jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Xu | Meixin Chen | Yuting You | Xueyuan Bai | Wei Zhang
Source: Journal of Food Composition and Analysis (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging combined with genetic algorithm-optimized machine learning for the rapid and nondestructive quantification of ginsenosides and polysaccharides in Panax ginseng Ginseng, a high - value food and medicinal ingredient, is influenced by origin. Prior studies used spectroscopy for component analysis, but model optimization, band application, and field use remain challenging. This study presents a novel method using hyperspectral imaging to create a dual - function non - destructive model. It can measure ginsenoside (RS) and polysaccharide (RP) contents simultaneously and identify the geographic origin.The integrated methodology includes two parts. First, it uses component - specific algorithms for quantitative prediction. ELM performs well in RS prediction in VNIR band (R² = 0.86), and GA - optimized back - propagation neural network is excellent in RP prediction (R² = 0.85). Second, it classifies the origin. It can use second - order derivative preprocessing with particle swarm optimization (accuracy = 0.91 in VNIR) or standalone processing (accuracy = 0.91 in short - wave infrared). The developed four - dimensional optimization framework ("component - band - model - feature") shows VNIR's detection advantages for both components and improves feature selection stability by 30% through GA iterative voting. Therefore, these findings establish an efficient, nondestructive ginseng quality - control tool enabling food producers to verify composition and origin during raw material screening and production standardization. • Ginseng composition is strongly affected by its geographic origin • A nondestructive model for determining ginseng composition is developed • An extreme machine learning model excels in predicting ginsenoside content • Second-order derivative preprocessing is employed for ginseng origin identification Hyperspectral imaging ; Ginseng ; Preprocessor ; VNIR ; Extreme learning machine ; Particle swarm optimization ; Pattern recognition (psychology) ; Data pre-processing ; Ginsenoside ; Artificial neural network Spectroscopy and Chemometric Analyses ; Ginseng Biological Effects and Applications ; Traditional Chinese Medicine Analysis
Authors: Heru Agung Saputra | Rosy Amalia Kurnia Putri | Eleazer L. Vivas | Herman | Amru Daulay | Fika Rofiek Mufakhir | Yayat Iman Supriyatna | Hendra Prasetia | Suyanti Suyanti | Sudibyo | Ulin Herlina | Asnan Rinovian | Sudiyarmanto Sudiyarmanto | Fajar Nurjaman | Widi Astuti
Source: Chemical Engineering Journal (article, 2025-12-20)
Field: Chemistry — Inorganic Chemistry

Recent advances in targeted adsorption of rare earth elements using metal-organic frameworks Adsorption ; Rare earth ; Metal-organic framework ; Porous medium ; Porosity Metal-Organic Frameworks: Synthesis and Applications ; Geochemistry and Elemental Analysis ; Layered Double Hydroxides Synthesis and Applications
Authors: Binqiang Li | Xianghong Liu | Kaixuan Wang | Huiwen Bai | Xingfeng Guo | Jie Jia | Zhiping Li | Binbin Wang
Source: Materials Today Communications (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Essential concerns of NiTi shape memory alloys and their advancement in additive manufacturing Materials science ; Nickel titanium ; Shape-memory alloy ; Metallurgy ; Composite material Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience
Authors: Swaminathan Ganesan | Sachchida Nand Pandey | Senthilkumar Krishnasamy | Senthil Muthu Kumar Thiagmani
Source: Discover Materials (article, 2025-02-23)
Field: Materials Science — Materials Chemistry

Prediction of thermal cycling behaviour of Ni-rich NiTi SMA using empirical and artificial neural network modelling NiTi SMAs, also known as Nitinol, are well-known and widely used due to their unique properties. This study predicts the transformation behaviour of a binary near-equiatomic shape memory alloy (SMA) during thermal cycling using empirical and ANN-based models. The input data was generated through thermal cycling tests using a differential scanning calorimeter (DSC) under a nitrogen atmosphere, wherein the maximum and minimum temperatures were varied based on the transformation temperatures of the alloy. Three different models, i.e. symmetrical, asymmetrical and artificial neural network (ANN), were developed to understand the transformation behaviour of the alloy using the same set of test data for validation. For qualitative and quantitative comparisons of the model, priority was given to the simplicity of the model (minimum variables) and the accuracy of the prediction. The results show that the ANN-based model can predict the transformation behaviour more accurately (99.81%) as compared to the conventional empirical models, i.e., symmetric (96.64%) and asymmetric models (98.14%). Artificial neural network ; SMA* ; Nickel titanium ; Cycling ; Temperature cycling ; Shape-memory alloy ; Thermal ; Computer science ; Artificial intelligence ; Thermodynamics ; Physics ; Geography Shape Memory Alloy Transformations ; Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Yue Yang | Shiang‐Tai Lin
Source: Journal of Chemical Information and Modeling (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Physics-Embedded Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems We present TeNNet-SAC (<b>T</b>hermodynamics-<b>e</b>mbedded <b>N</b>eural <b>Net</b>work for <b>S</b>egment <b>A</b>ctivity <b>C</b>oefficient) model, a novel machine learning framework for predicting activity coefficients in liquid mixtures using only the SMILES representations of the constituent molecules. Inspired by the quantum chemistry-based COSMO-SAC model, TeNNet-SAC evaluates activity coefficients by summing contributions from molecular surface segments. The model comprises three core components: (1) a σ-profile predictor, which generates molecular fingerprints (i.e., surface segment charge histogram or σ-profile) directly from SMILES; (2) a geometry predictor, which estimates molecular volume and surface area from SMILES; and (3) a Γ predictor, which computes the activity coefficients of surface segments in solution. The σ-profile and geometry predictors are trained on 39,745 quantum solvation calculations. The Γ predictor is initially pretrained on one million synthetic data points to capture physically consistent behavior and is subsequently fine-tuned end-to-end using experimental activity coefficient data to improve predictive accuracy. The base TeNNet-SAC model achieves accuracy comparable to COSMO-SAC, while the fine-tuned version consistently outperforms COSMO-SAC across benchmark systems. By treating segment activity coefficients as intermediate variables, TeNNet-SAC naturally generalizes to multicomponent mixtures and satisfies thermodynamic consistency, offering a robust and scalable solution for activity coefficient prediction. Benchmark (surveying) ; Surface (topology) ; Scalability ; Quantum ; Activity coefficient ; Histogram ; Solvation ; Data point ; Core (optical fiber) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Srinitish Srinivasan | Chandraumakantham OmKumar
Source: Scientific Reports (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

An edge sensitivity based gradient attack on graph isomorphic networks for graph classification problems Graph Neural Networks have gained popularity over the past few years. Their ability to model relationships between entities of the same and different kind, represent molecules, model flow etc. have made them a go to tool for researchers. However, owing to the abstract nature of graphs, there exists no ideal transformation to represent nodes and edges in the euclidean space. Moreover, GNNs are highly susceptible to adversarial attacks. However, a gradient based attack based on latent space embeddings does not exist in the GNN literature. Such attacks, classified as white box attacks, tamper with latent space representation of graphs without creating any noticeable difference in the overall distribution. Developing and testing GNN models based on such attacks on graph classification tasks would enable researchers to understand and develop stronger and more robust classification systems. Further, adversarial attack tests in the GNN literature have been performed on weaker, less representative neural network architectures. In order to tackle these gaps in literature, we propose a white box gradient based attack developed from contrastive latent space representations. Further, we develop a strong base(victim) learning spectral and spatial properties of graphs with consideration of isomorphic properties. We experimentally validate this model on 4 benchmark datasets in the molecular property prediction literature where our model outperformed over 75% of all LLM-based architectures. On attacking this model with our proposed adversarial attack strategy, the overall performance drops at an average of 25% thereby clearing a few gaps in the existent literature. The code for our paper can be found at https://github.com/Deceptrax123/An-edge-sensitivity-based-gradient-attack-on-GIN-for-inductive-problems. Computer science ; Graph ; Artificial intelligence ; Theoretical computer science ; Euclidean space ; Adversarial system ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: H.-Q. Jin | Kenneth M. Merz
Source: Journal of Chemical Theory and Computation (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and <i>ab initio</i> computation methods are widely used to evaluate PA; however, both methods are resource-intensive and time-consuming. Therefore, there is a critical need for efficient methods to estimate PA, enabling the rapid identification of the most favorable protonation site in complex organic molecules with multiple proton binding sites. In this work, we developed a fast and accurate method for PA prediction by using multiple descriptors in combination with machine learning (ML) models. Using a comprehensive set of 186 descriptors, our model demonstrated strong predictive performance, with an <i>R</i><sup>2</sup> of 0.96 and a MAE of 2.47 kcal/mol, comparable to experimental uncertainty. Furthermore, we designed quantum circuits as feature encoders for a classical neural network. To evaluate the effectiveness of this hybrid quantum-classical model, we compared its performance with traditional ML models using a reduced feature set derived from the full set. A correlation analysis showed that the quantum-encoded representations have a stronger positive correlation with the target values than the original features do. As a result, the hybrid model outperformed its classical counterpart and achieved consistent performance comparable to traditional ML models with the same reduced feature set on both a noiseless simulator and real quantum hardware, highlighting the potential of quantum machine learning for accurate and efficient PA predictions. Computer science ; Electronic circuit ; Quantum ; Proton ; Proton affinity ; Machine learning ; Physics ; Nuclear physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xinyi Jiang | Xi Shen | Ruizhe Gao | Jing Rao | Baihui Xing | Zhengli Hua
Source: International Journal of Hydrogen Energy (article, 2025-12-02)
Field: Materials Science — Metals and Alloys

Investigation of hydrogen-induced crack acoustic emission response law of 304 austenitic stainless steel at different temperatures Acoustic emission ; Austenitic stainless steel ; Short-time Fourier transform ; DBSCAN ; Fracture (geology) ; Fourier transform ; Feature extraction ; Amplitude ; Cluster analysis Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics
Authors: Beihan Chen | Zilong Hua | Jennifer K. Watkins | Linu Malakkal | Marat Khafizov | David H. Hurley | Miaomiao Jin
Source: Journal of Applied Physics (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for predicting the thermal properties of uranium nitride We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential framework. The MLIP was trained on density functional theory (DFT) data and validated against various quantities including energies, forces, elastic constants, phonon dispersion, and defect formation energies, achieving excellent agreement with DFT calculations, prior experimental results, and our thermal conductivity measurement. The potential was then employed in molecular dynamics simulations to predict key thermal properties such as melting point, thermal expansion, specific heat, and lattice thermal conductivity. To further assess model accuracy, we fabricated a UN sample and performed new thermal conductivity measurements representative of single-crystal properties, which showed strong agreement with the MLIP predictions. This work confirms the reliability and predictive capability of the developed potential for determining the thermal properties of UN. Thermal conductivity ; Interatomic potential ; Phonon ; Thermal ; Density functional theory ; Molecular dynamics ; Lattice (music) Nuclear Materials and Properties ; Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Qiyu wang | Zhenggang Xiao | Chen Fan
Source: Journal of environmental chemical engineering (article, 2025-08-06)
Field: Chemistry — Inorganic Chemistry

Machine Learning-powered prediction of multi-pollutant (perfluoroalkyl substances and antibiotics) adsorption onto metal-organic frameworks Adsorption ; Metal-organic framework ; Pollutant ; Environmental chemistry ; Chemistry ; Metal ; Environmental science ; Chemical engineering ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials ; Covalent Organic Framework Applications
Authors: Bin Zhao | Haitao Liu | Yuying Hao | Min Zhao | Kai‐Ge Zhou
Source: ACS Applied Materials & Interfaces (review, 2025-09-25)
Field: Materials Science — Materials Chemistry

Emerging Selective Ion Transport via 2D Confined Space for the Innovations in Separation and Energy Technologies Ion transport technology is pivotal in energy and environmental applications, yet traditional bulk materials, such as ion exchange membranes (IEMs) and porous ceramics, face limitations in selectivity, mechanical stability, and adaptability under dynamic conditions. Two-dimensional (2D) materials, including graphene oxide (GO), MXene, and covalent organic frameworks (COFs), offer transformative potential due to their tunable nanochannels, surface chemistry, and confinement effects. However, challenges persist in long-term structural stability, selectivity decay in complex environments, and scalable fabrication, alongside an insufficient mechanistic understanding of ion-material interactions under nanoconfinement. This review systematically analyzes ion transport mechanisms in 2D nanochannels, focusing on material design strategies (e.g., layer spacing regulation, heterostructures, and external field modulation), performance optimization, and applications in membrane separation, osmotic energy harvesting, energy storage, and sensing. Key findings reveal that 2D materials enhance ion selectivity via size exclusion, charge regulation, and solvation modification, while photothermal synergy, voltage-controlled pores, and dynamic hydrogen-bond networks enable breakthroughs in efficiency and adaptability. For instance, MXene/metal-organic framework (MOF) composites achieve osmotic power densities up to 8.29 W/m<sup>2</sup> and COF membranes attain Li<sup>+</sup>/Mg<sup>2+</sup> selectivity of 190. The integration of machine learning and advanced simulations is highlighted for future mechanistic exploration. This work provides critical insights into designing high-performance, intelligent ion transport systems, bridging fundamental research with practical applications in desalination, energy conversion, and biomedicine, thereby advancing the development of next-generation membrane technologies. Graphene ; Membrane ; Ion ; Ion transporter ; Energy transformation ; Transport phenomena ; Solvation ; Oxide MXene and MAX Phase Materials ; Membrane Separation Technologies ; Nanopore and Nanochannel Transport Studies
Authors: Jiewen Liu | Nan Gao | Yongming Sui | Susu Duan | Kaixiang Jin | Shunxin Li | Bo Zou
Source: Advanced Materials (article, 2025-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

In Situ Repair and Reconstruction of Copper Surface Enhanced Its Anti‐Oxidation Properties and Stability for Deep Learning‐Powered Anti‐Counterfeiting Labels The in situ repair of oxidized copper (Cu) surfaces while constructing a superior protective layer is critical for sustainable development and the efficient utilization of metallic materials. Here, a simple solvothermal treatment is presented to repair oxidized Cu surfaces (Cu foils, nanowires, and nanocubes) and reconstruct an antioxidant layer with an ordered (111) crystal-plane (Cu-SC) in situ. Electrochemical measurements reveal that the corrosion rate of Cu-SC in 0.1 m NaOH is reduced to 1.99 × 10<sup>-3</sup> mm yr<sup>-</sup>¹, a fivefold improvement over pristine Cu (1.00 × 10<sup>-</sup> <sup>2</sup> mm yr<sup>-</sup>¹). Density functional theory calculations confirm that the reconstructed (111) surface reduces oxygen molecule adsorption, significantly hinders oxygen atom diffusion into the bulk and continuous adsorption on surface. Anti-counterfeiting labels fabricated from Cu-SC nanowires exhibit exceptional durability, retaining reliable authentication accuracy after 144 h at 85 °C/85% relative humidity and 2000 bending cycles. The enhanced anti-oxidation properties of Cu-SC ensure the stability of its microstructures, which are critical for deep learning-based authentication, allowing precise feature extraction and accurate label verification even under extreme conditions. These results highlight the potential of (111) surface reconstruction for enhancing material stability, enabling advanced anti-counterfeiting applications, and promoting the sustainable utilization of metallic materials. Materials science ; Adsorption ; Copper ; Microstructure ; Chemical engineering ; Corrosion ; Nanowire ; Density functional theory ; Layer (electronics) ; Metal ; In situ ; Nanotechnology ; Durability ; Relative humidity ; Electrochemistry ; Composite material ; Metallurgy ; Physical chemistry ; Organic chemistry ; Electrode ; Computational chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanoporous metals and alloys ; Advanced Memory and Neural Computing
Authors: Zhiwei Zheng | Xuezheng Yue | Hao Wang
Source: Journal of Material Science and Technology (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Model distillation driven machine learning for automatic microstructure detection and segmentation Distillation ; Segmentation ; Artificial intelligence ; Microstructure ; Computer science ; Machine learning ; Process engineering ; Materials science ; Engineering ; Chromatography ; Chemistry ; Metallurgy Machine Learning in Materials Science ; Mineral Processing and Grinding ; Industrial Vision Systems and Defect Detection
Authors: Venkata Dinesh Avvari | P.S. Rama Sreekanth | Mohammad Hassan Shahavi | Robert Olejník | Nadir Ayrılmış | Ragavanantham Shanmugam
Source: Materials Chemistry and Physics (article, 2025-09-30)
Field: Materials Science — Biomaterials

Advances in electrospun nanofibrous yarns: Improved functionality and emerging applications Electrospinning ; Nanofiber ; Yarn ; Process (computing) ; Fabrication ; Transformative learning ; Nanoscopic scale Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Peter R. Spackman
Source: Crystal Growth & Design (article, 2025-05-20)
Field: Chemistry — Organic Chemistry

Switching Perspectives on Hirshfeld Partitioning Schemes We explore the foundations and applications of Hirshfeld partitioning, a fundamental technique used in the analysis of atoms in molecules, and the related Hirshfeld surface used to study molecules in crystals. Hirshfeld partitioning has direct connections to information theory, machine learning, and statistical mechanics, and this work aims to deepen our understanding of why these techniques are successful and so generally applicable, highlighting the connections of such methods to our physical understanding of the systems to which they are applied. We further investigate practical considerations in the implementation and interpretation of Hirshfeld surfaces and Voronoi partitioning. Chemistry ; Computer science ; Crystallography ; Materials science Coordination Chemistry and Organometallics ; Synthesis and characterization of novel inorganic/organometallic compounds ; Organometallic Complex Synthesis and Catalysis
Authors: Kunal Ghosh | Milica Todorović | Aki Vehtari | Patrick Rinke
Source: The Journal of Chemical Physics (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Active learning of molecular data for task-specific objectives Active learning (AL) has shown promise to be a particularly data-efficient machine learning approach. Yet, its performance depends on the application, and it is not clear when AL practitioners can expect computational savings. Here, we carry out a systematic AL performance assessment for three diverse molecular datasets and two common scientific tasks: compiling compact, informative datasets and targeted molecular searches. We implemented AL with Gaussian processes (GP) and used the many-body tensor as molecular representation. For the first task, we tested different data acquisition strategies, batch sizes, and GP noise settings. AL was insensitive to the acquisition batch size, and we observed the best AL performance for the acquisition strategy that combines uncertainty reduction with clustering to promote diversity. However, for optimal GP noise settings, AL did not outperform the randomized selection of data points. Conversely, for targeted searches, AL outperformed random sampling and achieved data savings of up to 64%. Our analysis provides insight into this task-specific performance difference in terms of target distributions and data collection strategies. We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal. Computer science ; Task (project management) ; Machine learning ; Cluster analysis ; Data mining ; Sampling (signal processing) ; Noise (video) ; Artificial intelligence ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics