jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guoqing Wang | Hengyue Zhang | Hua Zuo | Hewen Wang | Bayu Admasu Beshiwork | Birkneh Sirak Teketel | Baihai Li | Bin Lin
Source: Renewable Energy (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Ensemble learning enables accurate and interpretable identification of hydrogen storage in 2D materials beyond MXenes MXenes ; Identification (biology) ; Hydrogen storage ; Ensemble learning ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Hydrogen ; Nanotechnology ; Chemistry ; Biology MXene and MAX Phase Materials ; Hydrogen Storage and Materials ; Advanced Photocatalysis Techniques
Authors: Qiangqiang Yuan | Haiqing Yin | Yongwei Wang | Ruixia Sun | Zheqi Qiao | Cong Zhang | Zengqiang Zhang | Dil Faraz Khan | Dong Li | Jingbin Liang | Xuanhui Qu
Source: steel research international (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Optimization of Eutectic Carbide Inhomogeneity in M42 High‐Speed Steel through Machine Learning and Finite‐Element Modeling The optimization of forging processes in M42 high‐speed steel is crucial for enhancing its microstructure and mechanical properties, particularly in reducing eutectic carbide inhomogeneity. In this study, machine learning (ML) is integrated with finite‐element modeling (FEM) to address the challenges of process parameter optimization in large‐sized ingots. The random forest algorithm is employed to predict the inhomogeneity of eutectic carbides using strain variables derived from FEM simulations as input features. The optimized process, validated through experimental analysis, demonstrates a significant improvement in carbide fragmentation, leading to a more uniform distribution of fine precipitates. The optimized M42 steel exhibits superior mechanical properties, with yield and compressive strengths increasing by ≈115 MPa and 305 MPa compared to the prior forging process. In the results, the efficacy of ML‐driven optimization is underscored in achieving a refined microstructure and enhanced material performance, offering a promising approach for industrial applications of high‐speed steel. Eutectic system ; Finite element method ; Carbide ; Materials science ; Mechanical engineering ; High-speed steel ; Metallurgy ; Composite material ; Structural engineering ; Engineering ; Microstructure Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advanced materials and composites
Authors: José A.S. Laranjeira | Yusuf Zuntu Abdullahi | Yee Hui Robin Chang
Source: Journal of Physics and Chemistry of Solids (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Stability, electronic and optical properties of group IV-carbide semiconductors: Graphenyldiene, and naphthylene-based monolayers Monolayer ; Semiconductor ; Materials science ; Group (periodic table) ; Carbide ; Optoelectronics ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry Graphene research and applications ; Molecular Junctions and Nanostructures ; Diamond and Carbon-based Materials Research
Authors: Xiaogang Wang | Shuo Yang | Tucker Carrington | Dong H. Zhang
Source: The Journal of Chemical Physics (article, 2025-10-10)
Field: Chemistry — Spectroscopy

A numerically exact calculation of vibration–rotation–tunneling levels of water dimer on a new accurate potential energy surface: Achieving sub-cm−1 accuracy from the terahertz to the infrared Numerically exact vibrational-rotational-tunneling (VRT) levels of (H2O)2 and (D2O)2 have been computed in full dimensionality on a new highly accurate two-body potential energy surface (PES). Inter-molecular levels are computed with a basis of products of contracted intra-molecular basis functions and inter-molecular functions that are products of Wigner functions. Intra-molecular levels are computed with a product of contracted intra-molecular basis functions and contracted inter-molecular basis functions. We use a two-body PES that is fitted using the fundamental-invariant neural network method using 740 000 ab initio points. Energies for points near the bottom of the well are computed without the frozen-core approximation. The PES has a root-mean-square fitting error of only 0.70 cm-1. All the experimental VRT fork origins and tunneling splittings in the terahertz region (up to 150 cm-1) are in excellent agreement with our calculated levels. The largest error is 0.44 cm-1 for (H2O)2 and 0.85 cm-1 for (D2O)2. The calculated levels also agree very well with the 22, of a possible 24, observed OD stretch vibration-tunneling levels of (D2O)2 [Barclay et al., J. Chem. Phys. 150, 164307 (2019) and Barclay et al., 160, 114314 (2024)], with the largest error being 0.35 cm-1. Coupling, which causes predissociation, makes OH stretch states of (H2O)2 difficult to observe and their assignment is controversial. Our calculations resolve the controversy. For the only rotationally resolved experimental OH stretch state, the as[A] "2s" state [Huang and Miller, J. Chem. Phys 91, 6613 (1989)], near 3738 cm-1, the 3 observed vibrational-tunneling levels agree with the calculated levels to within 0.35 cm-1. Basis (linear algebra) ; Curse of dimensionality ; Potential energy surface ; Basis function ; Quantum tunnelling ; Ab initio ; Terahertz radiation ; Potential energy ; Energy (signal processing) Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Atmospheric Ozone and Climate
Authors: Selene Muñoz-Vargas | Marcos Fernando Pérez-Pucheta | Karl S. Coleman
Source: Nanoscale Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Simple graphene film production by chemical vapour deposition and the use of AI for quality analysis The reproducible production of graphene films at low cost, while minimizing inter-batch variability, is essential for the development of affordable and reliable devices. By using a simple and reproducible methodology along with readily available low-cost equipment, graphene films can easily become accessible to non-graphene specialist research laboratories. This will help accelerate and encourage the development of graphene application research. Here, we describe in detail a simple hot-walled chemical vapour deposition (CVD) procedure to grow graphene with low-cost equipment, using non-processed commercially available copper foil. The quality of the film on copper foil and on Si/SiO<sub>2</sub> substrates, after film transfer, was studied by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to confirm the predominantly monolayer nature of graphene. The quality of graphene was analysed by comparing against standardisation Raman metrics, full width at half maximum of the 2D peak and the ratio of the 2D : G peaks, proposed by the UK National Physical Laboratory showing the films produced were similar or better quality than commercially available monolayer graphene. The random forest machine learning method was used as a predictive tool for the classification of the quality of samples according to the batch and position on the copper substrate relative to the direction of gas flow, revealing that the position and full width half maximum of the G and 2D peaks are the important features in Raman for classification, supporting the idea that unintentional strain and doping are crucial to understand the inherent variations found in the production of monolayer graphene in hot-wall CVD setups. Graphene ; Monolayer ; Raman spectroscopy ; Chemical vapor deposition ; Substrate (aquarium) ; Graphene nanoribbons ; Graphene oxide paper ; FOIL method Graphene research and applications
Authors: Harpriya Minhas | Rahul Kumar Sharma | Biswarup Pathak
Source: ACS Applied Materials & Interfaces (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Antibonding States Drive Anharmonicity and Low Thermal Conductivity in Edge-Sharing Metal Chalcogenides Stereochemically active lone pairs (SCALPs) and orbital hybridization in edge-sharing polyhedra play a crucial role in suppressing lattice thermal conductivity (κ<sub>L</sub>) in thermoelectric materials. Strong mixing between pnictogen <i>s</i>- and chalcogen <i>p</i>-orbitals generates active lone pairs, which enhance lattice anharmonicity and lead to ultralow κ<sub>L</sub>. In this study, we leverage machine learning interatomic potential to systematically probe bonding-driven mechanisms and their influence on thermal transport in noncentrosymmetric pnictogen chalcogens. We show that the combined effects of SCALPs, Pn-Pn bonding, and edge-sharing polyhedra enable the formation of antibonding states near the valence band maxima, intensifying phonon scattering. To quantify the underlying anharmonicity, we introduce a set of bonding descriptors─lone pair angle, lone pair distance, ionicity, and hybridization─that capture the influence of local structural motifs and antibonding features near the valence band edge. This bonding-centric framework not only elucidates the origins of ultralow κ<sub>L</sub> but also offers a rational design strategy for accelerating the discovery of high-performance thermoelectric materials. Antibonding molecular orbital ; Pnictogen ; Lone pair ; Anharmonicity ; Thermoelectric materials ; Chalcogen ; Valence (chemistry) Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; 2D Materials and Applications
Authors: Xiaojuan Zhang | Jiaqian Guo | Kexin Yang | Wei Yu | Chang Yan | Xiaotong Zou | Changqing Fang | Wenjuan Zhang
Source: Chemical Engineering Journal (article, 2025-07-15)
Field: Materials Science — Polymers and Plastics

Synergistic construction of supramolecular high-entropy poly(eutectic) conductive elastomers employing hydrogen bonding reinforced factor and coordinable Al(III) cations Eutectic system ; Elastomer ; Hydrogen bond ; Supramolecular chemistry ; Materials science ; Electrical conductor ; Polymer chemistry ; Chemical engineering ; Polymer science ; Chemistry ; Composite material ; Molecule ; Organic chemistry ; Microstructure Polymer composites and self-healing ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Can Zhou | Xueqian Yu | Yan Sun | Jie Han
Source: Measurement (article, 2025-10-21)
Field: Chemistry — Analytical Chemistry

DBHRNet: A lightweight dual-branch hybrid regression network for zinc concentrate component content estimation using hyperspectral images Hyperspectral imaging ; Dimensionality reduction ; Pattern recognition (psychology) ; Robustness (evolution) ; Pooling ; Leverage (statistics) ; Feature extraction ; Component (thermodynamics) ; Feature learning Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Tingting Jiang | Q. M. Zhang | Zhan Si | Jingjing Hu | Ying Lv | Tingting Wang | Haizhu Yu | Manzhou Zhu
Source: The Journal of Chemical Physics (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Machine learning on frontier orbital energy of atomically precise gold nanoclusters The highly promising application of atomically precise gold nanoclusters (Au NCs, protected by organic ligands such as thiolate, phosphate, etc.) in electrochemical and photochemical catalysis has highlighted the importance of accurately predicting their electronic states, which has been challenging because of the high experimental and computational cost. In this study, a machine learning model based on interpretable automated feature engineering was developed. Starting with the 99 data points of Au NCs, 227 candidate parameters were first screened by a bi-directional stepwise regression, associated with the Kolmogorov-Arnold network model, to improve the prediction performance. With this suite of codes and using only 79 data points as the training set and 4 key descriptors, the mean average error (MAE) of the HOMO, LUMO, and HOMO-LUMO gap of the testing set (20 data points) reaches 0.17, 0.27, and 0.16 eV, respectively. The model could also be used to generalize the oxidation potential (OP) of the target clusters, with an MAE of 0.20 V. In particular, four dominant physicochemical parameters for HOMO/LUMO/HOMO-LUMO gap/OP were identified. The number of cluster charges (NC) and average Au-Au coordination numbers (CNAu-Au) are consistently present in the four filtered features, indicating that these parameters are critical for determining the electronic structure of Au NCs. Overall, the present study demonstrates that a small set of key structural descriptors could enable a cost-efficient strategy to accurately predict the electronic structure of Au NCs. Nanoclusters ; Data point ; Set (abstract data type) ; Electronic structure ; Energy (signal processing) ; Data set ; Key (lock) Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Mohammad Reza Najafi | M. A. Aghajani | Naser Safaie | Siamak Rahmanpour
Source: PLoS ONE (article, 2025-10-07)
Field: Chemistry — Analytical Chemistry

Multilayer perceptron neural network-genetic algorithm for modeling Nicotiana tabacum leaf quality The global industry of tobacco (Nicotiana tabacum L.) is a profitable one comprising various products, including cigars, cigarettes, chewing tobacco, and smokeless tobacco. The internal quality of the cigarettes is highly related to the chemical components of tobacco leaves and shreds. Blue mold severity (BMS), chlorophyll (Chl), total nitrogen (N), sugar (S), nicotine (Nt), chloride (Cl), and potassium (K) contents of tobacco leaves are linked to the flavor and taste of cigarette products. A precise analysis of the effects of these factors would open the door for improving farmer income in low- and middle-income countries. In this study, BMS, Chl, N, S, Nt, Cl, K, green weight (GW), dry weight (DW), and leaf quality of four cultivars, including Bergerac, Bell, Burly, and Basma, were evaluated during two growing seasons. Bell displayed the highest leaf quality in two growing seasons. Multiple linear regression, stepwise regression, ordinary least squares regression, partial least squares regression, principal component regression, and multilayer perceptron neural network-genetic algorithm (MLPNN-GA) were used for the prediction of tobacco leaf quality responding to BMS, Chl, N, S, Nt, Cl, K. MLPNN-GA models displayed higher prediction accuracy compared with the best regression model according to R2 for MLPNN-GA vs. regression models were: Bergerac; 1.00 vs. 0.82, Bell = 1.00 vs. 0.41, Burly = 1.00 vs. 0.68, Basma = 0.94 vs. 0.68, and all cultivars = 0.94 vs. 0.66. The close match between the predicted and actual data validated the superior efficiency of the developed MLNNP-GA models for predicting tobacco leaf quality responding to BMS, Chl, N, S, Nt, Cl, K. Analysis of the developed MLPNN-GA models showed that Bergerac, Bell, Burly, and Basma leaf quality was most sensitive to BMS. MLPNN-GA was demonstrated to be a practical mathematical tool for predicting tobacco leaf quality in response to its chemical components and BMS. Nicotiana tabacum ; Tobacco leaf ; Principal component analysis ; Sugar ; Partial least squares regression ; Regression analysis ; Multilayer perceptron ; Corm Spectroscopy and Chemometric Analyses ; Greenhouse Technology and Climate Control ; Leaf Properties and Growth Measurement
Authors: Akash Talapatra | Bahareh Nojabaei | Pooya Khodaparast
Source: Scientific Reports (article, 2025-04-18)
Field: Chemistry — Organic Chemistry

A data driven predictive viscosity model for the microemulsion phase The changes in phase viscosity at the oil-brine interface due to surfactant addition are critical under practical reservoir conditions. This study develops a computational, data-driven model to accurately estimate and predict peak phase viscosity in microemulsion systems at dynamic environments. Using equilibrium molecular dynamics (MD) simulations, we investigate a decane-sodium dodecyl sulfate (SDS)-brine system, generating viscosity data as of temperatures, pressures, surfactant concentrations, and salinities. The data, computed via the Einstein relation and Green-Kubo formula, provides robust training and test datasets for model development. Various machine learning (ML) based regression algorithms are employed on our dataset to train and fit the model. This study aims to compare the accuracy and correlation coefficients of these models, selecting the most precise model for predicting microemulsion phase viscosity under diverse reservoir conditions. Support Vector Regression (SVR) outperformed other models with an R<sup>2</sup> of 0.978 and 0.963 and mean absolute errors of 0.059 and 0.072 for training and test datasets, respectively. Unlike traditional empirical viscosity correlations, this model incorporates physics-based relationships, enhancing its adaptability to varying reservoir conditions. The proposed model accurately predicts microemulsion phase viscosity, including peak viscosity locations, across pressures, temperatures, salinities, and surfactant concentration. This work facilitates precise viscosity estimation, improving recovery efficiency under reservoir conditions. Microemulsion ; Viscosity ; Phase (matter) ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Pulmonary surfactant Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques ; Advanced Polymer Synthesis and Characterization
Authors: Jiahang Li | Yajun Zhang | Jie Wang | Huadong Yong
Source: Materials & Design (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Suppression of irradiation defects and crack propagation in niobium via grain boundary engineering: A deep potential molecular dynamics study • Developed a high accuracy machine learning potential for predicting Niobium’s properties. • Demonstrated grain boundary engineering and pre-strain improves irradiation resistance, strength, and fracture toughness. • Discovered specific grain boundaries enhance both strength and toughness, mitigating fracture-related damage. • A robust foundation for the design of high radiation-resistant and fracture-tough polycrystalline niobium. Superconductor niobium has attracted remarkable interest in recent decades due to their superior superconducting and mechanical properties, as well as practical applications in superconducting devices. However, it was inevitably exposed to irradiation fields and micro-crack propagation in extreme operating environments. Therefore, developing precise computational method for precise description of irradiation and micro-crack induced defects and exploring efficient strategy to suppress the accompanied damage become extremely important for the reliable and stable device performance. Here, we developed a multi-scale framework based on first-principles simulations, machine learning potential, and molecular dynamics simulations. The model provides simultaneously accurate description of mechanical parameters, point defect formation energy, grain boundary (GB) energy, and stacking faults energy. Based on this model, we screened out several metastable GBs that should be preferred in practice. Going further, the effect of GB on irradiation damage and micro-crack propagation are systematically investigated. By the proper selection of suitable GB, it is possible to effectively improve the irradiation resistance, strength, and fracture toughness, which are critical for suppressing irradiation damage and micro-crack propagation. These findings extend significantly the current understanding of GB engineering in niobium and provide a solid foundation for the design of high radiation-resistant and fracture toughness polycrystalline niobium. Materials science ; Niobium ; Grain boundary ; Molecular dynamics ; Irradiation ; Composite material ; Metallurgy ; Condensed matter physics ; Engineering physics ; Forensic engineering ; Microstructure ; Nuclear physics ; Computational chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; Microstructure and mechanical properties
Authors: Mustafa Çağrı Bayır | Ebru Mensur
Source: Materials (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

XGBoost-Based Modeling of Electrocaloric Property: A Bayesian Optimization in BCZT Electroceramics Electrocaloric materials, which exhibit adiabatic temperature change under an applied electric field, are promising for solid-state cooling technologies. In this study, the electrocaloric response of lead-free Ba<sub>x</sub>Ca<sub>1-x</sub>Zr<sub>y</sub>Ti<sub>1-y</sub>O<sub>3</sub> (BCZT) ceramics was modeled to investigate the effects of composition, processing, and measurement conditions on performance. A high-accuracy XGBoost regression model (R<sup>2</sup> = 0.99, MAE = 0.02 °C) was developed using a dataset of 2188 literature-derived data points to predict and design the electrocaloric response of BCZT ceramics. The feature space incorporated compositional ratios, processing parameters, measurement settings, and atomic-level Magpie descriptors, along with Curie temperature to account for phase-transition behavior. Feature importance analysis revealed that electric field, measurement temperature, and proximity to the Curie point are the most critical factors influencing ΔT<sub>EC</sub>. Bayesian optimization was applied to navigate the design space and identify performance maxima under unconstrained and realistic constraints, offering valuable insights into the nonlinear interactions governing electrocaloric performance. Under room temperature and moderate-field conditions (24 °C, 40 kV/cm), the optimized ΔT<sub>EC</sub> achieved a value of 1.03 °C for Ba<sub>0.85</sub>Ca<sub>0.15</sub>Zr<sub>0.40</sub>Ti<sub>0.60</sub>, to be processed at 1090 °C for 3 h during calcination, 1300 °C for 2 h during sintering. By integrating experimental insight with machine learning and optimization, this study offers a refined, interpretable framework for accelerating the design of high-performance electrocaloric ceramics while reducing the experimental workload. Electrocaloric effect ; Materials science ; Electric field ; Ceramic ; Curie temperature ; Field (mathematics) ; Adiabatic process ; Dielectric ; Computer science ; Thermodynamics ; Condensed matter physics ; Ferroelectricity ; Mathematics ; Physics ; Composite material ; Optoelectronics ; Ferromagnetism Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Dielectric materials and actuators