jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Love Edet M. | Moyo Mthabisi
Source:  (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

AI–Powered Biochemical Materials for Biomedical Applications: Current Trends and Future Prospects Traditional approaches to designing biochemical materials for biomedical use are often slow, labor-intensive, and constrained by the complexity of biological systems. This creates a critical gap between experimental material development and clinical translation. Artificial Intelligence (AI) now offers transformative solutions to this challenge by enabling predictive modeling, generative design, and rapid screening of novel biomaterials with tailored properties. As of 2025, AI is actively being used to design adaptive hydrogels, simulate protein-material interactions, and develop patient-specific drug delivery systems. Platforms like AlphaFold and generative neural networks are driving forward the rational design of bioactive materials, while AI-integrated high-throughput screening pipelines are drastically shortening the material discovery timeline. These innovations are accelerating healthcare solutions and bridging the translational gap. Bridging (networking) ; Generative grammar ; Transformative learning ; Biocompatible material ; Biomimetic materials ; Health care Machine Learning in Materials Science ; 3D Printing in Biomedical Research ; Hydrogels: synthesis, properties, applications
Authors: Qianxu Yang | Shenhong Lei | Wei Zhang | Baokun Zhu | Mingcong Lv | Xiao-Peng Yang
Source:  (article, 2025-03-07)
Field: Chemistry — Analytical Chemistry

A Distance-Based Multi-Model Fusion Method for Leaf Group Formula Raw Material Substitution Recommendation in Yunnan Cigarette Products This research aims to meet the practical needs of leaf group formula maintenance in cigarette production by utilizing machine learning to enhance the scientific and timely use of raw materials. A novel raw material substitution recommendation algorithm based on distance multi-model fusion is proposed. The algorithm comprises five spatial distance algorithm units and a voting decision mechanism, which together form a complete recommendation index mapping method. The results demonstrate that this method provides stable recommendation capabilities and offers a novel, efficient approach for formula maintenance personnel. Substitution (logic) ; Raw material ; Fusion ; Computer science ; Group (periodic table) ; Process engineering ; Pulp and paper industry ; Chemistry ; Engineering ; Organic chemistry Spectroscopy and Chemometric Analyses ; Rough Sets and Fuzzy Logic ; Food Quality and Safety Studies
Authors: Yunyun Liu | Heng Zhang | Rui Cao | Huijiang Gao | Qiang Yu | Meng An
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable Machine Learning for Optimizing the Specific Capacitance of Biomass-Derived Supercapacitors Random forest ; Overfitting ; Gradient boosting ; Boosting (machine learning) ; Regression ; Categorical variable ; Decision tree ; Hyperparameter ; Capacitance Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Thermochemical Biomass Conversion Processes
Authors: N. D. Rekha | Aswatha Munipapanna | N. Santhosh | Channa Keshava Naik N. | Venkatesh T. Lamani | Sarfaraz Kamangar | Saiful Islam | Addisu Frinjo Emma
Source: Scientific Reports (article, 2025-11-18)
Field: Materials Science — Polymers and Plastics

Energy absorption and damage prediction in natural fibre composites under low velocity impact using machine learning and FEA This study investigates the energy absorption and damage prediction of banana fiber composite laminates under low-velocity impact using a combination of experimental testing, finite element analysis (FEA), and machine learning (ML). Banana fiber composites are a promising eco-friendly alternative to synthetic materials in structural applications due to their sustainability, high strength, and energy absorption properties. The laminates, fabricated using the hand layup technique, were subjected to low-velocity impact tests to measure their energy absorption, force-displacement behavior, and damage progression. FEA simulations were conducted to model the impact response, and ML models, including logistic regression and Naive Bayes, were developed to predict the impact behavior. The results show that banana fiber composites exhibit significant energy absorption, with an experimental value of 14.36 kJ at a drop height of 1.8 m. Both FEA and ML models closely predicted this energy absorption, with minor deviations, validating the robustness of the methodologies. The study highlights the integration of ML as a powerful tool for predicting composite material behavior, achieving an accuracy of 1.0 in predicting energy absorption and damage initiation. The findings provide valuable insights into the potential of banana fiber composites for use in lightweight, high-strength materials for the automotive and aerospace industries. Finite element method ; Composite number ; Robustness (evolution) ; Natural fiber ; Fiber-reinforced composite ; Fiber ; Response surface methodology ; Energy (signal processing) Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Cellular and Composite Structures
Authors: Kai Wu | Chengli Hao | Wei Guo | Zhiwei Li | Decong Zheng
Source: Foods (article, 2025-11-13)
Field: Chemistry — Analytical Chemistry

Dual-Sensor Hyperspectral Fusion for Prediction of Sorghum Tannin Content Oriented to Liquor Brewing To address the demand for precise sorghum tannin control in liquor brewing, and to overcome the inefficiency and high cost of traditional methods, this study developed a non-destructive approach by fusing features from dual hyperspectral sensors. Based on 240 representative sorghum samples covering different varieties and production regions, visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data were sequentially collected, and the tannin content was determined using standard chemical methods as reference values. Using the competitive adaptive reweighted sampling (CARS) method, characteristic wavelength bands were extracted and fused feature subsets were constructed. Combined with partial least squares (PLS), support vector machine (SVM), and convolutional neural network (CNN) algorithms, the performance of models built from both full-data concatenation and feature fusion of VNIR and SWIR data was systematically compared. The results demonstrated that the feature-based models exhibited superior performance to the full-spectrum models, while the model incorporating dual-sensor feature fusion achieved the best overall results. The fused-feature-CNN model achieved the optimal prediction performance, with values of 0.83 for coefficient of determination for the prediction set (R<sub>P</sub><sup>2</sup>), 0.29 for root mean squared error for the prediction set (RMSE<sub>P</sub>), and 2.42 for residual predictive deviation for the prediction set (RPD<sub>P</sub>). This study confirms that the integration of multi-sensor feature fusion with deep learning strategies can provide an effective technical pathway for the rapid, non-destructive detection of sorghum tannin content and the development of online sorting equipment. Hyperspectral imaging ; VNIR ; Partial least squares regression ; Pattern recognition (psychology) ; Outlier ; Sorghum ; Fusion ; Feature (linguistics) ; Residual Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Vahid Daghigh | Hamid Daghigh
Source: International Journal of Applied Ceramic Technology (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Bayesian probabilistic machine learning analysis of ceramic‐coated ultra‐high‐temperature carbon/carbon composites Abstract Regulatory agencies and key stakeholders are increasingly promoting the use of probabilistic approaches in design processes for large corporations. This shift is particularly emphasized in analyzing mechanical properties, such as fatigue and failure prediction. Additionally, the use of probabilistic artificial intelligence represents a transformative advancement in material science that leads to enhanced predictive accuracy and robust decision‐making capabilities. These artificial intelligence methods enable more informed decision‐making in the design and evaluation of advanced materials by quantifying uncertainty and offering probabilistic assessments, particularly for applications involving extreme environments. High‐temperature materials, such as carbon/carbon (C/C) composites, are essential for modern technological applications. However, their vulnerability to oxidation poses a significant barrier, indicating the necessity for effective protective coatings. The application of these coatings to C/C composites is complex and has hindered their widespread use in high‐temperature settings. In this study, we utilize finite element analysis (FEA) and machine learning (ML) combined with Bayesian probability to examine the behavior of silicon carbide ceramic‐coated cubic C/C composites. The investigation focuses on how stress and strain evolve under varying thermal conditions and cyclic thermal loading from a probabilistic perspective. This work integrates FEA and Bayesian probabilistic‐based ML to enhance the predictive power for evaluating ultra‐high‐temperature materials. Probabilistic logic ; Finite element method ; Transformative learning ; Vulnerability (computing) ; Bayesian probability ; Probabilistic design ; Statistical model ; Bayesian network Machine Learning in Materials Science ; Thermal properties of materials ; Advanced ceramic materials synthesis
Authors: Siyu Yao | Tong Yu | Alessandra Fantina Victorio Ramos | Zhongkun Zhang | Zulipikaer Rouzi | Luis Rodriguez‐Saona
Source: Food Chemistry X (review, 2025-09-09)
Field: Chemistry — Analytical Chemistry

Toward smart and in-situ mycotoxin detection in food via vibrational spectroscopy and machine learning Recent advances in vibrational spectroscopy combined with machine learning are enabling smart and <i>in-situ</i> detection of mycotoxins in complex food matrices. Infrared and spontaneous Raman spectroscopy detect molecular vibrations or compositional changes in host matrices, capturing direct or indirect mycotoxin fingerprints, while surface-enhanced Raman spectroscopy (SERs) amplifies characteristic mycotoxins molecular vibrations via plasmonic nanostructures, enabling ultra-sensitive detection. Machine learning further enhances analysis by extracting subtle and unique mycotoxin spectral features from information-rich spectra, suppressing noise, and enabling robust predictions across heterogeneous samples. This review critically examines recent sensing strategies, model development, application performance, non-destructive screening, and potential application challenges, highlighting strengths and limitations relative to conventional methods. Innovations in portable, miniaturized spectrometers integrated with cloud computation are also discussed, supporting scalable, rapid, and on-site mycotoxin monitoring. By integrating state-of-art vibrational fingerprints with computational analysis, these approaches provide a pathway toward sensitive, smart, and field-deployable mycotoxin detection in food. Mycotoxin ; Raman spectroscopy ; Spectroscopy ; Plasmon ; Hyperspectral imaging ; Infrared spectroscopy Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Identification and Quantification in Food
Authors: Joohwi Lee | Katsutoshi Miyamoto
Source: ArXiv.org (preprint, 2025-03-06)
Field: Materials Science — Materials Chemistry

Accurate predictive model of band gap with selected important features based on explainable machine learning In the rapidly advancing field of materials informatics, nonlinear machine learning models have demonstrated exceptional predictive capabilities for material properties. However, their black-box nature limits interpretability, and they may incorporate features that do not contribute to, or even deteriorate, model performance. This study employs explainable ML (XML) techniques, including permutation feature importance and the SHapley Additive exPlanation, applied to a pristine support vector regression model designed to predict band gaps at the GW level using 18 input features. Guided by XML-derived individual feature importance, a simple framework is proposed to construct reduced-feature predictive models. Model evaluations indicate that an XML-guided compact model, consisting of the top five features, achieves comparable accuracy to the pristine model on in-domain datasets (0.254 vs. 0.247 eV) while demonstrating superior generalization with lower prediction errors on out-of-domain data (0.461 vs. 0.341 eV). Additionally, the study underscores the necessity for eliminating strongly correlated features (correlation coefficient greater than 0.8) to prevent misinterpretation and overestimation of feature importance before applying XML. This study highlights XML's effectiveness in developing simplified yet highly accurate machine learning models by clarifying feature roles, thereby reducing computational costs for feature acquisition and enhancing model trustworthiness for materials discovery. Feature (linguistics) ; Generalization ; Support vector machine ; Construct (python library) ; Field (mathematics) ; Predictive modelling ; Permutation (music) ; Feature model ; Trustworthiness Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Seoyoung Park | Minsuk Koo | Sungjun Kim
Source: Journal of Physics D Applied Physics (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Enhancing non-volatile memory and neuromorphic computing: integration of PRAM and OTS for scalable, energy-efficient architectures Abstract This paper investigates the integration of phase-change random access memory (PRAM) and ovonic threshold switch (OTS) devices, emphasizing their ability to advance non-volatile memory technologies, neuromorphic computing architectures, and energy-efficient systems. OTS devices’ nonlinear threshold switching effectively mitigates sneak currents in high-density crossbar arrays, while challenges like resistivity drift and structural relaxation are addressed through advanced modeling and experimental analysis. The study highlights innovations in phase-change materials, such as doped Sb 2 Te 3 alloys and Sb 2 Te 3 –GeTe superlattices deposited by magnetron sputtering, which have been reported to improve thermal stability, reduce RESET power, and enhance cycling endurance compared with conventional GST-based phase-change memory devices. PRAM-OTS hybrid systems demonstrate exceptional performance in spiking and multi-layer neural networks, replicating neuronal behaviors such as integrate-and-fire dynamics and spike-timing-dependent plasticity for low-latency, energy-efficient processing in artificial intelligence, robotics, and internet of things applications. These scalable and reliable systems provide a robust framework for next-generation high-performance computational platforms, addressing key challenges in scalability, energy efficiency, and operational longevity. Neuromorphic engineering ; Crossbar switch ; Scalability ; Memristor ; Reset (finance) ; Artificial neural network ; Random access memory ; Resistive random-access memory Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Quirin Göttl | Natalie Rosen | Jakob Burger
Source: ArXiv.org (preprint, 2025-02-13)
Field: Chemistry — Physical and Theoretical Chemistry

Convex envelope method for T, p flash calculations for mixtures with an arbitrary number of components and arbitrary aggregate states $T, p$ flash calculations determine the correct number of phases at phase equilibrium and their compositions for fixed temperature and pressure. They are essential for chemical process simulation and optimization. The convex envelope method (CEM) is an existing approach that employs the tangent plane criterion to determine liquid phase equilibria for mixtures with an arbitrary number of components without providing the number of phases beforehand. This work extends the CEM to include also vapor and solid phases. Thus, any phase equilibrium of a given mixture with an arbitrary number of components and phases can be calculated over the whole composition space. The CEM results are presented for various vapor-liquid and solid-liquid phase equilibria examples of up to four components. We show how the CEM can be used for parameter fitting of $g^E$-models. As an outlook, we demonstrate how the CEM can be combined with a machine learning-based tool for property prediction to construct phase equilibria. Envelope (radar) ; Aggregate (composite) ; Flash (photography) ; Regular polygon ; Mathematics ; Component (thermodynamics) ; Combinatorics ; Computer science ; Physics ; Materials science ; Geometry ; Thermodynamics ; Nanotechnology ; Telecommunications ; Optics Advanced Physical and Chemical Molecular Interactions ; Process Optimization and Integration ; Graph theory and applications
Authors: Osman Mamun | Chenlu Yang | Shuwen Yue
Source: ChemRxiv (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

Deep Graph Kernel Learning for Material and Atomic Level Uncertainty Quantification in Adsorption Energy Prediction For high-throughput catalytic material discovery, Graph Neural Networks (GNNs) provide an efficient method for predicting the adsorption energies of adsorbates on transition metal surfaces. While GNNs perform well on in-domain prediction tasks, they often struggle to generalize to out-of-domain scenarios. This limitation necessitates a robust method for quantifying prediction uncertainty to enable informed catalyst discovery. Gaussian Processes (GPs) offer a principled approach to uncertainty quantification within a Bayesian framework. However, traditional implementations suffer from key challenges, including cubic time complexity, high memory requirements, and an inability to learn meaningful representations from graph structures. To address these issues, we introduce Deep Graph Kernel Learning (DGKL), a scalable framework that integrates a GNN backbone with sparse variational Gaussian Processes (SVGP) for uncertainty quantification in adsorption energy prediction. We benchmark DGKL against state-of-the-art methods such as ensemble/query-by-committee and Monte Carlo dropout, using both ranking-based metrics (e.g., Spearman's rank correlation, negative log-likelihood, miscalibration area) and error-based metrics (e.g., RMSE vs. RMV and error vs. standard deviation plots). DGKL consistently outperforms existing methods across all evaluation metrics while maintaining computational efficiency and scalability. For example, the correlation coefficient between RMSE and RMV for DGKL ranges from 0.98 to 1.00, slightly exceeding the next best method (ensemble learning). More significantly, the expected normalized calibration error (ENCE) for DGKL ranges from 0.06 to 0.15 across different datasets and GNN backbones, while the ensemble method exhibits a wider range of 0.36 to 1.55. DGKL can be incorporated into an active learning framework to iteratively explore catalytic material space, guiding the discovery of novel active catalysts. Additionally, we propose a variation of DGKL capable of predicting atomic-level uncertainty, a feature absent in existing methods. This enables fine-grained insights into out-of-domain data and provides a pathway for enhancing predictive model performance. Adsorption ; Kernel (algebra) ; Graph ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Machine learning ; Materials science ; Chemistry ; Theoretical computer science ; Mathematics ; Statistics ; Physical chemistry ; Discrete mathematics Machine Learning in Materials Science
Authors: Yiji Chen | Ruimei Wu | Huaiying Yao | Li Li | Aihua Xiong | Li Jiang | Zhongshou Liu | Shirong Ai | Limin Lu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Electrochemistry

Machine Learning Combined with the Distribution Optimization Strategy for Highly Efficient Simultaneous Analysis of Cu2+ and Hg2+ in Water Based on S-Tio2/Mxene Electrochemical Platform Electrochemistry ; Computer science ; Distribution (mathematics) ; Chemistry ; Biological system ; Mathematics ; Electrode ; Physical chemistry Electrochemical Analysis and Applications
Authors: Jinming Zhu | Kuo Bao | Zhaoqing Wang | Yuan Qin | H. Yu | Tian Cui
Source: Matter and Radiation at Extremes (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Pressure-induced high-energy-density and superhard Be–C–O compounds Light element compounds under high pressure display intriguing properties and applications, owing to their diverse bonding patterns and crystalline structures. However, the system of ternary Be–C–O compounds under high pressure, as the lightest representative of the IIA–IVA–VIA family, remains largely unexplored. Using a machine-learning-accelerated crystal structure search and first-principles calculations, Be–C–O phase diagrams are investigated at pressures ranging from 0 to 100 GPa. Four ternary compounds are proposed to be stable at corresponding pressures: BeCO3, Be2CO4, Be2C4O3, and BeC4O2. Analyses of electronic structure and chemical bonding further reveal how the structural diversity of these compounds is induced. Remarkably, Be2C4O3 and BeC4O2 are recoverable to ambient conditions and possess both high energy density and high hardness. The volumetric energy densities of Be2C4O3 and BeC4O2 could approach 9.03 and 7.94 kJ/cm3, respectively. The Vickers hardnesses of Be2C4O3 and BeC4O2 are found to be close to 39.58 and 51.57 GPa, respectively. These findings demonstrate the structural and functional diversity of Be–C–O compounds under high pressure, providing guidance for further exploration of the IIA–IVA–VIA compounds. Ternary operation ; Crystal structure ; Phase diagram ; Vickers hardness test ; High pressure ; Chemical bond ; Phase (matter) ; Density functional theory Boron and Carbon Nanomaterials Research ; High-pressure geophysics and materials ; MXene and MAX Phase Materials
Authors: Shinto
Mundackal Francis (22783598) | Andrew Ferebee (22783601) | Sajib Kumar Mohonta (22783604) | Adeep Sen (22783607) | Pooja Puneet (22783610) | Yi Ding (172783) | Ramakrishna Podila (456098)
Source: Figshare (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Physics-InformedMachine Learning for the Predictionof Thermal Conductivity from IR Images Polymer–composite thermal interface materials (PC-TIMs) are essential for heat dissipation in electronics and energy storage systems, yet their cross-plane thermal conductivity (κ) is difficult to measure rapidly with conventional techniques. Here, we present a physics-driven machine learning (ML) framework that integrates infrared (IR) thermography with feature-engineered models to predict κ in the low-conductivity regime (<5 W m<sup>1–</sup> K<sup>–1</sup>). More than 200 thermal images, obtained from experiments and Multiphysics simulations, were converted into universal temperature fields and transformed into structured feature vectors encoding gradients, Laplacian variance, and thermal extrema. Random forest regressors achieved robust predictions (<i>R</i><sup>2</sup> = 0.905, MAE = 0.169) on experimental test sets, outperforming linear and boosting models. Domain adaptation via Gaussian-perturbed simulations enhanced transferability, while SHapley Additive exPlanations or SHAP analysis confirmed the physical relevance of density and gradient features. This work establishes IR thermography coupled with interpretable ML as a rapid and scalable diagnostic for PC-TIM development and manufacturing quality control. Thermography ; Thermal conductivity ; Support vector machine ; Thermal ; Boosting (machine learning) ; Temperature measurement ; Measure (data warehouse) ; Infrared ; Feature selection ; Robustness (evolution) Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Machine Learning in Materials Science