jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Nauman Khan | M. H. Eisa | Xiaohui Sun | Muffarih Shah | Suleiman M. Suleiman | Abdul Hameed | Ilyas Khan | Xiangsheng Chen | Nasir A. Ibrahim | Ashraf A. Qurtam | Zainuriah Hassan | Katabathini Narasimharao | Asmiet Ramizy
Source: Fuel (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Bismuth nanostructures: catalytic pathways to sustainable technologies • Systematic classification of Bi nanostructures with operando insights into dynamic catalytic surfaces. • Performance-driven design using defects, morphology, strain, and advanced heterostructures. • Unified structure–property–performance links between active sites and catalytic metrics. • Green, scalable, ML-accelerated synthesis of reactor-ready bismuth catalysts. Bismuth (Bi)-based nanostructures have emerged as pivotal candidates for sustainable energy conversion and environmental remediation due to their unique electronic configurations, semimetal to semiconductor transitions and earth abundance. However, translating laboratory into practical technologies is currently hindered by a fragmented understanding of dynamic active sites and the lack of a unified structure-performance framework. To bridge this gap, this review provides a comprehensive synthesis of the state of the art, which classifies the full spectrum of Bi-based nanostructures, including metallic Bi, oxides, oxyhalides, chalcogenides and perovskites. The novelty of this review is the integration of advanced operando and multimodal characterization analysis, which resolves the revolution of the dynamic surfaces under bias and light. We examine performance-centric design principles that couple strain and defect engineering with compositional tuning and rational heterostructure construction. These strategies are critically assessed across applications such as CO 2 reduction, hydrogen evolution, nitrogen fixation, and organic pollution degradation, explicitly linking active site motifs to kinetic descriptors. Furthermore, we outline scalable, green synthesis pathways and highlight the often overlooked but accelerating role of machine learning for discovery and uncertainty-aware benchmarking. Collectively, this work establishes a coherent roadmap for the rational design of next-generation catalysts, identifying decisive experience needed to translate fundamental advances into sustainable industrial technologies. Rational design ; Bismuth ; Heterojunction ; Characterization (materials science) ; Sustainable design ; Environmental pollution ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: A. Tripathi
Source: Open MIND (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Developing Quantum-Physics Inspired Machine Learning Model for the Partition Coefficient Prediction and Comparing its Performance with the Empirical Descriptors and Molecular Graph-based Models Supplementary Information for logP Prediction. It has dataset, figures, tables, and gnn code. Partition (number theory) ; Pattern recognition (psychology) ; Support vector machine ; Empirical modelling ; Training set ; Predictive modelling Machine Learning in Materials Science ; Big Data and Digital Economy ; Advanced Graph Neural Networks
Authors: Sanober Gul | Hassanien Gomaa | Maryam Batool | De-Ming Kong | Lina Zhu
Source: Journal of environmental chemical engineering (article, 2026-03-22)
Field: Chemistry — Inorganic Chemistry

A comprehensive review on advanced zirconium-based metal-organic frameworks: Rational design, critical properties, and versatile adsorptive applications in wastewater remediation Environmental remediation ; Wastewater ; Sewage treatment ; Groundwater remediation ; Human decontamination Metal-Organic Frameworks: Synthesis and Applications ; Advanced oxidation water treatment ; Covalent Organic Framework Applications
Authors: Yifan Sun | Hironobu Nakamura | Masaya Kumagai | Yuji Ohishi | Ken Kurosaki
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions Uranium dioxide has been widely used as a nuclear fuel in commercial light water reactors due to its high uranium density and chemical stability. However, its relatively low thermal conductivity is not optimal from the viewpoints of fuel integrity and safety margins, particularly during loss-of-coolant accidents. Although the development of accident-tolerant fuels with higher thermal conductivity is strongly desired, many potential uranium compounds remain unexplored due to constraints associated with handling radioactive materials. To efficiently screen promising uranium compounds with high thermal conductivity, past studies have leveraged machine-learning models to accelerate the discovery process. In this study, we experimentally examine the model's predictions by fabricating UCo and measuring its high-temperature thermophysical properties. Our results show that the thermal conductivity of UCo predicted by machine learning is in good agreement with the experimental measurements. Despite slight discrepancies, additional SHAP analysis suggests that the model's decision logic is consistent with known physical trends. Overall, this study fills a gap in reported thermophysical properties of UCo and provides experimental support for machine-learning-assisted screening of uranium compounds relevant to advanced fuel development. Thermal conductivity ; Uranium ; Uranium dioxide ; Nuclear fuel ; SPARK (programming language) ; Support vector machine ; Thermal Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Yongjun Huo | Jiaqi Song | Wenqian Li | J. Y. Zhang | Yujin Zhang | Yang Fu | Wangchao Yuan | Xin Chen | Sichen Liu | Miao Jiang | Yuan Cheng | Gang Zhang
Source: Advanced Science (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Emerging Advanced Electronic Packaging Materials for Thermal Management in Power Electronics Current research on integrated circuits and power electronics is rapidly advancing toward miniaturization, high power density, and multi-chip integration, which presents unprecedented challenges to the thermal management performance of packaging materials. Along the device-to-sink heat-flow path in power modules, thermal management relies primarily on two functional material systems: substrate materials that provide mechanical support and electrical insulation, and thermal interface materials (TIMs) that bridge heat transfer across heterogeneous interfaces. This paper summarizes recent advances in thermal management materials for power electronics, with a focus on ceramic-based substrate systems, particularly Si<sub>3</sub>N<sub>4</sub> ceramics, and TIM systems including conductive adhesives, diamond-reinforced composites, and 2D filler-reinforced polymer composites. Emphasis is placed on improvements in thermal conductivity, reduction of thermal resistance, and enhancement of mechanical reliability through process optimization, interfacial engineering, and hybrid filler design. In addition, representative multiscale simulation approaches and emerging applications of artificial intelligence and machine learning are reviewed as tools for understanding interfacial heat transport and accelerating materials screening and optimization. Finally, key challenges and future directions toward scalable, reliable, and intelligent thermal management solutions are discussed, providing guidance for both academic research and industrial deployment in next-generation power-electronics packaging. Thermal management of electronic devices and systems ; Electronics ; Thermal grease ; Reliability (semiconductor) ; Power electronics ; Bridge (graph theory) ; Power module ; Flexible electronics ; Software deployment Thermal properties of materials ; Silicon Carbide Semiconductor Technologies ; Advanced ceramic materials synthesis
Authors: Tomás Silva | Victor Sousa Lobo | João Pereira-da-Silva | Filipe Ferreira da Silva
Source: ACS Omega (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Dissociative Electron Attachment Prediction of Halogenated Organic Molecules Using Machine Learning Organic molecules ; Molecule ; Dissociative ; Artificial neural network ; Pattern recognition (psychology) ; Electron Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Z. Li | Sandro Scandolo
Source: The Journal of Chemical Physics (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

A physics-informed long-range polarizable potential based on deep learning Machine-learning-based interatomic potentials are widely employed in atomistic simulations, but they struggle to capture long-range electrostatic correlations, which are ubiquitous in polar and biomolecular systems. We present a physics-informed machine-learning interatomic potential that incorporates long-range electrostatic interactions through a polarizable framework. Our model combines two equivariant message-passing neural networks: one for short-range interactions and the other for environment-dependent atomic dipoles. The model is trained not only on energies and forces but also on Born effective-charge tensors, enabling accurate predictions of field-induced properties such as infrared absorption spectra and LO-TO phonon splittings. We validate the method on ionic solids (NaCl), liquid water, and halide perovskites (MAPbI3), demonstrating improved modeling of long-range polarization effects while maintaining competitive accuracy in energy and force predictions. Our results highlight the necessity of explicit long-range electrostatics for capturing collective phenomena in insulating and polar materials. Polarizability ; Electrostatics ; Polar ; Polarization (electrochemistry) ; Phonon ; Ionic bonding ; Interatomic potential ; Electric potential energy ; Infrared Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum many-body systems
Authors: Lisa Sahlmann | Nourhan Abdelrahman | Mats Meeusen | Koen Delaere | Peter Meuris | Bart Van Den Bossche | Natalia Konchakova | Herman Terryn | Mesfin Haile Mamme | Christian Feiler | Mikhail L. Zheludkevich
Source: npj Materials Degradation (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Surrogate modelling of corrosion inhibition finite element simulations using machine learning Abstract A modelling approach that combines a previously developed 2D continuum finite element model with machine learning to support the design and evaluation of corrosion-inhibiting coatings. The FEM simulates the leaching of corrosion inhibition pigments from an organic coating and the resulting protection of the metal surface. This is conducted for a system of aluminium alloy 2024-T3 with an active protective coating loaded with lithium carbonate particles. A generated dataset from FEM results was used to train ML models to predict inhibitor concentration and corrosion current density based on geometric and material input parameters. A feature importance analysis was conducted to identify the most influential input variables, providing insight into the factors controlling the achievement of corrosion inhibition. Furthermore, a blind test was performed using five unseen cases that were not involved in the training phase. Finally, the trained models were applied to explore their use in coating design. Finite element method ; Corrosion ; Coating ; Corrosion inhibitor ; Aluminium ; Aluminium alloy ; Feature (linguistics) Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Kati Asikainen | M. Alatalo | Marko Huttula | Assa Aravindh Sasikala Devi
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

Data-Efficient Machine learning for Predicting Dopant Formation Energies in TiO$_2$ Monolayer Machine learning models are increasingly applied in materials science, yet their predictive power is often constrained by data scarcity. Here, we show that accurate predictions can be achieved, even with a limited number of training examples, provided the dataset is compact and and grounded in physically relevant quantities. By combining density functional theory calculations with a machine-learning framework, we construct accurate descriptor-based models to predict the formation energies of doped lepidocrocite TiO$_2$ monolayers. The predictive accuracy of machine-learning models was first evaluated for single-dopant Pt configurations, demonstrating that the selected structural and chemical descriptors reliably capture the key factors governing dopant stability. Chemical transferability is then examined by extending the dataset to include Ag-doped configurations. Predictive accuracy improved systematically as additional Ag-doped data points were included in the training, while the performance of Pt remains robust. These results highlight the potential of small and well-curated datasets combined with physically informed descriptors to enable not only accurate but also chemically transferable machine-learning-driven screening in doped TiO$_2$ monolayer. Predictive power ; Dopant ; Transferability ; Predictive modelling ; Key (lock) ; Experimental data ; Density functional theory ; Training set Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Runze Zhou | Xinqiang Yuan | Chi Zhang | Longfei Zhang | Hongxing Dong | Long Zhang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Ceramics and Composites

Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks Fluoride ; Inverse ; Artificial neural network ; Refractive index ; Field (mathematics) ; Inverse problem Glass properties and applications ; Structural Analysis of Composite Materials ; Machine Learning in Materials Science
Authors: Kawaljit Randhawa
Source: Engineering Research Express (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Application of artificial intelligence in predicting and optimizing material properties Abstract Artificial intelligence (AI) has become an important and powerful tool in materials science for faster prediction of material properties, new material discovery, and improvement of processing methods. This review presents the use of AI in predicting material properties, optimizing processing parameters, analyzing microstructures, and evaluating material degradation and failure. Machine learning and deep learning techniques used to build structure–property-process relationships and to enable automatic microstructural analysis are discussed under a common AI-based workflow. Major challenges like data quality, model interpretability, and real-world implementation are critically examined, along with recent solutions like physics-informed AI and explainable AI. The main contribution of this review is a process-oriented and framework-based overview that links AI methods with material processing, microstructure development, and lifecycle performance, which makes it different from existing reviews that mainly focus on individual applications. Focus (optics) ; Deep learning ; Artificial neural network ; Material properties ; Materials processing ; Applications of artificial intelligence Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Advanced Materials Characterization Techniques
Authors: Muhammad Fadhlan Anshor | Harry Handoko Halim | Yoshitada Morikawa
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

CO Coadsorption Effects on Water-Gas Shift Reaction over Cu Clusters on Cu(111): Insights from Machine Learning Force Field and Microkinetic Modeling CO-induced Cu clustering has been observed experimentally and theoretically, yet its catalytic implications remain insufficiently understood. Here, we combine machine learning force field (MLFF) with density functional theory and microkinetic... Cluster analysis ; Density functional theory ; Force field (fiction) ; Field (mathematics) ; Catalysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Tanishq Prasad | Dev K. Patra | Debashis Kundu
Source: Multiscale Science and Engineering (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Molecular Descriptors-Based Analysis for Computation of Polyurethane Melting Temperature Partial least squares regression ; Molecular descriptor ; Normalization (sociology) ; Support vector machine ; Preprocessor ; Computation ; Experimental data ; Set (abstract data type) ; Least squares support vector machine ; Data pre-processing Machine Learning in Materials Science ; Polymer composites and self-healing ; Catalysis and Oxidation Reactions
Authors: Ranran Kou | Cong Wang | Ran Wan | Mingliang Su | Heng Qiu Xu | Yufeng Fu | Yun Lin | Xinhua Song | Yiming Bi | Liang Zhao | Junwei Guo | Hongbo Wang | Ziyi Liu | S. Yang | Cong Nie
Source: Frontiers in Chemistry (article, 2026-03-20)
Field: Chemistry — Analytical Chemistry

Rapid discrimination of geographical origin and analysis of chemical characterization of tobacco leaves from multiple countries Tobacco is a globally cultivated crop featuring distinct quality variations among leaves from different geographical origins. To develop a rapid, robust, and accurate method for multi-origin traceability, this study employed near-infrared spectroscopy combined with rapid chemical composition analysis to obtain 70 chemical components in samples from nine major tobacco-producing regions in China and four other countries (the United States, Brazil, Zimbabwe, and Zambia). One-way analysis of variance (ANOVA) and hierarchical cluster analysis (HCA) were used to investigate regional chemical differences. Discrimination models were built using a support vector machine (SVM), a backpropagation neural network, and a random forest. The best model was interpreted using permutation feature importance (PFI) to identify key markers for origin discrimination. One-way ANOVA revealed significant differences (p ≤ 0.001), and HCA demonstrated clear regional patterns. The SVM-hybrid kernel achieved the best performance with 97.96% test accuracy and macro-average recall, precision, and F1 scores of 0.9836, 0.9806, and 0.9821, respectively. The PFI algorithm was employed to identify and rank the top 20 key chemical components influencing the geographical origin discrimination. The top ten key components were Fru-Asn, succinic acid, rutin, Fru-Val, sulfate, serine, phosphate, starch, potassium, and Fru-Gly. This study integrated chemometrics, near-infrared, rapid chemical analysis, and interpretable machine learning to accurately distinguish tobacco origins, reveal regional traits, and offer insights into geographical traceability and chemical profiling. Key (lock) ; Kernel (algebra) ; Permutation (music) ; Principal component analysis ; Traceability ; Variance (accounting) ; Support vector machine ; Cluster (spacecraft) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Potato Plant Research
Authors: Tianhao Su | Shunbo Hu | Yue Wu | Runhai Oyang | Xitao Wang | Musen Li | Jeffrey R. Reimers | Tong-Yi Zhang
Source: arXiv (Cornell University) (preprint, 2026-03-19)
Field: Materials Science — Materials Chemistry

DeePAW: A universal machine learning model for orbital-free ab initio calculations Developing universal machine learning models for ab initio calculations is the frontier of materials cutting edge research in the new era of artificial intelligence. Here, we present the Deep Augment Way model (DeePAW) that is a universal machine learning (ML) model for orbital-free (OF) ab initio calculations, based on the density functional theory (DFT). DeePAW is currently the best OFDFT ML model according to the three criterions, 1) covering the largest number of elements, 2) having the widest application capability to diverse crystal structures, and 3) achieving the highest prediction accuracy without further fine-tuning. These scientific merits and innovations of DeePAW are stemmed from the novel SE(3)-equivariant double massage passing neuron networks. Besides predicting electron density distributions, DeePAW predicts formation energies of crystals as well and therefore paves an efficient avenue for multiscale materials modeling beyond conventional electronic structure calculation methods. Ab initio ; Density functional theory ; Electronic structure ; Ab initio quantum chemistry methods ; Frontier ; Enhanced Data Rates for GSM Evolution ; Deep learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Fatima Sajjad | Rashid Iftikhar | Muhammad Ali Inam | Saifullah Zadran | Salman Haider | Sahar Saleem | Humayun Nadeem | Alim-un-Nisa
Source: Next Materials (article, 2026-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-driven optimization of biochar-based supercapacitors for sustainable energy storage: Mechanisms, trends, and perspectives Global transition toward sustainable and efficient energy storage has intensified research into supercapacitors, valued for their rapid charge-discharge capability, high power density, and long operational lifespan. Among carbonaceous materials, biomass-derived biochar has emerged as a low-cost, renewable, and structurally tunable precursor for electrode design. This review provides a structured and critical synthesis of biochar-based supercapacitor strategies, systematically categorizing heteroatom doping, nanocomposite engineering, and hybrid architecture within an integrated electrochemical performance framework. Unlike existing literature that predominantly summarizes modification approaches, this review emphasizes cross-study comparability, mechanistic attribution, and device-level relevance to clarify transferable design principles. A consolidated analysis of recent machine learning (ML) applications reveals a paradigm shift from empirical optimization to data-driven predictive design. Benchmarking against reported datasets, descriptors, and model architectures, we confirm that tree-based ensemble and neural-network models (e.g., XGBoost, LightGBM, ANN) consistently achieve high predictive accuracy (R² > 0.9), while also identifying current limitations in data standardization and descriptor harmonization. The review critically evaluates how ML can move beyond correlation towards interpretable and transferable optimization under realistic device constraints. Finally, a converging research roadmap is proposed, prioritizing standardized reporting, device-level benchmarking, and constraint-aware ML integration to accelerate scalable implementation. By integrating materials engineering, data-driven modeling, and practical deployment considerations, this review establishes a comprehensive framework for advancing sustainable biochar-based supercapacitors toward real-world energy storage applications. • Biochar provides a green, low-cost carbon source for supercapacitor electrodes • Heteroatom-doped and nanocomposite biochar increase capacitance and conductivity • ML models can efficiently forecast supercapacitor performance • XGBoost and SHAP can elucidate chemistry, porosity and speciation role in biochar performance Supercapacitor ; Biochar ; Benchmarking ; Sustainability ; Standardization ; Energy storage ; Scalability ; Efficient energy use Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Machine Learning in Materials Science