jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaan Kırkağaç | Nadir Subaşı | Ertuğrul Adıgüzel
Source: Istanbul University - Journal of Electrical & Electronics Engineering (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Reliability Analysis of Al2O3/Epoxy Nanocomposites Under Surface Tracking Degradation Surface tracking resistance and long-term reliability are critical performance parameters for polymeric insulation materials used in high-voltage electrical applications. In this study, the tracking behavior of epoxy/aluminum oxide (Al2O3) nanocomposites was experimentally, statistically, and data-driven investigated under high electrical stress conditions. Epoxy composites containing 0, 1, 3, and 5 wt.% Al2O3 nanoparticles were fabricated and tested according to the International Electrotechnical Commission(IEC) 60112 standard at applied voltage levels of 400 V, 500 V, and 600 V, with repeated experiments conducted to ensure statistical consistency. The results demonstrated that Al2O3 incorporation significantly influences tracking resistance in a concentration-and voltage-dependent manner, where the composite containing 1 wt.% Al2O3 exhibited the highest tracking resistance at moderate voltage levels (400–500 V), achieving higher drop-to-failure values (defined as the number of contaminant droplets required to cause sustained surface tracking for more than 2 seconds or material burn-through, in accordance with IEC 60112) than pure epoxy. However, increasing the filler content beyond 1 wt.% led to a reduction in tracking lifetime, particularly under high electrical stress, which was associated with increased surface erosion and localized degradation. The degradation behavior at 600 V was further analyzed using two-parameter Weibull statistics, revealing a decrease in characteristic life with increasing Al2O3 content, while the 1 wt.% Al2O3 composite exhibited the highest Weibull shape parameter, indicating a stable and predictable wear-out dominated failure mechanism. In addition, machine learning models were developed to predict tracking lifetime with high accuracy (R2 &gt; 98%), and feature importance analysis identified erosion-related mass loss, filler content, and applied voltage as the dominant parameters governing tracking degradation. Overall, the strong agreement between experimental results, Weibull reliability analysis, and machine learning predictions provides a combined experimental and data-driven framework for evaluating and optimizing epoxy-based insulation materials for high-voltage applications. Cite this article as: K. Kırkağaç, N. Subaşı and E. Adıgüzel, “Reliability analysis of Al2O3/epoxy nanocomposites under surface tracking degradation,” Electrica, 26, 0017, doi: 10.5152/electr.2026.26017. Weibull distribution ; Tracking (education) ; Reliability (semiconductor) ; Nanocomposite ; Composite number ; Degradation (telecommunications) ; Nanoparticle tracking analysis ; Voltage ; Stress (linguistics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Ziyue Miao | Xichen Hu | Kai Liu | Shanming Hu | Guihua Yan | Hongwei Tan | Olli Ikkala | Zhong‐Peng Lv | Bo Peng
Source: Advanced Materials (article, 2026-03-26)
Field: Materials Science — Biomaterials

Bioinspired Sensory Transduction for Magnetic Profile Recognition and Encryption In biological systems, adaptive responses to environmental stimuli are facilitated by sensory transduction, where receptors transform stimuli into dynamic intermediate electrical signals for further processing. For bioinspired artificial systems, this suggests the need to develop concepts that transduce various stimuli into electrical intermediates for recognition. Inspired by biological magnetoreception in elasmobranchs, which sense magnetic environmental profiles for navigation, we introduce an artificial sensory transduction system for magnetic profile recognition of objects using electromagnetic induction to generate electrical intermediate signaling, coupled with machine learning for decoding. We design moldable magnetic soft composites (MSCs) comprising magnetic particles in a zwitterionic polymer matrix, encoding with both static (shape, rheology, and magnetization) and dynamic (magnetization decay) multidimensional features. Upon translocation through a receiving coil, MSCs generate distinct transient induced electrical signals. Machine learning algorithms decode the static and dynamic information with ∼100% and 87.5% recognition accuracy, respectively, with a recognition strength of 3 bits and a large information-carrying capacity of 10<sup>62</sup>-10<sup>934</sup> possible encoded states. We suggest that electromagnetic induction in soft composites is a useful and generalizable concept for sensory transduction in emerging adaptive dissipative bioinspired materials, haptic systems, and soft robotics. Sensory system ; Transduction (biophysics) ; Dissipative system ; Electromagnetic induction ; Biomimetics ; Encoding (memory) ; Transcranial magnetic stimulation ; SIGNAL (programming language) Calcium Carbonate Crystallization and Inhibition ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Wei, Rong | Partridge, Phillippa | Hong, Chi Cheng | Levinsky, Joshua J.B. | Hobday, Claire
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

The structural dynamics and barocaloric response of organic ionic plastic crystals Refrigeration systems account for 17% of global electricity consumption. As global temperatures rise, the demand for sustainable cooling technologies grows. Conventional refrigerants are highly efficient but have significant environmental impacts, necessitating greener alternatives. Barocaloric materials, which undergo large entropy and temperature changes under pressure, offer a promising solid-state solution. Organic ionic plastic crystals (OIPCs) exhibit multiple solid-solid phase transitions that can be driven by pressure, making them attractive barocaloric materials. OIPCs have tunable interionic interactions, and their characteristic phase transitions from highly orientationally disordered to ordered phase allow for large entropy changes. However, the microscopic mechanisms governing their phase behavior remain unclear. (Cyanomethyl)trimethylammonium hexafluorophosphate ([N111CN][PF6]) undergoes a four-step disorder-order transition with notable thermal hysteresis. Understanding the nature of the disorder in its high-temperature phase is crucial for optimising its barocaloric performance. This research compares ordered models of the disordered phase to explore its structural flexibility. Ab initio molecular dynamics simulations are employed to explore how the dynamic changes during the phase transition. Phase I is more disordered than other three phases, and the disordered events are likely result from the rotation of the anions [PF]6-. These insights will advance the understanding of OIPCs as next-generation solid-state refrigerants. Furthermore, once a reliable metric has been established to quantify such disorders, large scale parallel simulations will be conducted to generate data to train a machine learning model. Plastic crystal ; Phase transition ; Molecular dynamics ; Phase (matter) ; Thermal ; Ionic bonding ; Hexafluorophosphate ; Supercooling Shape Memory Alloy Transformations ; Dielectric materials and actuators ; Material Dynamics and Properties
Authors: Asmita Jana | Fabrice Roncoroni | Julie C. Fornaciari | Aditi S. Krishnapriyan | David Prendergast | Adam Weber | Ethan J. Crumlin | Jin Qian
Source: ChemRxiv (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

Benchmarking Machine-Learned Potentials for Water-Splitting Catalysts: Validation on Pt and IrO2 Surfaces Using OC20 and OMat24 Machine Learning Interatomic Potentials (MLIPs) enable first-principles accuracy in modeling complex catalytic interfaces at reduced cost. The Open Catalyst 2020 (OC20) and Open Materials 2024 (OMat24) datasets consist of density functional theory (DFT) data for adsorbates on metals and inorganic bulk materials, respectively. In this work, we benchmarked the Universal Model for Atoms (UMA) trained on the OC20 and OMat24 datasets with DFT calculations for adsorption energies on Pt and IrO2, respectively. We evaluated adsorption energies of common adsorbates such as *H, *O, *CO, and *NO on Pt, and reaction intermediates in the water splitting reaction, such as *O, *OH, and *H, on IrO2. UMA-OC20, trained using non-spin polarized DFT-RPBE energies with no van der Waals (vdW) corrections on metallic surfaces, reproduced the adsorption energies from the same flavor of DFT within 0.1 eV. However, the energy difference between UMA-OC20 and spin-polarized DFT was ∼1.2 eV, with the difference mainly arising from the reference gases. UMA-OMat24, trained using spin-polarized DFT-PBE with vdW corrections, predicted adsorption energies within 0.3 eV of the energies from the same flavor of DFT on IrO2. When both the DFT flavor and geometry were kept fixed between the MLIPs and DFT, UMA-OC20 and UMA-OMat24 predicted adsorption energies within 0.1 and 0.2 eV of their corresponding DFT adsorption energies, respectively. The norm of coordinate differences between UMA-OC20 and DFT was ∼0.1 Å for a system size of ∼20 atoms, while it was < 0.6 Å for a system size of ∼60 atoms, between UMA-OMat24 and DFT. MLIP requires considerably fewer (∼6000 times less) CPU-hours compared to DFT. Thus, we recommend opting for an MLIP-followed-by-DFT procedure, as it not only reduces the total compute time but also ensures accurate geometry and energies. Density functional theory ; Adsorption ; van der Waals force ; Catalysis ; Interatomic potential ; Metal Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Claudia Barile | Vimalathithan Paramsamy Kannan
Source:  (article, 2026-03-26)
Field: Materials Science — Polymers and Plastics

Evaluating the Recovery of Mechanical Properties of Self-Healing Composites Using Destructive and Nondestructive Testing The concept of self-healing polymers has been prevalent over the last few decades. However, their performance and behaviour in structural applications in the form of layered composites have not been studied extensively. In this study, an attempt has been made to evaluate the recovery of the mechanical properties of Carbon Fibre-Reinforced Polymer composites (CFRPs) with an intrinsically healable polymeric resin system. Destructive tests, including static tensile, compression, and flexural tests, are carried out to evaluate their ability to recover mechanical compliance after healing. Nondestructive tests based on the Acousto-Ultrasonic (AU) approach are carried out to establish and distinguish the state of these composites. The results show that the tested self-healing CFRPs can recover their mechanical properties, particularly their flexural and compressive properties, after unstable matrix damage. On the other hand, the AU approach, supported by Machine Learning (ML) models, demonstrates that the damaged states and the heal states of these composites can be distinguished from the virgin state. Nondestructive testing ; Flexural strength ; Polymer ; Matrix (chemical analysis) ; Compressive strength ; Materials testing Polymer composites and self-healing ; Smart Materials for Construction ; Composite Material Mechanics
Authors: Junbao Hu | Dingyu Hou | Jian Jiang
Source: CCS Chemistry (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Physical embedding machine learning force fields for organic systems Machine learning force fields possess unprecedented potential in achieving both accuracy and efficiency in molecular simulations. Nevertheless, their application in organic systems is often hindered by structural collapse during simulation and significant deviations in the prediction of macroscopic properties. Here, two physics-embedded strategies are introduced to overcome these limitations. First, a physics-inspired self-adaptive bond-length sampling method achieves long-timescale stable simulations by requiring only several tens of single-molecule data sets, and has been validated across molecular systems, including engineering fluids, polypeptides, and pharmaceuticals. Second, a top-down intermolecular correction strategy based on a physical equation is introduced. This strategy requires only a small amount of simulation data and completes the optimization of tunable parameters within a few hours on a single RTX 4090 GPU, significantly reducing errors in density and viscosity, as validated in systems including ethylene carbonate, ethyl acetate, and dimethyl carbonate. Together, these approaches directly integrate physical insights into the machine learning models, thereby enhancing robustness and generalizability, and providing a scalable pathway for physics-embedded machine learning force fields. Scalability ; Embedding ; Robustness (evolution) ; Physical system ; Intermolecular force ; Force field (fiction) ; Training set ; Uncertainty quantification Machine Learning in Materials Science
Authors: Aiqin Yang | Dian Jin | Mingkang Liu | Daye Zheng | Qi Wang | Qiangqiang Gu | Jian-Hua Jiang
Source: npj Computational Materials (article, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening Discovering nonlinear optical (NLO) materials with strong shift current response, particularly in the infrared (IR) regime, is essential for next-generation optoelectronics yet remains highly challenging in both experiments and theory, which still largely relies on case-by-case studies. Here, we employ a high-throughput screening strategy, applying a multi-step filter to the Materials Project database (>154,000 materials), which yielded 2519 candidate materials for detailed first-principles evaluation. From these calculations, we identify 32 NLO materials with strong shift current response (σ > 100 μA/V2). Our work reveals that layered structures with C3v symmetry and heavy p-block elements (e.g., Te and Sb) exhibit apparent superiority in enhancing shift current. More importantly, 9 of these compounds show shift current response peaks in the IR region, with the strongest reaching 616 μA/V2, holding significant application potential in fields such as IR photodetection, sensing, and energy harvesting. Beyond identifying promising candidates, this work establishes a comprehensive and high-quality first-principles dataset for NLO response, providing a solid foundation for future AI-driven screening and accelerated discovery of high-performance NLO materials, as demonstrated by a prototype machine-learning application. Current (fluid) ; Nonlinear optical ; Infrared ; Nonlinear system ; Work (physics) ; Filter (signal processing) ; Optical materials ; Foundation (evidence) Crystal Structures and Properties ; 2D Materials and Applications ; Photorefractive and Nonlinear Optics
Authors: Bora Jeong | Myeung-jin Lee | Ho Sung Jang | Sunmi Shin | T. R. Kim | HeeSoo Lee | Hong-Dae Kim
Source: Applied Nano (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Boosting NH3-Selective Catalytic Reduction of NOx by Cooperation of Nb and Boron Nitride to V-Based Catalyst over a Wide Temperature Window The commercialization of V-based catalysts for the selective catalytic reduction of NOx by NH3 (NH3-SCR) is hindered by their narrow operating temperature window, insufficient low-temperature (LT) activity, and severe SO2-to-SO3 oxidation. To bridge this gap, we herein introduced Nb and hexagonal BN into a VW/TiO2 system to simultaneously enhance its LT SCR activity, suppress undesired side reactions, and improve durability. Nb incorporation promoted V5+/V4+ redox cycling and enhanced lattice oxygen mobility, thus reducing the apparent activation energy and suppressing SO2 oxidation at elevated temperatures. However, excessive Nb loading induced NH3 oxidation and N2O formation. This drawback was mitigated by introducing BN as a dispersion promoter, which helped secure high catalytic performance at a reduced Nb content. The VWNb/Ti-BN catalyst achieved superior NOx conversion and N2 selectivity over a wide temperature range and benefited from notably suppressed NH3 oxidation and SO2-to-SO3 oxidation. Kinetic analysis revealed that Nb primarily lowered the reaction energy barrier via redox property enhancement, whereas BN accelerated surface reaction turnover by stabilizing and dispersing active acidic sites, markedly increasing the turnover frequency without reducing the activation energy. In situ spectroscopic analysis confirmed the accelerated consumption of adsorbed NH3 species and enhanced formation of reactive NOx intermediates, indicating SCR pathway enhancement. After aging in the presence of SO2 and H2O, the best-performing honeycomb-type monolithic catalyst retained and NOx conversion of &gt;80%, demonstrating excellent long-term durability under practical conditions. A composition-aware machine learning model based on log-ratio-transformed variables quantitatively identified the synergistic balance among V, Nb, W, BN, and TiO2 as the dominant factor governing LT SCR performance. Thus, this work provides valuable mechanistic insights and a strategy for designing wide-temperature-window SCR catalysts with improved activity, selectivity, and resistance to sulfur poisoning. NOx ; Catalysis ; Redox ; Selectivity ; Activation energy ; Adsorption ; Selective catalytic reduction Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Huibin Wang | Takahiro Mori | Ikuro Abe
Source: Current Opinion in Green and Sustainable Chemistry (article, 2026-02-14)
Field: Chemistry — Inorganic Chemistry

Harnessing Fe(II)/α-ketoglutarate-dependent oxygenases for sustainable synthesis Fe(II)/α-ketoglutarate-dependent oxygenases (αKG OXs) have emerged as versatile biocatalysts for selective C–H functionalization, offering sustainable routes to complex molecules under mild conditions. Recent advances in data-driven enzyme discovery, machine learning (ML)-guided enzyme engineering, and computational design have significantly improved their substrate scope, stability, and scalability for pharmaceutical synthesis. Beyond native oxidative modifications, repurposed αKG OXs mediate new-to-nature transformations, including site‐selective azidation, Mukaiyama hydration, Conia-ene cyclization, and photoenzymatic cross-coupling. Their integration into chemoenzymatic cascades has enabled concise and green syntheses of complex natural products and drug intermediates. This review highlights recent advances in studies that establish αKG OXs as key enzymes for sustainable biocatalytic synthesis. • αKG OXs enable selective C–H functionalization under mild conditions. • Data-driven and computational designs expand reaction diversity and enzyme stability. • Repurposed enzymes catalyze new-to-nature transformations. • Chemoenzymatic cascades streamline syntheses of natural products and drugs. Enzyme ; Substrate specificity ; Oxygenase ; Substrate (aquarium) ; Key (lock) ; Scalability Metal-Catalyzed Oxygenation Mechanisms ; Catalytic C–H Functionalization Methods ; Microbial Natural Products and Biosynthesis
Authors: Robert V. Stanton | Wanyi Nie | Sergei Tretiak | Dhara J. Trivedi
Source: npj Computational Materials (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind. Perovskite (structure) ; Realization (probability) ; Computation ; Identification (biology) ; Work (physics) ; Key (lock) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: F Alexander Sepúlveda | Daniel Cerro-Ramos | T Jesper Jacobsson
Source: Journal of Chemical Information and Modeling (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Density Estimation Based on Mixtures of Gaussians for Perovskite Solar Cells Modeling Accurately modeling the complex relationships among synthesis parameters, material compositions, and performance metrics is essential for accelerating the development of perovskite solar cells (PSCs). In this context, machine learning (ML) has proven to be a valuable tool. While most ML applications in PSC research rely on discriminative "black-box" models, this study adopts a generative approach by modeling the joint probability density function. We employ Gaussian Mixture Models (GMMs), a pragmatic and interpretable choice well-suited for the scarce, low-dimensional tabular data typical of PSC research. This single GMM framework is evaluated on five distinct tasks: discovering clusters, regression, generating novel configurations, training on data sets with missing data and, inverse design of the experimental (synthesis) conditions. That is, assuming we have the perovskite material composition and a target PCE, we infer the experimental conditions. For this latter task we use a novel "GMM-Assisted Optimization" method, which demonstrates to be more effective than standard random-start optimization, achieving an RMSE of 1.52 against target PCEs, more than halving the 3.32 RMSE of the baseline. These findings highlight the power of probabilistic modeling for data-driven discovery in PSC research. Mixture model ; Discriminative model ; Probabilistic logic ; Task (project management) ; Mean squared error ; Gaussian ; Perovskite (structure) ; Inverse ; Experimental data Machine Learning in Materials Science ; Perovskite Materials and Applications ; Gaussian Processes and Bayesian Inference