal ; Conductivity Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Luca Murg | Shao-Chun Lee | Vitor F. Grizzi | Yang Zhang
Source: Journal of Applied Physics (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Enhanced exploration of LiF–NaF thermal conductivity through transferable equivariant graph neural networks Although molten salt reactors and thermal storage systems are attracting increasing interest, our understanding of the physicochemical properties of molten salts is still incomplete. This is largely due to the difficulty of conducting experiments under extreme temperatures with strict control of impurities and corrosion. Ab initio calculations, machine-learned force fields, and classical molecular dynamics have helped to alleviate some of these issues. However, discrepancies between experimental and theoretical computations of the thermal conductivity of fluoride molten salts have become of increasing concern. In this paper, we present a modernized method for training a transferable equivariant graph neural network force fields to model a simple fluoride molten salt system, LiF–NaF, using minimal ab initio calculations. Using this transferable machine-learned force field, the thermal conductivity as well as various other functions of LiF–NaF were computed at various chemical temperatures and ratios in order to gain new insights into the limitations and behaviors of molten salts in relation to their thermal conductivity. Results show discrepancies between experimental and theoretical computations of the thermal conductivity as a function of temperature but good agreement between experimental and theoretical computations of the thermal conductivity as a function of ratio. Secondary results show compelling agreement of a machine-learned force field with first-principles computations and the ability to interpolate and extrapolate various chemical ratios. Thermal conductivity ; Molten salt ; Ab initio ; Computation ; Chemistry ; Thermal ; Materials science ; Thermodynamics ; Chemical physics ; Inorganic chemistry ; Composite material ; Computer science ; Physics ; Organic chemistry ; Algorithm Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Battery Materials and Technologies
Authors: Kaiyi Zheng | Xianwen Shang | Qin Zhou | Yang Zhang | Jiyong Shi | Xiaobo Zou | Meng Zhang
Source: Foods (review, 2025-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Advances in SERS-Based Detection of Organophosphorus Pesticides in Food: A Critical and Comprehensive Review Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure-performance relationships between nanomaterial design and molecular enhancement mechanisms. Functional groups such as P=O, P=S, and aromatic rings are highlighted as key determinants of Raman activity through combined chemical and electromagnetic effects. State-of-the-art substrates, including noble metals, carbon-based materials, bimetallic hybrids, MOF-derived systems, and emerging liquid metals, are critically evaluated with respect to sensitivity, stability, and applicability in typical matrices such as fruit and vegetable surfaces, juices, grains, and agricultural waters. Reported performance commonly achieves sub-μg L<sup>-1</sup> to low μg L<sup>-1</sup> detection limits in liquids and 10<sup>-3</sup> to 10 μg cm<sup>-2</sup> on surfaces, with reproducibility often in the 5-15% RSD range under optimized conditions. Persistent challenges are also emphasized, including substrate variability, quantitative accuracy under matrix interference, and limited portability for real-world applications. Structure-response correlation models and data-driven strategies are discussed as tools to improve substrate predictability. Although AI and machine learning show promise for automated spectral interpretation and high-throughput screening, current applications remain primarily proof-of-concept rather than routine workflows. Future priorities include standardized fabrication protocols, portable detection systems, and computation-guided multidimensional designs to accelerate translation from laboratory research to practical deployment in food safety and environmental surveillance. Complex matrix ; Software portability ; Substrate (aquarium) ; Multiplex ; Pesticide Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses
Authors: Tapas Chatterjee | S. El-Borgi | Mohamed Trabelssi | Michael I. Friswell
Source: Probabilistic Engineering Mechanics (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Stochastic dispersion behavior and optimal design of locally resonant metamaterial nanobeams using nonlocal strain gradient theory This study examines the stochastic response of a metamaterial (MM) nanobeam, focusing on bandgap formation and analyzed using machine learning. The nanobeam is modeled as an infinitely long Euler Bernoulli beam with two length scale parameters: the nonlocal and strain gradient parameter. Periodically distributed linear resonators along its length introduce periodicity. The deterministic analysis is conducted by estimating bandgap edge frequencies using the dispersion of elastic waves in a representative unit cell. The impact of uncertainties on wave propagation behavior indicate that geometric properties predominantly influence variability in frequency response, followed by material properties, affecting the location and width of the bandgap. Scale dependent parameters, however, have a negligible effect. A Gaussian process (GP) surrogate model is employed to efficiently capture the stochastic behavior of the nanobeam. To highlight the utility of machine learning in computationally intensive tasks, a multi-objective optimization problem is formulated to tailor the bandgap features of the nanobeam. The offline-trained GP model yields a Pareto front of design configurations closely aligned with actual simulations, eliminating the need for repeated analyses during optimization. This surrogate based optimizer efficiently facilitates reverse engineering of MM designs for user defined wave dispersion characteristics, showcasing its potential for large scale optimization. Importantly, the stochastic dispersion framework grounded in nonlocal strain gradient theory can be directly applied to other periodic MM nanostructures. By varying unit cell configurations and materials within the same computational pipeline, new insights into bandgap emergence across applications ranging from phononic waveguides, nanoscale acoustic devices to structure–property relationships in next-generation MMs can be rapidly obtained. • Investigated stochastic bandgap behavior in nanobeams via machine learning. • Modeled nanobeams as nonlocal strain gradient beams with periodic resonators. • Analyzed effects of material, geometric, and scale uncertainties on bandgaps. • Applied Gaussian process modeling for efficient stochastic response analysis. • Optimized designs for tailored wave dispersion using multi-objective methods. Metamaterial ; Dispersion (optics) ; Strain (injury) ; Classical mechanics ; Physics ; Mechanics ; Mathematical analysis ; Statistical physics ; Mathematics ; Materials science ; Optics Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Mohammad Amin Ghanavati | Sohrab Rohani
Source: Crystal Growth & Design (article, 2025-03-28)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystal Formation Prediction: Hybrid GIN-Mordred Model Outperforms DFT-Based Methods Cocrystals offer significant potential across various industries, especially pharmaceuticals, by addressing the poor solubility of new drug candidates. However, traditional experimental screening for cocrystal formation is expensive and time-consuming, highlighting the need for predictive models. In this study, we compared four cocrystal prediction approaches: two deep learning (DL) models based on DFT-driven data (PointNet for electrostatic potential (ESP) maps and a novel LSTM for sequential hydrogen bond parameters), a novel hybrid model combining graph isomorphism networks (GIN) with Mordred descriptors, and the empirical Hydrogen Bond Energy (HBE) method. To perform this comparison, we compiled and carried out DFT calculations for 14,790 molecules (7395 pairs of successful and unsuccessful cocrystals). Notably, the GIN-Mordred model outperformed all other methods, achieving the highest balanced accuracy (BACC: 0.916), F1-score (0.956), recall (0.932), and AUC (0.97), with superior segregation performance in distinguishing between cocrystallization outcomes. Importantly, the GIN-Mordred model does not require costly DFT calculations, demonstrating that a combination of graph-based and descriptor-based molecular representation provides an efficient and accurate alternative for cocrystal prediction. This model significantly streamlines the process of tuning the physicochemical properties of crystalline materials for various applications. Cocrystal ; Chemistry ; Biological system ; Computer science ; Computational chemistry ; Materials science ; Molecule ; Hydrogen bond ; Organic chemistry ; Biology Crystallography and molecular interactions ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Ting Xu | Jilun Wang | Xiaohua Yu | Yutao Niu | Hanqing Li | Yongzhi Wu | Li Ma | Ju Rong
Source: Computational Materials Science (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

A new generation of functional desalted carbon and nitrogen membrane with photocatalytic and adsorption properties Photocatalysis ; Adsorption ; Nitrogen ; Membrane ; Carbon fibers ; Chemical engineering ; Chemistry ; Materials science ; Nanotechnology ; Catalysis ; Organic chemistry ; Biochemistry ; Composite material Graphene research and applications ; Fuel Cells and Related Materials ; Membrane Separation and Gas Transport
Authors: Vivin Vinod | Peter Zaspel
Source: Journal of Chemical Theory and Computation (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies Recent progress in machine learning (ML) has made high-accuracy quantum chemistry (QC) calculations more accessible. Of particular interest are multifidelity machine learning (MFML) methods, where training data from differing accuracies or fidelities are used. These methods usually employ a fixed scaling factor, γ, to relate the number of training samples across different fidelities, which reflects the cost and assumed sparsity of the data. This study investigates the impact of modifying γ on model efficiency and accuracy for the prediction of vertical excitation energies using the QeMFi benchmark data set. Further, this work introduces QC compute time-informed scaling factors, denoted as θ, that vary based on QC compute times at different fidelities. A novel error metric, error contours of MFML, is proposed to provide a comprehensive view of model error contributions from each fidelity. The results indicate that high model accuracy can be achieved with just 2 training samples at the target fidelity when a larger number of samples from lower fidelities are used. This is further illustrated through a novel concept, the Γ-curve, which compares model error against the time-cost of generating training samples, demonstrating that multifidelity models can achieve high accuracy while minimizing training data costs. Excitation ; Computer science ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhiwei Gao | Siwei Wu | Xin Li | Xiaoguang Zhou | Guangming Cao | Zhenyu Liu
Source: Journal of Materials Research and Technology (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Modelling strain-induced precipitation kinetics of Nb (C, N) by symbolic regression machine learning Because strain-induced precipitation (SIP) behavior in Nb micro-alloyed steels directly affects recovery and recrystallization processes during hot rolling, it is crucial to establish high-precision models for SIP kinetics. In this work, based on experimental results with Nb micro-alloyed steels, symbolic regression machine learning (SRML) was used to construct the models for precipitation start ( P s ) and finish times ( P f ) with concentrations of Nb, C, N, Mn, and Si in solution, deformation temperature ( T def ), strain ( ε ), strain rate ( ε ˙ ), Zener-Hollomon parameter ( Z ), and the supersaturation ratio ( K s ) being the model inputs. Compared to the Pereda model for P s and Medina model for P f , root mean square errors ( RMSEs ) were reduced by about 89% and 91%, respectively. Furthermore, the effects of Si and Mn contents, strain, and strain rate on the SIP kinetics were analyzed, showing good consistency with the experimental results. The precipitated volume fractions were calculated based on the JMAK equation with the precipitation start times by using different models, which further demonstrates that the SRML model outperforms conventional models. Finally, the static recrystallization critical temperatures (SRCT) were determined for different Nb micro-alloyed steels, which can guide the designs for new thermomechanical processing routes. Symbolic regression ; Materials science ; Precipitation ; Kinetics ; Strain (injury) ; Regression ; Regression analysis ; Metallurgy ; Artificial intelligence ; Statistics ; Machine learning ; Computer science ; Meteorology ; Mathematics ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties