jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Flóra Boróka Németh | Andrea Hamza | Beatrix Tugyi | Maya El-Ali | Luca Szegletes | Ádám Madarász | Imre Pápai
Source: Research Square (preprint, 2025-08-01)
Field: Chemistry — Electrochemistry

PredPotS – A Web Tool for Predicting One-Electron Standard Reduction Potentials for Organic Molecules in Aqueous Phase Aqueous solution ; Redox ; Computer science ; Electrochemistry ; Chemistry ; Organic molecules ; Dimensionality reduction ; Reduction (mathematics) ; Molecule ; Computational chemistry ; Artificial intelligence ; Physical chemistry ; Mathematics ; Inorganic chemistry ; Electrode ; Organic chemistry Electrochemical Analysis and Applications ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Zhao Xiang | Tianhang Zhou | Yuqi Wei | Chen Shen | Zhenghao Wu
Source: ChemRxiv (preprint, 2025-06-06)
Field: Materials Science — Materials Chemistry

Learning Accurate and Transferable Force Fields for Physical Property Predictions of Organic Liquids Machine learning interatomic potentials (MLIPs) have emerged as efficient surrogates for quantum mechanical calculations, offering substantial acceleration in ab-initio atomistic simulations. While most applications of MLIPs have focused on isolated molecules, extending their accuracy and transferability to predict condensed-phase properties of liquids remains a major challenge. In this work, we present a novel framework for constructing transferable and data-efficient force fields for organic liquids using a dual-space active learning (AL) strategy. This approach enables an efficient AL workflow across both configurational and chemical space by coupling a query-by-committee method with an explicitly constructed target chemical space, generated using computationally inexpensive classical methods. Our approach employs a Euclidean transformer architecture to train the Neural Network Potential for Liquid Simulations (NPLS). As a proof of concept, we target the complete alkane family and train NPLS using high-level DFT data. The resulting model is rigorously benchmarked against experimental measurements and the widely used classical force field, i.e., OPLS-AA, across thermodynamic, dynamic, and phase transition properties. Remarkably, the NPLS model demonstrates strong generalization to larger systems of polyolefins and accurately captures liquid-to-solid transitions. However, systematic deviations are observed, particularly in the predicted liquid densities. We attribute these discrepancies to the non-negligible influence of nuclear quantum fluctuations (NQF), which are not captured with classical molecular dynamics (MD) sampling. To address this, we perform path-integral (PI) MD simulations to incorporate NQF, predictions of which show significantly improved agreement with experimental measurements. The NLPS-PIMD results outperform the OPLS-AA force fields in accuracy. We believe this methodology provides a practical and extensible route for developing highly accurate and transferable bespoke or universal MLIPs for complex liquids. Physical property ; Property (philosophy) ; Materials science ; Chemical physics ; Statistical physics ; Physics ; Composite material Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Various Chemistry Research Topics
Authors: Emma Rydholm | Rocío Mercado | Samuel Genheden
Source: Digital Discovery (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Retrosynformer: planning multi-step chemical synthesis routes <i>via</i> a decision transformer We present RetroSynFormer, a novel approach to multi-step retrosynthesis planning. Here, we express the task of iteratively breaking down a compound into building blocks as a sequence-modeling problem and train a Decision Transformer-based model. Transformer ; Retrosynthetic analysis ; Task (project management) ; Decision tree ; Decision support system ; Work (physics) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Process Optimization and Integration
Authors: Pei Yuan Li | Ya Li Zhang | Hao Yan | Xiao Meng Zhou
Source: Corrosion Reviews (article, 2025-06-19)
Field: Materials Science — Polymers and Plastics

Investigation of common aircraft alloy corrosion behavior and mechanism by the potential halon replacement fire suppressant C <sub>2</sub> F <sub>5</sub> I Abstract This study investigated the kinetics and mechanisms of high-temperature corrosion of common aerospace alloys, such as those used in aircraft engines and fuselage skins, when locally exposed to the fire extinguishing agent C 2 F 5 I.The aim is to elucidate the material degradation process and to assess the efficacy of C 2 F 5 I as a halon replacement in aerospace applications. The results of the study showed that at 200 °C, C 2 F 5 I was compatible with all metals tested with no significant corrosion. However, at temperatures above 300 °C, the thermal decomposition of C 2 F 5 I produced C1–C4 fluorinated hydrocarbon products that resulted in significant corrosion of the alloys. Among these alloys, 2024 exhibits the best corrosion resistance, which shows that the performance of Al-based alloys is better than that of Fe-based alloys and Cu-based alloys. Based on these findings, the most heavily corroded alloy, H62, was selected for further analysis of corrosion products and gas by-products. The mechanism of Cu alloy corrosion by C 2 F 5 I at elevated temperatures was elucidated. The results provide valuable insights for performance evaluation and selection of halon alternatives. Corrosion ; Alloy ; Degradation (telecommunications) ; Aerospace ; Decomposition ; Fuselage ; Mechanism (biology) Flame retardant materials and properties ; Engineering and Material Science Research ; Fire effects on concrete materials
Authors: Hend M. Ahmed | J.I. Abd-El Thalouth | Elmorsy Khaled | Usama Rashed | A. A. El‐Halwagy | Tawfik A. Khattab
Source: Scientific Reports (article, 2025-11-10)
Field: Materials Science — Surfaces, Coatings and Films

Plasma-induced modification of fenugreek galactomannan thickener film via dielectric barrier discharge for textile printing Fenugreek seeds are a valuable source of galactomannan, a natural thickener with applications in textile printing. However, traditional galactomannan extraction methods often suffer from lengthy processing times and limited yields. To address these limitations, the use of dielectric barrier discharge (DBD) atmospheric cold plasma was applied as a pretreatment method. Dried fenugreek seeds were subjected to air and oxygen gas plasma treatments at various discharge powers and exposure durations. Subsequently, galactomannan was extracted and formed into films (membranes) for characterization. Additionally, the rheological properties of the galactomannan solutions were investigated to assess their potential as eco-friendly thickening agents in textile printing pastes. The antibacterial activity of the treated galactomannan was also investigated. Galactomannan ; Dielectric barrier discharge ; Textile ; Rheology ; Extraction (chemistry) ; Propellant Surface Modification and Superhydrophobicity ; Plasma Applications and Diagnostics ; Advanced Cellulose Research Studies
Source: Journal of Food Chemistry and Nanotechnology (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Investigation on Structural and Elastic Properties of Nickel Ferrite Nanoparticles Synthesized via Green Route Poorwang K Bhatt, Payalkumari M Savaliya, Shyam R Ajudiya, Ashish R Tanna and Harshal B Desai Nickel ferrite nanoparticles are synthesized using Mangifera Indica pulp (mango pulp) as a fuel in the self-combustion method. The green synthesis route is inexpensive, eco-friendly, and easily scalable for producing large quantities of solid materials. Nanoparticle ; Nickel ; Ferrite (magnet) ; Pulp (tooth) Magnetic Properties and Synthesis of Ferrites ; Nanoparticle-Based Drug Delivery ; Pigment Synthesis and Properties
Authors: Rajkanya Das | Ranjan Kr. Kakati | Anu Kumari | Sonalika Vaidya | Arabinda Baruah
Source: Environmental Science and Pollution Research (article, 2025-10-27)
Field: Chemistry — Organic Chemistry

Nickel-modified tungsten disulphide: an efficient catalyst for the reduction of nitrophenol isomers and pharmaceutical pollutants Catalysis ; Nickel ; Steric effects ; Selective catalytic reduction ; Nitrophenol ; 4-Nitrophenol ; Tungsten ; Electron transfer Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Murali Krishna Pasupuleti
Source: International Journal of Academic and Industrial Research Innovations(IJAIRI) (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Edge Tensor-AI Enabled Predictive Control of Self-Healing Anti-Corrosion Coatings for Marine Robots Abstract: This study investigates a closed-loop durability framework in which edge-deployed tensorized machine learning models predict coating health and drive predictive control actions that enhance the persistence of self-healing anti-corrosion coatings on marine robots. The targeted operating context combines high-chloride seawater exposure, mission-driven thermal cycling, and limited maintenance opportunities, creating a need for autonomous decisions that preserve coating integrity and delay substrate corrosion. Real electrochemical impedance spectroscopy (EIS) measurements reported for epoxy coatings on carbon steel with and without linseed-oil microcapsules are curated as a domain-grounded benchmark, spanning intact exposure to 3.5 wt% NaCl and damaged exposure to 0.1 mol/L NaCl over 500-1000 h. A tensor feature representation couples immersion time, fitted circuit parameters, and damage/healing state to support edge-friendly prognostics. The proposed control layer operationalizes coating self-healing as an actionable resource: surfacing intervals to increase oxygen availability for drying-oil curing, local thermal stimulation for accelerated polymerization, and mission-aware constraints on energy and time. Results show that microcapsule-enabled coatings sustain orders-of-magnitude higher corrosion impedance under damage compared with neat epoxy, while exhibiting measurable trade-offs in intact barrier metrics. The study further demonstrates that lightweight neural prognostics can be evaluated on real corrosion data and integrated into an interpretable predictive control loop suitable for embedded marine platforms. These findings motivate a shift from passive coatings toward cyber-physical “coating health management,” in which materials self-healing and robot autonomy co-evolve. The resulting design space spans coating formulation, onboard sensing, edge inference, and mission-level decision-making, enabling durable marine robotics in environments where corrosion is a dominant life-limiting failure mode. Keywords: marine robotics; corrosion; self-healing coatings; electrochemical impedance spectroscopy; edge AI; predictive control; tensor learning Coating ; Model predictive control ; Robotics ; Corrosion ; Context (archaeology) ; Robot ; Prognostics ; Durability ; Electrical impedance Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Structural Integrity and Reliability Analysis
Authors: Meena Sankari S | R. Sagayaraj | S. Sebastian | A Amalorpavadoss | V. Porkalai | S. Aravazhi
Source: Research Square (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Investigation of Magnetic, Thermal and Electrical Properties of Mg0.5Zn0.5Fe2O4 Ferrite Nanoparticles by Annealing Temperature Effect Annealing (glass) ; Crystallite ; Coercivity ; Spinel ; Nanoparticle ; Analytical Chemistry (journal) ; Crystallinity ; Thermal stability ; Grain size Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Filippozzi, Davide | Mayer, Alexandre | Roy, Nicolas | Fang, Wei | Rahimi-Iman, Arash
Source: ArXiv.org (article, 2025-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches Chiral photonic metasurfaces provide unique capabilities for tailoring light-matter interactions, which are essential for next-generation photonic devices. Here, we report an advanced optimization framework that combines deep learning and evolutionary algorithms to significantly improve both the design and performance of chiral photonic nanostructures. Building on previous work utilizing a three-layer perceptron reinforced learning and stochastic evolutionary algorithm with decaying changes and mass extinction for chiral photonic optimization, our study introduces a refined pipeline featuring a two-output neural network architecture to reduce the trade-off between high chiral dichroism (CD) and reflectivity. Additionally, we use an improved fitness function, and efficient data augmentation techniques. A comparative analysis between a neural network (NN)-based approach and a genetic algorithm (GA) is presented for structures of different interface pattern depth, material combinations, and geometric complexity. We demonstrate a twice higher CD and the impact of both the corner number and the refractive index contrast at the example of a GaP/air and PMMA/air metasurface as a result of superior optimization performance. Additionally, a substantial increase in the number of structures explored within limited computational resources is highlighted, with tailored spectral reflectivity suggested by our electromagnetic simulations, paving the way for chiral mirrors applicable to polarization-selective light-matter interaction studies. Artificial neural network ; Photonics ; Genetic algorithm ; Evolutionary algorithm ; Perceptron ; Pipeline (software) ; Genetic programming ; Multilayer perceptron Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Neural Networks and Reservoir Computing
Authors: Haneef Raza
Source: Research Square (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

Polyimide-MACE: Unifying Mechanical Dynamics and Chemical Reactivity in High-Dimension Polymers via Equivariant Active Learning Mechanical energy ; Active learning (machine learning) ; Potential energy ; Polymer ; Glass transition ; Artificial neural network ; Potential energy surface ; Coherence (philosophical gambling strategy) ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Nanopore and Nanochannel Transport Studies
Authors: Sameer Hussain | Satyakam Agarwala | Deboja Sharma | Gwyneth Rachel Suiam
Source: Research Square (preprint, 2025-01-06)
Field: Materials Science — Materials Chemistry

Bio Fabrication of Zinc oxide nanoparticles using Dillenia indica (Elephant apple) Core extract and its Antimicrobial properties. Fourier transform infrared spectroscopy ; Nanoparticle ; Antimicrobial ; Aspergillus niger ; Nuclear chemistry ; Silver nanoparticle ; Agar diffusion test ; Zinc ; Nanotechnology ; Chemistry ; Materials science ; Chemical engineering ; Organic chemistry ; Food science ; Biochemistry ; Escherichia coli Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection ; Essential Oils and Antimicrobial Activity
Authors: Zhao Xiang | Tianhang Zhou | Yuqi Wei | Chen Shen | Zhenghao Wu
Source: ChemRxiv (preprint, 2025-07-09)
Field: Materials Science — Materials Chemistry

Learning Accurate and Transferable Force Fields for Physical Property Predictions of Organic Liquids Machine learning interatomic potentials (MLIPs) have emerged as efficient surrogates for quantum mechanical calculations, offering substantial acceleration in ab-initio atomistic simulations. While most applications of MLIPs have focused on small molecules, extending their accuracy and transferability to predict condensed-phase properties of liquids remains a major challenge. In this work, we present a novel framework for constructing transferable and data-efficient force fields for organic liquids using a dual-space active learning (AL) strategy. This approach enables an efficient AL workflow across both configurational and chemical space by coupling a query-by-committee method with an explicitly constructed target chemical space, generated using computationally inexpensive classical methods. Our approach employs a Euclidean transformer architecture to train the Neural Network Potential for Liquid Simulations (NPLS). As a proof of concept, we target the complete alkane family and train NPLS using high-level DFT data. The resulting model is rigorously benchmarked against experimental measurements, the state-of-the-art MLIP and widely-used classical force field across thermodynamic, dynamic, and phase transition properties. Instead of emphasizing model transferability across chemical elements in the periodic table, we highlight the importance of transferability across thermodynamic states. Remarkably, the NPLS model exhibits strong generalization across a broad range of temperatures and pressures, accurately capturing liquid–solid phase transitions and enabling stable simulations of the derived polymers. However, systematic deviations, particularly in predicted liquid densities, arise from the neglect of nuclear quantum fluctuations (NQF) in classical MD. To address this, we perform path-integral MD (PIMD) simulations incorporating NQF, which significantly improve agreement with experiments. The resulting NPLS-PIMD predictions outperform those from OPLS-AA force fields in accuracy. We believe this methodology provides a practical and extensible route for developing highly accurate and transferable bespoke or universal MLIPs for complex liquids. Property (philosophy) ; Physical property ; Statistical physics ; Materials science ; Chemical physics ; Mechanics ; Physics ; Composite material Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Various Chemistry Research Topics
Authors: Bayader F. Abbas | Mahmoud Najim Al-jibouri | Fatima Haitham Zuhair
Source: Bulletin of the Chemical Society of Ethiopia (article, 2025-05-27)
Field: Materials Science — Polymers and Plastics

Mixed ligands of (E)-1-(3-(((1H-1,2,4-triazol-3-yl)imino)methyl)-4-hydroxy-5- methoxyphenyl)ethan-1-one and chloroanilic acid: Synthesis, characterization and thermal stability study The present work involved the synthesis, characterization and thermal study of metal complexes of Co(II), Cu(II), Ni(II) and Zn(II) derived from mixed ligands;HL1: (E)-1-(3-(((1H-1,2,4-triazol-3-yl)imino)methyl)-4-hydroxy-5-methoxyphenyl)ethan-1-one and H2L2: 2,3-dichloro-5,6-dihydroxycyclohexa-2,5-diene-1,4-dione. The new ligands and their metal complexes were fully identified on the basis of FT-IR, EI-MS, NMR, UV-Visible spectroscopy, molar conductivity and magnetic susceptibility measurements. The new metal complexes were prepared in their solutions in ethanol and in solid state with respect 1:1:1 mole ratio of M:L1 : HL2. As well as the elemental microanalysis was carried out to confirm the skeletal structure of the metal complexes. The physicochemical properties such as melting point, magnetic susceptibility and molar conductivity at room temperature were determined. The spectral data of IR, UV-Visible spectra and magnetic moments revealed the formation of octahedral environment around Co(II), Ni(II), Cu(II) and Zn(II) ions. As well as the TG-DTA measurements confirmed the thermal stability due to the cleavage of weak points in the skeletal structures of metal complexes. KEY WORDS: (E)-1-(3-(((1H-1,2,4-triazol-3-yl)imino)methyl)-4-hydroxy-5-methoxyphenyl)ethan-1-one, Thermal stability of apocynin, Thermal stability of mixed ligands Bull. Chem. Soc. Ethiop. 2025, 39(8), 1549-1558. DOI: https://dx.doi.org/10.4314/bcse.v39i8.8 Chemistry ; Thermal stability ; Characterization (materials science) ; Stability (learning theory) ; Stereochemistry ; Computational chemistry ; Combinatorial chemistry ; Organic chemistry ; Nanotechnology Synthesis and properties of polymers
Authors: Debasis Maity | Jinbo Huang | Martin Fussenegger
Source: Advanced Energy Materials (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Wearable Thermoelectric Generators for In Vivo Modulation of Insulin Release ABSTRACT Bioelectronic medicine has provided innovative approaches to regulate cellular function, but a major limitation of most bioelectronic therapies is their dependence on external power sources such as batteries. Wearable thermoelectric generators with the ability to harvest sustainable energy from body heat represent a promising solution. Here, we present a device, called “integrated grid of generators imparting thermoelectric energy” (IGNITE), that enables wearable, sustainable, self‐powered modulation of insulin secretion via bioelectric stimulation of engineered human cells. The IGNITE system continuously harvests body heat and converts it into a controlled electrical output to drive insulin release. The wearable system consists of an 8 × 8 series generator array composed of P‐type (Sb 2 Te 3 @PEDOT:PSS) and N‐type (Bi 2 Te 3 @TCNQ) materials, which efficiently generates and amplifies electrical energy to produce a stable 4.2 V output through an integrated voltage regulation circuit. A 60 s electrical stimulation induces reactive oxygen species (ROS)‐mediated activation of engineered mammalian cells, leading to insulin production in a controlled and reproducible manner. In a proof‐of‐concept experiment in type 1 diabetic mice, the system achieved glycemic regulation without any external power source. This device lays the foundation for next‐generation bioelectronic therapies by integrating sustainable bioenergy energy harvesting, nanotechnology, and cellular engineering for endocrine modulation. Thermoelectric generator ; Thermoelectric effect ; Wearable computer ; Wearable technology ; Voltage ; Modulation (music) ; Renewable energy ; Insulin Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors