jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rui Li | Zijun Qi | Zhanpeng Sun | Biao Meng | Wei Shen | Zhaofu Zhang | Gai Wu
Source: Materials Science in Semiconductor Processing (article, 2025-03-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Defect evolution in gallium oxide during stretching process: A molecular dynamics simulation Materials science ; Molecular dynamics ; Gallium ; Oxide ; Gallium oxide ; Process (computing) ; Chemical physics ; Nanotechnology ; Computational chemistry ; Metallurgy ; Computer science ; Chemistry Ga2O3 and related materials ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Haoming Zhang | Mo Cheng | Xuanyu Jiang | Hui Zhang | Xiaodong Pi | Deren Yang | Tianqi Deng
Source: Journal of Materials Informatics (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Neuroevolution potential for thermal transport in silicon carbide Silicon carbide (SiC) is a representative high-thermal-conductivity semiconductor for applications in power electronics and quantum devices. In these applications, thermal management becomes critical for stable functioning. Meanwhile, SiC crystallizes into hundreds of polytypes with various stacking orders and small energy difference. Such a characteristic also leads to the formation of stacking faults. To understand the thermal transport property of SiC in the presence of polytypism and crystallographic defects, we have developed a neuroevolution potential for SiC. The model is trained on a diverse dataset with stoichiometric and off-stoichiometric Si&lt;sub&gt;x&lt;/sub&gt;C&lt;sub&gt;y&lt;/sub&gt; configurations. The dataset is strategically sampled using graph network encoding and clustering in the feature space. The potential achieves high accuracy with energy error of 4.1 meV/atom and force error of 0.22 eV/Å in the test set. And the dataset diversity is found to be critical to the model robustness. The application of this model is demonstrated by simulating the thermal transport property of SiC polytypes and stacking faults using homogeneous nonequilibrium molecular dynamics. The thermal resistance of stacking faults can reach substantial values up to 10&lt;sup&gt;-10&lt;/sup&gt; K·m&lt;sup&gt;2&lt;/sup&gt;·W&lt;sup&gt;-1&lt;/sup&gt;, which may create hot spots and impede heat dissipation in devices. The thermal resistance varies considerably among different fault types, with those exhibiting consecutive cubic k stacking configurations demonstrating remarkably lower resistance. This work provides an accurate and efficient machine-learning interatomic potential for simulations of thermal and phonon properties of SiC and understanding of its thermal management in the presence of polytypism and crystallographic defects. Silicon carbide ; Neuroevolution ; Thermal ; Silicon ; Silicon valley ; Nanotechnology ; Computer science ; Materials science ; Business ; Artificial intelligence ; Optoelectronics ; Metallurgy ; Physics ; Artificial neural network ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Xiaoshuang Wang | Yuhang Hu | Chun Kit Ang | Mahmud Iwan Solihin | Jun Jiat Tiang | Wei Hong Lim
Source: Applied Food Research (article, 2025-09-25)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging-based deep learning benchmarks in non-destructive testing of cherry tomatoes • Hyperspectral imaging enables non-destructive prediction of cherry tomato quality. • Deep learning and ML models were compared under four spectral preprocessing methods. • Bayesian optimization was used for automated DL architecture and preprocessing tuning. • Grad-CAM identified key wavelengths linked to chemical traits, enhancing model interpretability. Non-destructive testing of fruit quality is critical for enhancing post-harvest processing efficiency and market value, particularly for delicate produce such as cherry tomatoes. Traditional methods, which are labor-intensive and destructive, limit real-time applicability. Hyperspectral imaging (HSI), by capturing spatial and spectral information, offers a promising alternative for rapid, non-invasive quality evaluation. This study evaluates the potential of combining HSI with traditional machine learning and deep learning (DL) models to predict four key physicochemical properties of cherry tomatoes: soluble solids content, acidity, sugar content, and firmness. A dataset of 310 samples was analyzed across two spectral ranges: visible/near-infrared (406-1010 nm) and near-infrared (957-1677 nm). Four preprocessing methods were applied to enhance spectral features: multiplicative scatter correction, standard normal variate, first derivative, and second derivative. To establish deep learning benchmarks, Bayesian optimization hyperparameter search, as neural architecture search, was employed for spectral preprocessing and DL network architecture tuning. For fair comparison, traditional ML models with preprocessing methods were optimized using grid search. Seven models, including linear regression (LR), partial least squares regression (PLSR), support vector regression (SVR), VGG, ResNet, Transformer, and DenseNet, were developed. Results indicated that DL models, particularly ResNet and Transformer, exhibit superior accuracy and robustness, achieving coefficient of determination (R 2 ) values up to 0.96 in the test sets. Spectral analysis based on Grad-CAM confirms that these DL models consistently focus on chemically informative wavelengths. These findings highlight the advantages of optimized DL models in developing interpretable and accurate HSI for non-destructive fruit quality testing, supporting advancements in agricultural productivity, enabling manufacturing innovation in post-harvest processing, and promoting sustainable food consumption through reduced waste and improved quality control. Hyperspectral imaging ; Preprocessor ; Deep learning ; Hyperparameter ; Partial least squares regression ; Artificial neural network ; Pattern recognition (psychology) ; Regression Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: María Judit Montes de Oca‐Estévez | Álvaro Valdés | Rita Prosmiti
Source: Molecules (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Quantum Data-Driven Modeling of Interactions and Vibrational Spectral Bands in Cationic Light Noble-Gas Hydrides: [He2H]+ and [Ne2H]+ Motivated by two of the most unexpected discoveries in recent years-the detection of ArH<sup>+</sup> and HeH+ noble gas molecules in the cold, low-pressure regions of the Universe-we investigate [He<sub>2</sub>H]<sup>+</sup> and [Ne<sub>2</sub>H]<sup>+</sup> as potentially detectable species in the interstellar medium, providing new insights into their energetic and spectral properties. These findings are crucial for advancing our understanding of noble gas chemistry in astrophysical environments. To achieve this, we employed a data-driven approach to construct a high-accuracy machine-learning potential energy surface using the reproducing kernel Hilbert space method. Training and testing datasets are generated via high-level CCSD(T)/CBS[56] quantum chemistry computations, followed by a rigorous validation protocol to ensure the reliability of the potential. The ML-PES is then used to compute vibrational states within the MCTDH framework, and assign spectral transitions for the most common isotopologues of these species in the interstellar medium. Our results are compared with previously recorded values, revealing that both cations exhibit a prominent proton-shuttle motion within the infrared spectral range, making them strong candidates for telescopic observation. This study provides a solid computational foundation, based on rigorous, fully quantum treatments, aiming to assist in the identification of these yet unobserved He/Ne hydride cations in astrophysical environments. Noble gas ; Cationic polymerization ; Quantum ; Chemical physics ; Chemistry ; Physics ; Materials science ; Atomic physics ; Physical chemistry ; Photochemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Vitor S. Duarte | Renata Layse G. de Paula | Leonardo R. Almeida | Giulio D. C. D’Oliveira | Caridad N. Pérez | Jean M. F. Custódio | Valter H. Carvalho‐Silva | Andrea Lombardi | Hamilton B. Napolitano
Source: Biofuels Bioproducts and Biorefining (article, 2025-04-15)
Field: Chemistry — Organic Chemistry

Exploring quinolinone–chalcones: Synthesis, antioxidant potential and industrial applications in biofuels Abstract Biofuels undergo oxidative degradation, which reduces their storage stability and efficiency, hindering their large‐scale adoption and integration into the global energy matrix. To address this issue, we synthesized and characterized four novel quinolinone–chalcone derivatives (substituted with Br, Cl, OCH 3 and OCH 2 CH 3 ) as potential antioxidant additives. Structural analysis, density functional theory calculations and machine learning‐based hydroxyl radical scavenging rate evaluation were performed to assess the influence of the substituents on stability and antioxidant capacity. Single‐crystal X‐ray diffraction revealed C–H···O and C–H···π interactions contributing to supramolecular stability, while electronic property analysis indicated enhanced kinetic stability and regions near the sulfonamide group prone to nucleophilic attack. Machine learning predictions showed that the bromine derivative exhibited a hydroxyl radical scavenging rate of 7.97 × 10 9 m −1 s −1 , comparable with the commercial antioxidant butylated hydroxytoluene (4.34 × 10 9 m −1 s −1 ). These findings highlight the potential of quinolinone–chalcone derivatives as efficient antioxidant additives, improving the oxidative stability and sustainability of biofuels. Biofuel ; Antioxidant ; Chemistry ; Industrial chemistry ; Biotechnology ; Biochemical engineering ; Pulp and paper industry ; Organic chemistry ; Biology ; Engineering Synthesis and biological activity ; Free Radicals and Antioxidants ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Huaxin Song | Zhipeng Sun | Yue Hao | Xiangfen Jiang | Xuebin Wang
Source: Journal of Material Science and Technology (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Machine learning-assisted prediction and optimization of electrocatalytic activity Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Yucheng Hou | Xiaobo She | Xiang Wang | Xiwu Huang | Lei Li | Yingxu Wang | Xiaoyu Yang | Jiaqing Yuan | Lingfei Li | Changtai Xia | Shan Li | Yufeng Guo | Weihua Tang | Yu Liu
Source: Applied Physics Letters (article, 2025-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-performance triple-band ultraviolet photodetector based on mixed-phase InGaO thin film for material identification The wavelength-dependent nature of material reflectance in the ultraviolet (UV) range makes multi-band UV response analysis an effective technique for material identification. In this work, a triple-band UV photodetector was constructed on the In2O3-Ga2O3 (InGaO) mixed-phase thin films. By incorporating In into Ga2O3 via plasma-enhanced chemical vapor deposition method, the bandgap of the mixed-phase InGaO thin films was modulated to 4.64 eV, extending the spectral response range to cover UVA (365 nm), UVB (295 nm), and UVC (255 nm) bands. This broadband photoresponse enables quantitative analysis of material-specific UV reflectance signatures, offering a reliable basis for material differentiation. Integrating a 5 × 4 photodetector array with convolutional neural networks, a precise identification and an accurate imaging of geometrically identical materials (Al, Ag, Si, and Au) were realized. For Al, Ag, and Si, this system achieves an overall accuracy exceeding 95%. This work proposes an efficient strategy for nondestructive surface inspection and material recognition by combining multi-spectral sensing with intelligent data processing. Photodetector ; Thin film ; Ultraviolet ; Chemical vapor deposition ; Broadband ; Band gap ; Infrared Ga2O3 and related materials ; Transition Metal Oxide Nanomaterials ; Thin-Film Transistor Technologies
Authors: Zhiquan Kho | Andy Bridger | Keith T. Butler | Ercin C. Duran | Mohsen Danaie | Alexander S. Eggeman
Source: APL Materials (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

On the use of clustering workflows for automated microstructure segmentation of analytical STEM datasets This study considers the issue of automated segmentation of scanning transmission electron microscopy (STEM) datasets using unsupervised machine learning approaches. To this end, a systematic comparison of two clustering workflows that had been established in previous literature was performed on two distinct material systems—an experimentally acquired Co2FeSi Heusler alloy and a simulated Au-matrix and Al2Cu precipitate. The cluster outputs were evaluated using a variety of unsupervised clustering metrics measuring separation and cohesion. It was found that the cluster output of a variational autoencoder (VAE) performed better compared to a more conventional latent transformation via Uniform Manifold Approximation &amp; Projection (UMAP) on 4D-STEM data alone. However, the UMAP workflow applied to merged 4D-STEM and STEM-energy dispersive x-ray (STEM-EDX) data produced the best cluster output overall, indicating that the correlated information provides beneficial constraints to the latent space. A potential general workflow for analyzing merged datasets to identify structural-composition changes across different material systems is proposed. Workflow ; Materials science ; Cluster analysis ; Microstructure ; Segmentation ; Computer science ; Data mining ; Systems engineering ; Artificial intelligence ; Metallurgy ; Engineering ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Shubham Agrawal | Sayee Srikarah Volaity | Oluwadamilare Charles Adesina | Bryan K. Aylas‐Paredes | Taihao Han | Srinivas Kilambi | Aditya Kumar | Narayanan Neithalath
Source: Journal of the American Ceramic Society (article, 2025-10-15)
Field: Materials Science — Biomaterials

Ultrafast belite (β‐C <sub>2</sub> S) production using biofuel‐based combustion synthesis Abstract This study presents a novel low‐temperature, combustion synthesis (CS) approach for the rapid production of belite‐rich cements. CS leverages the exothermic heat released from the combustion of biofuels such as lignin and/or biomass, intermixed with pelletized limestone and quartz. At an imposed furnace temperature of ∼700°C (as opposed to 1200°C–1300°C required in a conventional kiln), the source materials are rapidly transformed to belite, resulting in energy, emissions, and economic benefits. This work explores the influence of various process parameters, viz., fuel types and contents, airflow rate, porosity, holding temperature, and holding time on the efficiency of CS‐based belite synthesis. Through careful optimization of these parameters, including through machine learning‐based methods, &gt;90% belite content is obtained in the synthesized pellets. Advanced analytical tests show that the belite produced from CS closely resembles that obtained from traditional high‐temperature processing. CS has the potential to significantly reduce energy consumption and emissions associated with belite production, and to accelerate the synthesis process by three‐to‐four times as compared to the conventional method. Belite ; Combustion ; Process (computing) ; Exothermic reaction ; Work (physics) ; Fuel efficiency Clay minerals and soil interactions ; Metal Extraction and Bioleaching ; Minerals Flotation and Separation Techniques
Authors: Orlando A. Mendible-Barreto | Jonathan K. Whitmer | Yamil J. Colón
Source: The Journal of Chemical Physics (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Considerations in the use of machine learning force fields for free energy calculations Machine learning force fields (MLFFs) promise to accurately describe the potential energy surface of molecules at the ab initio level of theory with improved computational efficiency. Within MLFFs, equivariant graph neural networks (EQNNs) have shown great promise in accuracy and performance and are the focus of this work. The capability of EQNNs to recover free energy surfaces (FES) remains to be thoroughly investigated. In this work, we investigate the impact of collective variables (CVs) distribution within the training data on the accuracy of EQNNs predicting the FES of butane and alanine dipeptide. A generalizable workflow is presented in which training configurations are generated with classical molecular dynamics simulations, and energies and forces are obtained with ab initio calculations. We evaluate how bond and angle constraints in the training data influence the accuracy of EQNN force fields in reproducing the FES of the molecules at both classical and ab initio levels of theory. Results indicate that the model's accuracy is unaffected by the distribution of sampled CVs during training, given that the training data includes configurations from characteristic regions of the system's FES. However, when the training data is obtained from classical simulations, the EQNN struggles to extrapolate the free energy for configurations with high free energy. In contrast, models trained with the same configurations on ab initio data show improved extrapolation accuracy. The findings underscore the difficulties in creating a comprehensive training dataset for EQNNs to predict FESs and highlight the importance of prior knowledge of the system's FES. Extrapolation ; Ab initio ; Computer science ; Artificial intelligence ; Work (physics) ; Force field (fiction) ; Energy (signal processing) ; Machine learning ; Statistical physics ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Huijie Zhang | Y.C. Lin | Gang Su | Song Zhang | Guicheng Wu | Ning‐Fu Zeng
Source: Advanced Engineering Materials (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Effect of Hot Deformation Plus Aging Treatment on the Microhardness of a TC18 Alloy The effect of hot deformation plus aging treatment on the microhardness of a solid solution TC18 titanium alloy is investigated. First, the microstructure evolution during hot deformation and aging treatment stages is analyzed. Additionally, the influence of deformation parameters on microstructure evolutions of the aged alloy is further studied. After aging treatment, the alloy exhibits a trimodal microstructure, which contributes to its excellent mechanical performance. Aging treatment significantly refines the microstructure, which is also influenced by deformation parameters. The microhardness variations at different stages are subsequently analyzed and how deformation conditions affect the microhardness of the aged alloy is examined. Aging treatment substantially enhances microhardness through the precipitation of acicular α phases. Deformation parameters also have a significant impact. A predictive model for the microhardness of the aged alloy is established using machine learning methods, achieving high accuracy. This research is significant for tailoring the desired microhardness of TC18 alloys during die forging and subsequent aging treatment. Materials science ; Indentation hardness ; Metallurgy ; Deformation (meteorology) ; Alloy ; Microstructure ; Composite material Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Metal Alloys Wear and Properties
Authors: Gang Wu | Hong‐Hui Wu | Yanjing Su | Lijie Qiao | Lei Gao
Source: Applied Surface Science (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Revealing critical features determining the hydrogen adsorption behaviors in ferric oxide films via machine learning Ferric ; Adsorption ; Oxide ; Hydrogen ; Chemical engineering ; Materials science ; Inorganic chemistry ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Yunzhuo Zhang | Shu‐Lin Bai | Da Wan | Ai Peng | Pengfei Zhang | Zhiwei Zhang | Yujie Bao | Zhanpeng Xu | Shuwei Tang
Source: Chemical Engineering Science (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Decoding the ZT optimization of BiCuSeO thermoelectrics via interpretable machine learning Boosting (machine learning) ; Figure of merit ; Mean squared error ; Gradient boosting ; Hyperparameter ; Thermoelectric materials ; Thermoelectric effect ; Root mean square Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Advanced Technologies in Various Fields
Authors: Eric R. Beyerle | Pratyush Tiwary
Source: Physical Review Letters (article, 2025-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inferring the Isotropic-Nematic Phase Transition with Generative Machine Learning Generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. We utilize a score-based modeling procedure called thermodynamic maps to describe the isotropic-nematic phase transition in a melt of Gay-Berne ellipsoids. When trained on samples from a single temperature on either side of the phase transition, this generative machine learning approach infers effectively the nematic order parameter at intermediate temperatures. These results demonstrate score-based models' ability to learn the physics of a nontrivial liquid crystal phase transition. Liquid crystal ; Phase transition ; Isotropy ; Generative grammar ; Phase (matter) ; Materials science ; Condensed matter physics ; Generative model ; Statistical physics ; Optics ; Theoretical physics ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry ; Visual perception and processing mechanisms