jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: X. Liu | Belinda Tan | Pengcheng Xiang | Wentao Huang | Xiaoshuan Zhang
Source: Journal of Food Process Engineering (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

Multimodal Data Fusion Based Intelligent Grading System for Kiwifruit Maturity Using Spectral‐Impedance Properties ABSTRACT The quality of kiwifruit directly affects its market value. However, current grading methods mainly rely on manual operations, with incomplete grading standards and limited accuracy. To address this issue, this study proposes a multimodal sensing approach that integrates visible–near infrared spectroscopy (VIS–NIR) and bioimpedance technology. By leveraging the complementary advantages of VIS–NIR spectroscopy technology and bioimpedance technology, this method combines bioimpedance analysis with VIS–NIR spectroscopy analysis to improve the accuracy of non‐destructive post‐harvest ripeness detection for kiwifruit. In this study, 120 “Hayward” kiwifruit samples stored at 10°C were used as the research objects. Their VIS–NIR spectral characteristics (450–860 nm) and bioimpedance properties (10 Hz–1 MHz) were measured, while physical and chemical indicators such as firmness, soluble solids content (SSC), and water loss rate were tested, and sensory evaluations were conducted. On this basis, a machine learning‐based ripeness grading model was developed. A three‐level data fusion strategy was adopted, including low‐level feature concatenation, intermediate‐level amplitude/phase combination, and high‐level consensus decision‐making. Additionally, the performance of BP (Back Propagation) neural networks, RBF (Radial Basis Function) neural networks, and SVM (Support Vector Machine) models was compared. The optimal grading performance was achieved by directly concatenating VIS–NIR spectral data and bioimpedance data and inputting them into the SVM model—yielding an accuracy of 87.39%, precision of 87.38%, and recall of 87.39%. This performance significantly outperformed single‐modality approaches (VIS–NIR spectroscopy: 77.62%; bioimpedance: 76.49%). This method provides a new technical pathway for non‐destructive quality detection and precise grading of post‐harvest kiwifruit. Ripeness ; Grading (engineering) ; Support vector machine ; Pattern recognition (psychology) ; Sensor fusion ; Fusion ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Yi‐Lin Shen | Dong-Ying Lan | Pinjing He | Ya-Ping Qi | Peng Wei | Fan Lü | Hua Zhang
Source: TrAC Trends in Analytical Chemistry (review, 2025-02-15)
Field: Chemistry — Analytical Chemistry

Nondestructive optical and spectroscopic techniques combined with machine learning for identifying solid waste: A review Materials science ; Nondestructive testing ; Process engineering ; Computer science ; Environmental science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Recycling and Waste Management Techniques
Authors: Gang Wu | Jun-Hao Wan | Chen Qian | Xian‐Wei Liu
Source: Accounts of Chemical Research (article, 2025-08-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic Scattering Interferometric Microscopy: Decoding the Dynamic Interfacial Chemistry of Single Nanoparticles ConspectusThe ability to detect and image nanomaterials at interfaces is crucial for a wide range of applications, from the engineering and characterization of nanocomposites to enabling label-free detection for biomedical diagnostics and therapy. Light microscopy, which relies on the optical properties of nanomaterials, has significantly contributed to this goal due to its adequate temporal and spatial resolutions and compatibility with diverse application scenarios. However, the optical intensity readout of these label-free optical imaging techniques inherently limits their selectivity. Consequently, visualizing dynamic interfacial changes over a single particle with high spatiotemporal resolution under mild solution reaction conditions remains a challenge.In this Account, we highlight the recent progress in plasmonic scattering interferometric microscopy (PSIM), a technique developed to address these challenges. We begin with the fundamental principles of plasmonics and light scattering relevant to PSIM, demonstrating its ability to optically identify and measure various nanoparticles. Significant improvements in imaging quality were achieved through the development of a high-resolution plasmonic scattering interferometric microscope (HR-PSIM). These advances have enabled the real-time observation of compositional transformations in single nanoparticles, offering new insights into their electrocatalytic activity and reaction kinetics at the single-particle level. Leveraging the high-resolution capacity of HR-PSIM for visualizing chemical reactions, we explored electrochemical processes in real-time with remarkable spatial resolution. In addition, we introduce novel algorithmic tools for noise reduction and automation, designed to eliminate background interference and reconstruct high-quality, high-resolution images. The integration of deep learning into PSIM has further advanced the technique, enabling the precise localization and identification of nanoparticles with enhanced robustness across varying spatiotemporal conditions. This Account concludes with an outlook on the future development of PSIM, discussing current limitations and the potential for further enhancements. We envision that the continued refinement of PSIM will open new avenues for studying surface chemistry and nanoscale reactions, leading to significant breakthroughs in nanoscience research and a broad range of practical applications. Plasmon ; Interferometry ; Microscopy ; Nanoparticle ; Plasmonic nanoparticles ; Nanotechnology ; Scattering ; Chemistry ; Optical microscope ; Dynamic light scattering ; Materials science ; Optics ; Optoelectronics ; Physics ; Scanning electron microscope Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced Biosensing Techniques and Applications
Authors: He Sun | Shyam Dwaraknath | Handong Ling | Kristin A. Persson | Sophia E. Hayes
Source: Scientific Reports (article, 2025-07-21)
Field: Chemistry — Spectroscopy

Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as <sup>27</sup>Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 <sup>27</sup>Al crystalline solids using DFT and benchmarked them against 105 experimentally measured <sup>27</sup>Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R<sup>2</sup> value of 0.98 and an RMSE of 0.61 MHz for C<sub>Q</sub>, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of <sup>27</sup>Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction. Electric field gradient ; Electric field ; Field (mathematics) ; Nuclear magnetic resonance ; Computer science ; Materials science ; Physics ; Mathematics ; Pure mathematics ; Quantum mechanics Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Hidenori Nakajima | C. Murata | Naoki Noto | Susumu Saito
Source: The Journal of Organic Chemistry (article, 2025-01-06)
Field: Chemistry — Inorganic Chemistry

Database Construction for the Virtual Screening of the Ruthenium-Catalyzed Hydrogenation of Ketones During the recent development of machine-learning (ML) methods for organic synthesis, the value of "failed experiments" has increasingly been acknowledged. Accordingly, we have developed an exhaustive database comprising 300 entries of experimental data obtained by performing ruthenium-catalyzed hydrogenation reactions using 10 ketones as substrates and 30 phosphine ligands. After evaluating the predictive performance of ML models using the constructed database, we conducted a virtual screening of commercially available phosphine ligands. For the virtual screening, we utilized several models, such as histogram-based gradient boosting and Ridge regression, combined with the Mordred descriptors and MACCSKeys, respectively. The disclosed approach resulted in the identification of high-performance phosphine ligands, and the rationale behind the predictions in the virtual screening was analyzed using SHAP. Phosphine ; Ruthenium ; Virtual screening ; Computer science ; Database ; Gradient boosting ; Chemical database ; Boosting (machine learning) ; Predictive value ; Identification (biology) ; Chemistry ; Combinatorial chemistry ; Catalysis ; Artificial intelligence ; Organic chemistry ; Computational chemistry Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Marta Stefańska | Thomas Müntener | Sebastian Hiller
Source: Journal of the American Chemical Society (article, 2025-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predictions of Steady-State Photo-CIDNP Enhancement by Machine Learning Photochemically induced dynamic nuclear polarization (photo-CIDNP) is a hyperpolarization method used to boost signal sensitivity in NMR spectroscopy. So far, there is no theory to predict the steady-state photo-CIDNP enhancement reliably, and hence, suitable target molecules need to be identified through tedious experimental screenings. Here, we explore the use of machine learning to predict steady-state photo-CIDNP enhancement. For a series of 27 indole-, five amino-acid-, and eight phenol-derivatives, the signal-to-noise enhancement (SNE) of steady-state photo-CIDNP experiments was measured and then connected to a combination of eight molecular features. The nucleophilic Fukui index was identified as a strong qualitative indicator of the site with the highest SNE in each molecule. Furthermore, a semiquantitative machine learning model based on Logistic Regression identified the sites with high enhancements (SNE > 90) in 100% of cases. Among several quantitative machine learning models for enhancement prediction, CatBoost Regressor and K-Nearest Neighbors showed the best performance. The results demonstrate the high potential of machine learning approaches for predictions of photo-CIDNP SNE, which will enable virtual prescreening of compound libraries. CIDNP ; Chemistry ; Biological system ; Hyperpolarization (physics) ; Polarization (electrochemistry) ; Steady state (chemistry) ; Artificial intelligence ; Chemical physics ; Nuclear magnetic resonance spectroscopy ; Computer science ; Organic chemistry ; Physical chemistry Nonlinear Optical Materials Research ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Xiaohan Song | Weidong Zheng | Haoqiang Ai | Hao Zhou | Liyin Feng | Zheng Cui | Ruiqiang Guo
Source: Journal of Applied Physics (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Key physical descriptors for predicting interfacial thermal resistance by machine learning Interfacial thermal resistance (ITR) plays a crucial role in the thermal management of micro-nano devices. Compared with traditional approaches, machine learning is a cost-effective, accurate, and efficient method for predicting material properties, but its limited interpretability hinders the exploration of underlying mechanisms. In this study, we have constructed machine learning models that can effectively predict ITR by comparing six algorithms, with the random forest (RF) model achieving the highest accuracy. Through feature engineering, 39 descriptors have been narrowed down to 7 descriptors while maintaining accuracy, among which 5 descriptors are all strongly related to the bonding strength and, thus, the vibrational properties of the two materials consisting of the interface. In particular, we have defined new descriptors corresponding to the matching degree of the sound velocity and applied it to obtain an improved RF model. We further employed this improved RF model to explore materials with a low ITR when in contact with Si, providing guidance for the search of chip heat dissipation materials. The key physical descriptors agree well with the physical picture of phonon transport across interfaces and enhance the interpretability of the machine learning models, which can accelerate the design and optimization of thermal interfaces in relevant applications. Key (lock) ; Thermal resistance ; Computer science ; Thermal ; Resistance (ecology) ; Materials science ; Physics ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Shuxia Ouyang | Qingfeng Wu | Rae Eon Kim | Jae Heung Lee | Jeong‐Ah Lee | Hyoung Seop Kim
Source: Metals and Materials International (article, 2025-02-24)
Field: Materials Science — Biomaterials

Recent Progress in Developing Creep-Resistant Mg-Gd-Based Alloys Materials science ; Solid mechanics ; Creep ; Metallurgy ; Engineering physics ; Composite material ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials
Authors: Yuliang Shi | Farnaz A. Shakib
Source: The Journal of Physical Chemistry C (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Machine Learning Committee Neural Network Potential Energy Surfaces for Two-Dimensional Metal–Organic Frameworks Two-dimensional (2D) layered metal–organic frameworks (MOFs) are gaining attention due to their unique structural and electronic properties with promising applications in compact electronic device fabrication. Long-time and large-scale molecular dynamics simulations of these materials can enhance and expedite the mapping out of their structure–property–function relationships for these applications. To make such simulations more feasible, herein, we construct a high-dimensional committee neural network potential (CNNP) for archetypal 2D MOFs Ni3(HIB)2 and Ni3(HITP)2 where HIB = hexaiminobenzene and HITP = hexaiminotriphenylene. We harness the power of active learning and committee neural networks to obtain a CNNP model by using only hundreds of snapshots from ab initio molecular dynamics (AIMD) trajectories. The developed CNNP model allows for simulations of thousands of atoms over extended time scales, which is typically unfeasible with AIMD simulations while maintaining the accuracy of the reference data. Our stable MD simulations based on the developed CNNP model reveal the flexible nature of the studied 2D MOFs at room temperature, including puckered layers, as opposed to the planar ones from 0 K electronic structure calculations. Furthermore, our model demonstrates transferability between bulk and monolayers, as well as different organic linkers. As the first model of its kind, we show that the high-dimensional CNNP models could be a reliable and effective approach for future studies on 2D MOFs. Planar ; Artificial neural network ; Molecular dynamics ; Computer science ; Metal-organic framework ; Electronic structure ; Construct (python library) ; Materials science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Memory and Neural Computing
Authors: Pradeep Sachan | Anwesha Mahapatra | Lalith Adithya Sai Channapragada | Rajwinder Kaur | Shubham Sahay | Prakash Chandra Mondal
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Electrochemistry

Electrosynthesis of molecular memory elements The increasing pace of computing beyond Moore's law scaling and the von Neumann bottleneck necessitates a universal memory solution that offers high speed, low-power consumption, scalability, and non-volatility, such as resistive switching memristors. However, inconsistencies in the homogeneity and uniformity of surface coverage for switching materials on various electrode substrates, especially those prepared <i>via</i> non-covalent methods, result in reduced interfacial stability, thus yielding poor device reproducibility. Electrosynthesis, a reliable and versatile technique for creating covalently bound molecular films on electrode surfaces, enables controlled deposition of large-area, high-quality molecular thin films with nanoscale thicknesses, making it an ideal platform for scalable nanoelectronics. This study explores the electrochemical grafting of two distinct ruthenium complexes: structurally symmetrical [Ru(tpy-ph-NH<sub>2</sub>)<sub>2</sub>](2PF<sub>6</sub>)] (1) and asymmetrical [Ru(tpy-ph-NH<sub>2</sub>)(naptpy)](2PF<sub>6</sub>)] (2), for the fabrication of large-area, two-terminal molecular junctions intended for resistive switching memory applications. A comparative analysis reveals that 2 exhibits relatively superior memory performance to 1, attributed to its donor-acceptor configuration playing a crucial role. Stable vertical molecular junctions with the configuration ITO/Ru complex<sub>24nm</sub>/Al were fabricated, and electrical measurements were carried out to understand the enhanced switching characteristics. The redox-active molecular devices demonstrate non-volatile resistive switching behavior within a ±3.0 V operation window, featuring a large <i>I</i> <sub>ON</sub>/<i>I</i> <sub>OFF</sub> ratio (∼10<sup>3</sup>), a high power consumption ratio (SET/RESET = 25.5 mJ/75000 mJ), and switching time (SET/RESET = 56/24 ms). Synapse-like potentiation and convolutional neural network simulation were performed, highlighting the potential of these devices for in-memory data processing applications. Electrosynthesis ; Computer science ; Chemistry ; Electrochemistry ; Physical chemistry Electrochemical Analysis and Applications ; Surface Chemistry and Catalysis
Authors: Yinglong Hu | Ronghao Li | Shuang Quan Fang | Jianbin Luo | Hao Ma
Source: International Journal of Heat and Mass Transfer (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Tunable thermal conductivity and anisotropy of two-dimensional fullerene networks controlled by covalent bonding connections Materials science ; Covalent bond ; Fullerene ; Thermal conductivity ; Anisotropy ; Thermal ; Conductivity ; Chemical physics ; Composite material ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Optics ; Physics ; Chemistry Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Ali Moeini | Fatemeh Shariatmadar Tehrani | Alireza Naeimi Sadigh
Source: International Journal of Energy Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Band Gap Prediction/Classification and Feature Importance Analysis of Inorganic Perovskites Perovskites are a class of materials, known for their diverse structural, electronic, and optical properties. Band gap in perovskites is crucial in determining their suitability for applications such as solar cells, light‐emitting diodes, and photodetectors. By tuning the band gap through composition and structural modifications, perovskites can be optimized for specific optoelectronic and energy‐related applications, making them a versatile material in modern technology. Machine learning (ML) provides an efficient approach to predicting material band gaps by analyzing atomic and structural features, facilitating the discovery of materials with tailored electronic properties. This study employs adaptive boosting regression (ABR), random forest regression (RFR), and gradient boosting regression (GBR) for band gap prediction, alongside support vector machine (SVM), random forest classifier (RFC), and multilayer perceptron (MLP) for classifying compounds with zero and nonzero band gaps. Regression models are assessed using mean absolute error (MAE), mean squared error (MSE), and R 2 , while classification performance is evaluated based on accuracy, precision, recall, and F1‐score. ABR excels in predicting band gaps of inorganic perovskites, while RFC is the most effective model for classification. Feature analysis identifies the standard deviation of valence charges as the key predictor. This study underscores ML’s potential to accelerate perovskite discovery through accurate band gap predictions. Band gap ; Feature (linguistics) ; Artificial intelligence ; Materials science ; Machine learning ; Computer science ; Optoelectronics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Iradat Hussain Mafat | Sridhar Palla
Source: Journal of environmental chemical engineering (article, 2025-06-29)
Field: Chemistry — Inorganic Chemistry

Recent advances in computational screening of metal-organic frameworks for carbon capture Carbon fibers ; Metal-organic framework ; Environmental chemistry ; Environmental science ; Computer science ; Chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Zeolite Catalysis and Synthesis
Authors: Hassane Lgaz | Han‐Seung Lee | Mouslim Messali
Source: Ceramics International (article, 2025-09-22)
Field: Materials Science — Surfaces, Coatings and Films

Self-healing ceramic coatings: Mechanisms, design strategies, and emerging applications for extreme environments Ceramic ; Aerospace ; Corrosion ; Self-healing ; Oxide ; Damage tolerance ; Crystallization ; Material Design Surface Modification and Superhydrophobicity ; Corrosion Behavior and Inhibition ; Advanced Photocatalysis Techniques
Authors: Dan Wang | Hongping Zhou | Zhongyi Guo | Kai Guo
Source: Optics Letters (article, 2025-06-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spectrum prediction and inverse design of metasurfaces via transfer learning based on material similarity Spectrum prediction and inverse design of metasurfaces based on deep learning have been a hot research topic. The dependence of deep learning on data is a major challenge for its widespread application in the field of metasurfaces. In this letter, we proposed a transfer learning method based on material similarity to accomplish spectrum prediction and the inverse design of metasurfaces. As a proof-of-concept, we investigated the transfer tasks of two types of metasurface, i.e., absorption metasurface and polarization conversion metasurface, whose material properties could be represented by the Drude model to reflect the material similarity, and accomplished the spectrum prediction and inverse design through transfer learning. We achieved 50% data saving, demonstrating reduction of the reliance on training data volume while ensuring network performance. The proposed concept may provide a new avenue for metasurface and metamaterial designs. Optics ; Similarity (geometry) ; Inverse ; Computer science ; Inverse problem ; Inverse scattering problem ; Physics ; Artificial intelligence ; Scattering ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Animal Vocal Communication and Behavior