jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kubra Sattar | Ivan S. Babichuk | Saleem Ayaz Khan | Jian Yang | Syed Rizwan
Source: npj 2D Materials and Applications (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

MXene-MoS2 engineered heterostructured vertical memristors array: high-performance non-volatile memory with scalable integration Abstract The emergence of artificial intelligence and neuromorphic computing has revealed the importance of two-dimensional (2D) layered materials in the field of non-volatile memory technologies. Based on the available literature, this is the very first report demonstrating the integration of 2D Transition Metal Carbide MXenes, i.e. Titanium Carbide (Ti 3 C 2 T x ) and Vanadium Carbide (V 2 CT x ) with MoS 2 from the Transition Metal Dichalcogenide family for memristor devices. The nanoscale Ti 3 C 2 T x and V 2 CT x MXene were employed as the bottom electrodes (as independent as well as bi-electrode combination) deposited uniformly on the substrate, along with the successful transfer of Chemical Vapor Deposition grown few-layers MoS 2 onto the nano-ranged sheets of MXene with Ag utilized as the top electrode. Raman analysis and Optical microscopy images indicated the fabrication of the novel best-yielding device Ti 3 C 2 /V 2 C/MoS 2 /Ag structure with high fidelity. Additionally, current-voltage (I-V) analysis showcased bipolar resistive switching phenomena comprising of an efficiently low SET voltage of 0.6 V, endurance of up-to 3000 cycles and retention of over 10 3 seconds, extrapolated over million seconds. The stability of SET/RESET voltages, cycle-to-cycle and device-to-device reproducibility is demonstrated by 18 devices for a memristor array of (5 ×5), emphasizing the unexplored potential of MXene and MoS 2 based memristors for the scalable and large-area integration of devices in future memristors. Furthermore, Ti 3 C 2 /V 2 C/MoS 2 /Ag memristor also exhibited synaptic features of potentiation and depression showcasing potential to be employed in neuromorphic computing techniques. Memristor ; Neuromorphic engineering ; Resistive random-access memory ; Fabrication ; Scalability ; Chemical vapor deposition ; Electrode ; Raman spectroscopy ; Nanoscopic scale MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Yaqi Zhu | Zhigong Song
Source: Acta Physica Sinica (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dependence of Interfacial Bonding Strength on Crystallographic Matching in &lt;i&gt;β&lt;/i&gt;-Ga&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;/Diamond Heterogeneous Bonding <i>β</i>-Ga<sub>2</sub>O<sub>3</sub>作为第四代半导体材料的代表，在功率器件中展现出巨大的应用潜力，然而，其极低的本征热导率限制了其在高功率密度器件中的进一步应用。近年来，通过将<i>β</i>-Ga<sub>2</sub>O<sub>3</sub>与高热导率材料（如金刚石）进行异质键合以提升散热性能，已成为一种重要的技术方向。然而，其界面微观演变及界面键合强度对晶向的依赖关系尚不明确，制约了键合工艺的优化与器件可靠性的提升。为此，本研究通过构建具备第一性原理精度的百万原子级数据集并拟合MTP势函数，系统地研究了界面键合强度的晶向依赖性。结果表明，所有弛豫后的键合界面均实现了原子级致密接触，且无明显缺陷。其中，<i>β</i>-Ga<sub>2</sub>O<sub>3</sub>(010)/金刚石(100)组合表现出最优的力学性能。本研究从原子尺度揭示了界面键合强度的晶向匹配依赖性，对于优化晶圆键合工艺、提升高功率器件的机械稳定性与长期可靠性具有重要的理论指导意义。 Bonding strength ; Matching (statistics) ; Chemical bond ; Bond strength ; Atomic force microscopy Ga2O3 and related materials ; High Entropy Alloys Studies ; Metal and Thin Film Mechanics
Authors: Chuansheng Liang | Peng Cheng | Chenxi Shao
Source: Metals (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for Predicting Carburizing Process Outcomes in 20Cr2Ni4 Steel: A Cascade Modeling Approach Carburizing process optimization requires accurate prediction of multiple interrelated outcomes, yet existing models either oversimplify the physics or require prohibitively large datasets. Here, we present a physics-informed machine learning (PIML) cascade model for vacuum carburizing of 20Cr2Ni4 gear steel that predicts surface carbon content, maximum hardness, and effective case depth through a three-stage sequential architecture. The model integrates Fick’s diffusion law and empirical carbon–hardness relationships with ensemble learning using physics-derived features to reduce data requirements while maintaining interpretability. Validation against experimental data yields coefficient of determination values of 0.968 (surface carbon, RMSE = 0.0023 wt%), 0.963 (maximum hardness, RMSE = 1.27 HV), and 0.999 (case depth, RMSE = 0.0053 mm) on physics-augmented test data; leave-one-out cross-validation (LOOCV) on original experimental data yields R2 = 0.87–0.95, representing true generalization capability. Feature importance analysis reveals that physics-derived features collectively account for over 70% of the prediction power, with the characteristic diffusion length (Dt) contributing 42.2%, followed by temperature-related features (22.4%) and time-related features (14.8%). Compared to pure physics-based and data-driven approaches, the proposed framework achieves superior accuracy for case depth prediction while preserving physical consistency. The methodology demonstrates potential for adaptation to other vacuum-carburizing applications with similar Cr-Ni steel compositions, although extension to fundamentally different processes (e.g., gas carburizing and nitriding) would require process-specific recalibration. Carburizing ; Cascade ; Process (computing) ; Generalization ; Mean squared error ; Feature (linguistics) ; Artificial neural network ; Test data Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Model Reduction and Neural Networks
Authors: Rui Mei | Xiao‐Ping Yao | Zhong‐Jie Wang | Jia‐Liang Xie | Wu Li | Miao‐Ling Lin | Ping‐Heng Tan
Source: Advanced Functional Materials (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Non‐Anharmonic Phonon Broadening in Real Crystals ABSTRACT The linewidth of the Raman mode can directly provide insight into phonon lifetime, a critical parameter governing the thermal, electrical, and optical properties of materials. Real crystals inherently deviate from ideal lattices due to isotopes, impurities, and defects, yet the explicit impact of these imperfections on non‐anharmonic phonon broadening remains elusive. Herein, temperature‐dependent Raman spectroscopy combined with first‐principles calculations unveils a significant temperature‐independent non‐anharmonic phonon broadening () in near‐ideal hBN‐encapsulated monolayer , primarily originating from isotopic mass disorder. Systematic studies on samples with progressively introduced defects further demonstrate that surface ripples, adsorbed impurities, and vacancies substantially contribute to in intralayer phonons. In contrast, interlayer shear modes in multilayer exhibit negligible sensitivity to uniform intralayer disorder, but are strongly affected by inhomogeneous interlayer imperfections. These findings establish a direct link between crystalline defects and non‐anharmonic phonon broadening, providing critical insights for phonon lifetime engineering and thermal management in functional semiconductor devices. Phonon ; Raman spectroscopy ; Laser linewidth ; Monolayer ; Semiconductor ; Raman scattering ; Thermal Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Mohamed Bechir Ben Hamida
Source: Preprints.org (preprint, 2026-01-26)
Field: Chemistry — Inorganic Chemistry

Modeling and Optimizing of Cumene Synthesis Using Zeolite-Catalyzed Alkylation This study focused on simulating and optimizing the production of cumene (iso-propylbenzene) through the alkylation of benzene with propylene using a Beta Zeolite catalyst. Two process configurations were evaluated: one without a transalkylation reactor and another incorporating a transalkylation unit to convert byproducts back into cumene. The process was modeled under steady-state conditions in Aspen HYSYS using plug flow reactors and the Peng-Robinson fluid package, with reaction kinetics derived from literature on zeolite-catalyzed systems. Optimization studies examined the effects of reactor temperature, pressure, and benzene-to-propylene molar ratio. In-creasing the reactor temperature to 178°C improved propylene conversion to 96.20%, while raising the pressure from 3540 kPa to 3600 kPa further enhanced conversion to 96.24%. Cumene production was enhanced to 135.792 kmol/h while minimising by-product formation by optimising the benzene-to-propylene molar feed ratio to about 0.75:1. The original fresh benzene feed flow rate was 127.7 kmol/h, which was reduced to 101 kmol/h. According to a comparison analysis, the setup without a transalkylation reactor generated 4.171 kmol/h of diisopropylbenzene (DIPB) as waste, which posed a risk to the environment because of its toxicity as well as financial losses. On the other hand, DIPB conversion into more cumene was made possible by the transalkylation reactor, which increased process sustainability and efficiency. These results show that adding a transalkylation step and optimising reaction parameters including tempera-ture, pressure, and input ratios greatly increases cumene yield while lowering waste production, resulting in a more practical and sustainable process. Transalkylation ; Cumene ; Alkylation ; Batch reactor ; Yield (engineering) ; Benzene ; Catalysis ; Isothermal process Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Mesoporous Materials and Catalysis
Authors: Cesar Cesar Pérez-Alonso | J. Alvarez-Ramírez | Reyna Natividad | Ever Peralta-Reyes | Alejandro Regalado-Méndez
Source: Research Square (preprint, 2026-01-09)
Field: Chemistry — Organic Chemistry

Prediction of Thermodynamic Properties of Sacred Pepper (Piper auritum) Using Machine Learning Support vector machine ; Artificial neural network ; Random forest ; Entropy (arrow of time) ; Predictive modelling ; Regression ; Decision tree ; Nonlinear system ; Extreme learning machine Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Machine Learning in Materials Science
Authors: Ashraf M. El-Shamy
Source: Arabian Journal for Science and Engineering (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Decoding for Commercial Corrosion Inhibitor in Oil and Gas Production: Unveiling the Multifaceted Corrosion Dynamics and Protective Strategies Abstract This study presents a comprehensive electrochemical evaluation of a commercial corrosion inhibitor for carbon steel in a simulated oilfield brine (3.5% NaCl) under acidic conditions (pH 3.4). The performance and mechanism were investigated using open circuit potential monitoring, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS). The inhibitor demonstrated a concentration-dependent efficacy, with 100 ppm identified as the optimal dosage. At this concentration, a high inhibition efficiency of 93.8% (from polarization) and a significant increase in charge-transfer resistance (from 3.1 to 20.2 kΩ·cm 2 ) were achieved. Analysis of the electrochemical parameters revealed a mixed-type inhibition mechanism with predominant anodic control, attributed to the strong adsorption of the inhibitor molecules onto the steel surface. This adsorption, facilitated by functional groups such as –NH₂ and –C=O, forms a protective film that increases surface homogeneity and effectively blocks active corrosion sites. While the study establishes a robust performance baseline and mechanistic understanding under controlled laboratory conditions, it also highlights the necessity for future validation under dynamic flow and long-term exposure scenarios to fully assess field applicability. The findings provide critical data for the informed selection and application of inhibitors in corrosive, chloride-containing environments. Corrosion ; Dielectric spectroscopy ; Corrosion inhibitor ; Electrochemistry ; Carbon steel ; Adsorption ; Brine Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Philipp Stärk | Henrik Stooß | Marcel F. Langer | Egor Rumiantsev | Alexander Schlaich | Michele Ceriotti | Philip R. Loche
Source: ArXiv.org (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Simultaneous Learning of Static and Dynamic Charges Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems. Coupling (piping) ; Coulomb ; Dielectric ; AKA ; Charge (physics) ; Isotropy ; Cluster (spacecraft) ; Connection (principal bundle) ; Electric potential Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Xinyu Feng | Xijing Zhu | Yao Liu | Chao Yan
Source: Research Square (preprint, 2026-03-12)
Field: Materials Science — Ceramics and Composites

Fatigue life prediction of ceramic matrix composite materials based on physical constraint multi-scale attention network Service life ; Constraint (computer-aided design) ; Stress (linguistics) ; Ceramic matrix composite ; Mean squared error ; Composite number Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Smart Materials for Construction
Authors: Yaowei Wei | Jianchong Li | Wenze Ma | Hongqin Lei | Fei Zhang | Zhenfei Luo | Henan Liu | Xianghui Huang | Linjie Zhao | Mingjun Chen
Source: Micromachines (article, 2026-01-27)
Field: Materials Science — Surfaces, Coatings and Films

Sensitivity Analysis of Process Parameters on Deposition Quality and Multi-Objective Prediction in Ion-Assisted Electron Beam Evaporation of Ta2O5 Films Tantalum pentoxide (Ta<sub>2</sub>O<sub>5</sub>) films deposited on fused silica substrates are critical components of high-power laser systems. Ion-assisted electron beam evaporation (IAD-EBE) is the mainstream technique for fabricating Ta<sub>2</sub>O<sub>5</sub> films. However, it commonly requires extensive experimental efforts for deposition quality optimization, while each coating cycle is extremely time-consuming. To solve this issue, this work establishes a dataset targeting the surface roughness (<i>Rq</i>) and refractive index (<i>n</i>) of Ta<sub>2</sub>O<sub>5</sub> films using atomic force microscopy, as well as ellipsometer and deposition experiments. Influence of assisting ion source beam voltage (<i>V</i>)/current (<i>I</i>) and Ar (<i>Q</i><sub>1</sub>)/O<sub>2</sub> (<i>Q</i><sub>2</sub>) flow rate on the <i>n</i> and <i>Rq</i> of Ta<sub>2</sub>O<sub>5</sub> films are analyzed. Combining energy-field mechanism analysis with a Bayesian optimization approach (PI-BO), both deposition quality prediction and feature analysis of process parameters are achieved. The determination coefficient/mean absolute error for the prediction models of <i>n</i> and <i>Rq</i> reach 0.927/0.013 nm and 0.821/0.049 nm, respectively. Based on sensitivity analysis, the weight factors of <i>V</i>, <i>I</i>, <i>Q</i><sub>1</sub>, and <i>Q</i><sub>2</sub> affecting <i>n</i>/<i>Rq</i> of Ta<sub>2</sub>O<sub>5</sub> films are determined to be 0.616/0.274, 0.199/0.144, 0.113/0.582, and 0.072/0.000. <i>V</i> and <i>Q</i><sub>2</sub> are identified as the core factors for regulating deposition quality. The optimal ranges for <i>V</i> and <i>Q</i><sub>2</sub> are 600~700 V and 70~80 sccm, respectively. This study proposes a PI-BO method for predicting <i>Rq</i> and <i>n</i> of Ta<sub>2</sub>O<sub>5</sub> films under small-data conditions, while determining the preferred parameter ranges and their sensitivity weight factors. These findings provide effective theoretical support and technical guidance for IAD-EBE strategy design and optimization of optical films in high-power laser systems. Deposition (geology) ; Electron beam physical vapor deposition ; Evaporation ; Surface roughness ; Sensitivity (control systems) ; Tantalum pentoxide ; Coating ; Thin film ; Pulsed laser deposition ; Surface finish Optical Coatings and Gratings ; Electron and X-Ray Spectroscopy Techniques ; Laser Material Processing Techniques
Authors: Tomeshwar Prasad | Jagdeo Singh | Milan Hait
Source: Discover Sensors (article, 2026-03-12)
Field: Chemistry — Spectroscopy

Recent advances in luminescent chemosensors for sensitive and selective detection of heavy metal ions in aqueous environments The detection of heavy metal ions in aqueous environments is of critical importance due to their pronounced toxicity and environmental persistence. Luminescent chemosensors have emerged as powerful analytical tools owing to their high sensitivity, rapid response, and potential for real-time monitoring. This review comprehensively examines the recent advances in luminescent chemosensor design and application for heavy metal detection, emphasizing mechanistic pathways, molecular design strategies, and photophysical principles. Representative fluorophore classes, nanomaterial- and polymer-assisted approaches, and molecular recognition motifs are analyzed for their selectivity and detection efficiency. Comparative assessments of detection limits, selectivity performance, and analytical validation in real water samples highlight the practical capabilities and limitations of current luminescent chemosensors. Challenges in field deployment, such as interference from complex matrices, stability, and reproducibility, are discussed alongside emerging methodologies and hybrid strategies aimed at overcoming these obstacles. The review also contextualizes luminescent chemosensing within the broader landscape of conventional and advanced detection technologies. Collectively, this analysis underscores the promise of luminescent chemosensors as versatile, cost-effective, and environmentally friendly platforms for heavy metal monitoring, while outlining future directions for enhancing their sensitivity, selectivity, and applicability in real-world aqueous systems. Luminescence ; Metal ions in aqueous solution ; Fluorophore ; Aqueous solution ; Aqueous medium ; Luminescent Measurements ; Fluorescence Molecular Sensors and Ion Detection ; Carbon and Quantum Dots Applications ; Analytical chemistry methods development
Authors: Huaxi Wu | Xinkai Guo | Wen Sun | Lu-Kai Song | Qingyang Deng | Shiyuan Yang | Debiao Meng
Source: Applied Sciences (article, 2026-02-13)
Field: Materials Science — Metals and Alloys

Fatigue Life Prediction of Steels in Hydrogen Environments Using Physics-Informed Learning Hydrogen embrittlement poses a critical threat to the durability of metallic components in emerging hydrogen energy infrastructure. Reliable fatigue life assessment in hydrogen-rich environments is, however, severely constrained by the high cost and low throughput of high-pressure testing, resulting in characteristically sparse experimental datasets. Conventional empirical fatigue models struggle to capture hydrogen–mechanical coupling effects, while purely data-driven approaches often suffer from severe overfitting under data-scarce conditions. To address this challenge, this study develops a physics-enhanced learning framework that integrates established fracture mechanics principles with machine learning. Using high-strength GS80A steel as a case study, two complementary strategies are introduced. First, a physically augmented input strategy reformulates raw experimental variables into dimensionless physical descriptors derived from the Basquin and Goodman relations, thereby reducing the complexity of the learning space. Second, a physics-regularized ensemble strategy combines deterministic physical predictions with neural network outputs through a dual-pathway inference scheme, ensuring physically admissible behavior during extrapolation. An automated hyperparameter selection module is further employed to establish a robust data-driven baseline. Comparative evaluation against optimized multi-layer perceptron and support vector regression models demonstrates that the proposed framework significantly improves predictive robustness in small-sample regimes. Specifically, the coefficient of determination (R2) exceeds 0.975, with the root mean square error (RMSE) reduced by approximately 70% compared to the pure data-driven baseline. By systematically embedding mechanistic priors into the learning process, the proposed approach provides a reliable and interpretable tool for fatigue assessment of metallic components operating in hydrogen environments. Overfitting ; Robustness (evolution) ; Artificial neural network ; Perceptron ; Hyperparameter ; Feature selection ; Support vector machine ; Embedding Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; High Temperature Alloys and Creep
Authors: Seong‐Hoon Jang | Shin Kiyohara | Hitoshi Takamura | Yu Kumagai
Source: Scientific Data (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models Oxygen ion conductors are indispensable materials for such as solid oxide fuel cells, sensors, and membranes. Despite extensive research across diverse structural families, systematic data enabling comparative analysis remains scarce. Here, we present a curated dataset of oxygen ion conductors compiled from 84 experimental reports spanning 60 years, covering 483 materials. Each record includes activation energy (E<sub>a</sub>) and prefactor (A) derived from Arrhenius plots, alongside detailed metadata on structure, composition, measurement method, and data source. When the original papers derive these using an erroneous Arrhenius equation, we replotted these using the correct one. To illustrate how the database can be used, we constructed interpretable regression models for predicting oxygen ionic conductivity. Two symbolic regression models for E<sub>a</sub> and A suggest that oxygen ion transport is primarily governed by local coordination environment and the electrostatic interactions, respectively. This dataset establishes a reliable foundation for data-driven discovery and predictive modeling of next-generation oxygen ion conductors. Arrhenius equation ; Metadata ; Oxygen ; Regression analysis ; Regression ; Ionic bonding Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Advanced Sensor and Energy Harvesting Materials