jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifeng Yao | Zechuan Yi | Wei Juene Chong | Kaiyang Li | Qianli Huang | Yong Liu | Cuie Wen | Hong Wu
Source: Progress in Materials Science (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Micro-arc oxidation surface-modification of titanium alloys for biomedical applications: Mechanisms, advances, challenges, and prospects Corrosion ; Coating ; Tribology ; Microstructure ; Dopant ; Ceramic ; Titanium alloy Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Magnesium Alloys: Properties and Applications
Authors: Angelo Oñate | Juan Pablo Sanhueza | Diego Wackerling | Carlos Lanziotti | David Funes Rojas | Víctor Tuninetti | Abdul Herrim Seidou
Source: Journal of Alloys and Compounds (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Low-cost austenitic complex concentrated alloy with deformation-induced plasticity and nanoprecipitate as a strategy to enhance energy absorption at cryogenic temperature: Computational modeling and experimental validation Nanoindentation ; Austenite ; Alloy ; Plasticity ; Toughness ; Formability ; Indentation hardness ; Martensite ; Dislocation ; Phase (matter) Metal Alloys Wear and Properties ; High Entropy Alloys Studies ; Microstructure and Mechanical Properties of Steels
Authors: Zhiwen Wu | Qiuhui Deng | Xinyu Chen | Craig A. Grimes | Yu Liu | Le LI | Qingyun Cai | Deshuai Zhen
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Colorimetric Sensor for On-Site Detection of Uranyl Ions Using Spherical COFs as Photosensitive Oxidases Colorimetry ; Uranyl ; Absorbance ; RGB color model ; Detection limit ; Naked eye ; Linear range ; Nanoparticle Covalent Organic Framework Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Imran Rashid | Liqiang Gu | Nasim Ullah | Muhammad Sarfraz | Abdul Raouf | Adeel Ahmed | Muhammad Rafiq | Ikram Ullah | Bing Yu | H. Cong
Source: Materials Today Chemistry (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Advanced quantum dots-hydrogel nanocomposites: Application in the environment, energy, and health Quantum dot ; Fabrication ; Nanocomposite ; Energy transformation ; Self-healing hydrogels ; Energy storage ; Environmental remediation ; Smart material Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Pradeep Kumar Rout | Sayon Satpati | N. Senthil Kumar | Suvadip Batabyal | Dipankar Sukul | SUBHAS GHOSAL
Source: ChemistrySelect (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Predicting Corrosion Inhibition Performance of Benzothiazole Derivatives on Mild Steel in Aqueous HCl Medium Using Machine Learning Algorithms ABSTRACT Recent experimental studies suggest that various heterocyclic organic molecules have good anticorrosion properties on mild steel in aqueous HCl medium. However, screening a large number of such molecules for anticorrosion activities is experimentally challenging. To address this challenge, we have applied a Quantum Mechanics based Machine Learning (ML) approach on a set of benzothiazole (BT) derivatives to predict new molecules that might have better corrosion inhibition efficiency. To overcome the small number of BT derivatives with experimentally available data on corrosion inhibition, theoretical calculations using high‐level density functional theory and molecular dynamics simulations on structurally similar molecules with various electron‐donating and electron‐withdrawing groups were performed and added to the dataset. Using data augmentation, we first expand the dataset in order to reduce the biasness of the dataset used in different ML models. Using correlation matrix, we find that few properties such as dipole moment, Molecular surface area, energy of the Frontier molecular orbitals etc. are highly correlated and contribute maximum to the adsorption energy property. We feed these data to three distinct ML models (Tabular Neural Network, Light Gradient Boosting Method, and Random Forest Regressor) and predict new BT derivatives that might have better corrosion inhibition properties without performing experiments. Benzothiazole ; Corrosion ; Molecule ; Aqueous solution ; Density functional theory ; Corrosion inhibitor ; Dipole ; Adsorption ; Molecular descriptor Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Concrete Corrosion and Durability
Authors: Ahmad Abualasal | Zoltán Ádám Tamus
Source: Polymers (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Non-Destructive Assessment of Gamma Radiation Aging in Nuclear Cables via New Dielectric Spectroscopy Markers and Machine Learning Algorithm Low-voltage instrumentation and control (I&C) cables in nuclear power plants are continuously exposed to gamma (γ) radiation within containment areas, leading to cumulative degradation of their polymer insulation over decades of operation. Since conventional mechanical aging assessments are destructive, this study establishes a non-destructive diagnostic framework using high-frequency dielectric spectroscopy. Cable samples with ethylene propylene rubber (EPR) insulation and chlorosulfonated polyethylene (CSPE) jackets were subjected to controlled γ-irradiation at doses up to 1200 kGy. The broadband dielectric response was analyzed along with derived novel diagnostic parameters from capacitance and loss tangent spectra and a machine learning AI algorithm. The results show a strong, material-dependent relationship between radiation dose and dielectric indicators. For EPR insulation, the central capacitance (CC) and (C × F × LF) exhibit high positive sensitivity for Black and White EPR materials, respectively, whereas for CSPE jackets, the central frequency (CF) shows a pronounced monotonic decrease with the radiation exposure. These findings enable a straightforward, transparent interpretation of dielectric data and implement a new, accurate method of irradiated cables diagnosis. Dissipation factor ; Dielectric ; Capacitance ; Dielectric loss ; Ethylene propylene rubber ; Polyethylene ; Accelerated aging ; Radiation ; Cross-linked polyethylene High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Polymer Science and PVC
Authors: Oliver Xie | Bradley D. Olsen
Source: Industrial & Engineering Chemistry Research (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Learning Universal Fundamental Relations for Predicting the Phase Behavior of Liquid Mixtures Predicting properties of liquid mixtures is central to chemical engineering, yet accurate predictions from unimolecular properties have proven elusive. Activity models are a popular class of mixture models due to their parsimony yet strong correlative ability. The latest evolution in activity models is data-driven machine learning models using neural networks (NN). However, NN models require a substantial amount of training data to make their predictions, and many lack an accessible free energy function, hindering extrapolation. This work presents a joint data- and theory-driven model learning a set of universal fundamental relations (UFR) governing small molecule liquid mixtures. UFR models accurately correlate experimental infinite dilution activity coefficient data (IDAC). Trained only on IDAC data, UFR models can predict T–x–y and P–x–y vapor–liquid equilibrium curves of mixtures outside the training set. Several UFR models were also able to simultaneously predict binary liquid–liquid phase separations. Work (physics) ; Set (abstract data type) ; Binary number ; Experimental data ; Phase (matter) ; Class (philosophy) ; Activity coefficient ; Training set ; Artificial neural network Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Mehrab Mahdian | Tamás Stummer | Norman Sepsik | Ferenc Ender | Diána Balogh-Weiser | Tamás Párdy
Source: Scientific Data (article, 2026-01-03)
Field: Materials Science — Biomaterials

Cogni-e-SpinDB 1.0: Open Dataset of Electrospinning Parameter Configurations and Resultant Nanofiber Morphologies Electrospinning is a versatile technique for producing nanofibers by elongating and depositing a polymer solution in an electrostatic field. Nanofiber quality is governed by process, environmental, and solution parameters, requiring extensive fine-tuning. Optimization is largely driven by independent experimental data, yet few openly available datasets exist to support modeling or provide reference parameters for stable nanofiber formation. We present Cogni-e-Spin DB 1.0, a dataset containing 809 experimental records of electrospinning parameters and corresponding nanofiber morphologies. This is the first large-scale, diverse, and machine-learning-ready dataset designed to address data scarcity. Its scale and diversity enable comprehensive process-structure-property analyses and support machine learning methods to gain insights into the complex electrospinning process. To assess the dataset's reliability and utility, we verified data integrity against source publications and trained machine learning models as a proof of concept. These data are intended to accelerate fundamental research, model training, and process optimization across various electrospinning applications. We also developed a companion web platform to enable live dataset contributions and interactive exploration, fostering continuous community-driven updates. Electrospinning ; Nanofiber ; Process (computing) ; Reliability (semiconductor) ; Scale (ratio) ; Quality (philosophy) Electrospun Nanofibers in Biomedical Applications ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Shouzhen Li | Huayu Zheng | Lingjie Sun | Shuaishuai Ding | Xiaotao Zhang | Weiwei Hu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Strategies Integrating Knowledge Transfer and a Cascaded Framework: Guiding the Discovery of NIR-II Organic Cocrystals Cocrystal ; Interpretability ; Identification (biology) ; Generalization ; Feature (linguistics) ; Chemical space ; Quantitative structure–activity relationship ; Class (philosophy) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Perovskite Materials and Applications
Authors: Chenghan Li | Garnet Kin-Lic Chan
Source: Proceedings of the National Academy of Sciences (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine learning framework that bridges this gap by distilling knowledge from a small number of high-fidelity quantum calculations into increasingly coarse-grained, machine-learned quantum Hamiltonians. By retaining explicit electronic degrees of freedom, our approach further enables a faithful embedding of quantum and classical degrees of freedom that captures long-range electrostatics and the quantum response to a classical environment to infinite order. As validation, we compute the proton dissociation constants of weak acids and the kinetic rate of an enzymatic reaction entirely from first principles, reproducing experimental measurements within chemical accuracy or their uncertainties. Our work demonstrates a path to condensed phase simulations of reaction free energies at the highest levels of accuracy with converged statistics. Quantum ; Degrees of freedom (physics and chemistry) ; Work (physics) ; Embedding ; Kinetic energy ; Reaction coordinate ; Quantum chemical ; Quantum algorithm ; Wave function ; Quantum process Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Ksenia A. Romanova | N. M. Chtchelkatchev | Yu. G. Galyametdinov
Source: Journal of Molecular Liquids (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Multiscale computational design and experimental validation of mesogenic lanthanide(III) complexes for advanced optoelectronic materials Intermolecular force ; Molecular dynamics ; Density functional theory ; Supramolecular chemistry ; Phase (matter) ; Anisotropy ; Rational design ; Phase transition ; RADIUS Lanthanide and Transition Metal Complexes ; Block Copolymer Self-Assembly ; Magnetism in coordination complexes
Authors: Matthew P. Bogen | Supraja S. Chittari | Abigail S. Knight | Erin C. Day
Source: UNC Libraries (article, 2026-04-11)
Field: Materials Science — Materials Chemistry

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials. Workflow ; Expansive ; Selection (genetic algorithm) ; Perspective (graphical) ; Flexibility (engineering) ; Characterization (materials science) ; Data exploration ; Key (lock) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Kun Luo | Yangyang Huang | X. Zhang | Zhiqiang Li | Jie Xiong | Dongsheng Wang | Rongchao Liu | Liang Tao | Yujie Wang
Source: Frontiers in Plant Science (article, 2026-01-21)
Field: Chemistry — Analytical Chemistry

Design and experiment of tea winnowing parameter control system based on YOLO-AE Tea winnowing is a key process in tea processing. At present, tea winnowing parameters are adjusted by manual observation of tea leaves. This results in the uncertainty of winnowing quality. In this work, we propose a new tea winnowing method based on deep learning for the characteristics of white tea. Firstly, the YOLOv11 model is improved by introducing ACmix and EUCB. The recognition accuracy of the improved YOLO-AE model is improved by 2.1%, and the detection time is shortened by 40%, which significantly improves the detection performance and shortens the inference time. The region segmentation and convolution neural network algorithm are used to distinguish the proportion parameters of each grade in tea in real time, and the accurate wind selection parameters are obtained by combining the winnowing theory. The recognition accuracy of the verification set of the recognition model attains 94%. The MAP (0.5:0.95) is 0.93. A test on the tea winnowing parameter control test bench reveals that the identification accuracy of tea materials with different proportions is consistent. Additionally, the difference between the two batches of high-quality white tea is less than 3%. The winnowing scheme proposed in this study can provide the basic theory and technical support for the design of tea precision winnowing equipment. Winnowing ; Black tea ; Identification (biology) ; Inference ; Process (computing) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: David M. Vahey | Manting Mu | Shannon A. Bonke | Timo Sommer | Prithvi Vangal | Carl J. Mallia | Max García-Melchor | Erwin Reisner
Source: Nature Synthesis (article, 2026-03-12)
Field: Chemistry — Organic Chemistry

Anti-Friedel–Crafts alkylation via electron donor–acceptor photoinitiation Abstract The ubiquity of C–H bonds in organic molecules makes direct C–H functionalization an atom- and step-efficient strategy in synthetic chemistry. However, direct C–H alkylation, particularly of electron-poor aromatic substrates, remains a major challenge because current methods suffer from limited selectivity, functional group tolerance and/or require harsh acidic, pyrophoric or toxic reagents. Here we introduce a selective, scalable and transition-metal-free synthetic strategy for C–H alkylation of electron-poor aromatics under mild conditions, which also exhibits high functional group tolerance applicable to the late-stage functionalization of pharmaceutical compounds. The mechanistic design exploits a redox-active phthalimide ester tag to form an electron donor–acceptor complex that fragments upon photoexcitation to yield a nucleophilic alkyl radical, which selectively alkylates the most electrophilic position of electron-deficient aromatics, thereby exhibiting ‘anti-Friedel–Crafts’ selectivity. Mechanistic studies, microkinetic modelling simulations and computational analyses indicate that the reaction then propagates via radical anion autocatalysis. The ‘anti-Friedel–Crafts’ selectivity is consistent with theoretical predictions from Fukui indices and machine-learning models that provide the framework necessary to forecast selectivity in previously ‘unseen’ substrates, thereby enabling selective alkylation of a wide range of complex molecules and late-stage pharmaceuticals. Alkylation ; Nucleophile ; Electrophile ; Phthalimide ; Selectivity ; Functional group ; Molecule ; Alkyl Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Guravtar Singh Mann | Bikram Singh | Khalid A Alzahrani | Abuzar Khan | Raman Rohith Kumar | Arun Y. Patil | Basem E. Keshta
Source: Journal of Natural Fibers (article, 2026-04-07)
Field: Materials Science — Polymers and Plastics

AI-Powered Innovations in Natural Fiber Packaging for Sustainability The need for improved and environmentally friendly sustainable packaging materials with exceptional physical, mechanical, and barrier qualities is growing. The existing industrial packaging materials primarily use non-biodegradable plastics and other raw materials with limited recyclability and has serious consequences toward raised carbon footprints. Therefore, the integration of artificial intelligence (AI) in the sustainable packaging industry is fundamentally changing how things are packaged and distributed, with emerging materials and technologies that promote safety, quality, and sustainability. Many AI-based tools are being used or evaluated for packaging systems, including (but not limited to) machine learning (ML), deep learning (DL), sensors for monitoring, predictive analytics, and generative models for accurate predictions of shelf life, automated identification of defects or contamination, traceability or authentication of a product’s origins, optimal material selection for barrier and environmental properties, and more sustainable packaging designs. This article investigates various applications of AI for sustainable packaging for various applications. Moreover, various challenges in practice data heterogeneity, model generalizability, and scalability showing the importance strong and well-designed AI framework are necessary to implement effectively are also discussed. Overall, the sustainable-packaging sector can expect that AI innovations will improve productivity, food safety and waste management. Sustainability ; Fiber ; Natural (archaeology) ; Natural fiber Natural Fiber Reinforced Composites ; Consumer Packaging Perceptions and Trends ; Nanocomposite Films for Food Packaging
Authors: Josefa Díaz-Álvarez | Francisco A. Galea-Gragera | Francisco Chávez | Pedro A. Salguero-López | Fernando Llera Cid
Source: Frontiers in Artificial Intelligence (article, 2026-02-27)
Field: Chemistry — Analytical Chemistry

Classification of Lupinus seeds into sweet and bitter categories using VIS–NIR spectroscopy and machine learning The results indicate that combining VIS-NIR spectroscopy with machine learning provides a suitable non-destructive alternative to discriminate sweet and bitter <i>Lupinus</i> materials/ecotypes. Overfitting ; Resampling ; Bitter taste ; Germplasm ; Pattern recognition (psychology) ; Chemometrics ; Identification (biology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Botanical Research and Chemistry
Authors: Lorenzo Celiberti | Alexander Ehrentraut | Luca Leoni | Cesare Franchini
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Machine Learning the order-disorder Jahn-Teller transition in LaMnO$_3$ We investigate the Jahn-Teller structural phase transition in LaMnO$_3$ at $T_{JT} \simeq 750$ K using molecular dynamics simulations based on machine-learning force fields trained on ab initio data. Analysis of the site-site correlation function of the distortions reveals that the transition is driven by the ordering of the $Q_2$ Jahn-Teller distortion of the MnO$_6$ octahedra, which acts as the order parameter and establishes the order-disorder nature of the transition. Dynamical local distortions are found to persist above $T_{JT}$. Our results reproduce the experimental temperature dependence of both structural and phonon properties and highlight the presence of anharmonic effects at finite temperature. More broadly, the combined use of machine-learning molecular dynamics and velocity autocorrelation function analysis provides a robust framework for uncovering the microscopic mechanisms of structural phase transitions in correlated materials. In particular, this approach enables a clear distinction between order-disorder transitions and alternative mechanisms, such as displacive behavior, through the temperature evolution of vibrational properties. Autocorrelation ; Phase transition ; Anharmonicity ; Molecular dynamics ; Ab initio ; Function (biology) ; Distortion (music) ; Phase (matter) Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Inorganic Chemistry and Materials