jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tereza Benešová | Kateřina Pokorná | Andreas Erlebach | Christopher J. Heard
Source: Journal of Catalysis (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Mobility and sintering of silica-supported platinum clusters via reactive neural network potentials Sintering ; Coalescence (physics) ; Silanol ; Catalysis ; Platinum ; Cluster (spacecraft) ; Hydrogen ; Oxide Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Alex Kondi | Efi-Maria Papia | Vassilios Constantoudis | Dimitrios Nioras | Ioannis Syngelakis | Chrysa Aivalioti | E. Aperathitis | Εvangelos Gogolides
Source: Micro and Nano Engineering (article, 2025-08-11)
Field: Materials Science — Surfaces, Coatings and Films

Measurement of thickness of thin coatings on rough substrates via computational analysis of SEM images This work introduces a computational method to quantify the thickness of thin films deposited on highly rough substrates analyzing top-down Scanning Electron Microscope (SEM) images. The method entails measuring the bright areas of top-down SEM images of the rough surface obtained before and after deposition, allowing for the prediction of film thickness through the ratio of bright area enhancement caused by deposition to the average perimeter of these areas before and after deposition. Validation of this technique was conducted via synthetic SEM images with predefined film thicknesses, incorporating simple and complex substrate morphologies generated through Diffusion-Limited Aggregation (DLA) simulations for added realism. Experimental applications were explored through the analysis of SEM images of plasma-etched polymer (PMMA) surfaces coated with carbyne and of nanorods of TiO 2 coated with NiO, demonstrating the method's efficacy across varying surface roughness and morphologies. This work lays the foundation for future advancements, including the implementation of a neural network trained on synthetic datasets to enhance the measurement accuracy of coating thickness on rough substrates as well as the reconstruction of true surface morphologies prior to metal layer sputtering via SEM image analysis. • Quantified thin film thickness on rough substrates using top-down SEM images, avoiding destructive cross-sectional imaging. • Validated with synthetic SEM images of known thickness and real cases incl. carbyne-coated PMMA and NiO-coated TiO 2 nanorods. • Demonstrated effectiveness across diverse surface morphologies. • Provided a robust alternative to traditional techniques, even for substrates with extreme roughness. Materials science ; Composite material ; Computer science Electron and X-Ray Spectroscopy Techniques ; Surface Roughness and Optical Measurements ; Advancements in Photolithography Techniques
Authors: Osbel Mosqueda-Frómeta | Iván Andújar | Grisis M. Mosqueda-Rodríguez | Alexey Pestryakov | Barbarita Companioni | María de Lourdes Tapia y Figueroa | Oscar Concepción | Maritza Escalona | Nina Bogdanchikova | José Carlos Lorenzo
Source: In Vitro Cellular & Developmental Biology - Plant (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Mini-review: Application of nanoparticles in plant biotechnology and agriculture Biotechnology ; Agriculture ; Engineering ; Biochemical engineering ; Environmental science ; Nanotechnology ; Biology ; Materials science ; Ecology Nanoparticles: synthesis and applications ; Plant tissue culture and regeneration
Authors: Fayu Sun | Fei Li | Dedong Hu | Weiqiang Wang
Source: Textile Research Journal (article, 2025-01-06)
Field: Materials Science — Polymers and Plastics

The swelling experiment and molecular simulation of polyethylene terephthalate(PET) in supercritical carbon dioxide The present study aimed to employ a synergistic approach integrating experiments and molecular dynamics simulation to investigate the swelling of PET films caused by SC-CO 2 and sorption of CO 2 within the PET films. The rate of volume change (Δ V/V ) of PET films increased with temperature elevation (50–140°C) and pressure elevation (10–27.5 MPa) which were often used in dyeing process. The sorption concentration decreased with rising temperature at the lower pressure, while at high pressure, it increased concomitantly with temperature elevation. The values of glass transition temperature ( T g ) predicted by Δ V/V were 75.5, 69.7, and 60°C , and fitted by fractional free volume ( FFV ) were 75.8, 67.0, and 56.7°C , respectively. In addition, it was found that the swellability of PET was dependent upon the interaction between CO 2 and the carboxyl group of PET chains. Eventually, the model based on backpropagation neural network optimized by Sparrow search algorithm (SSA-BPNN) exhibited robust predictive capabilities, yielding results consistent with experimental data for training set and testing set. Therefore, it can be concluded that the SSA-BPNN model is verified to be an efficient quantitative tool to predict the Δ V/V caused by SC-CO 2 and sorption concentration within PET films in this work. Polyethylene terephthalate ; Supercritical carbon dioxide ; Materials science ; Swelling ; Supercritical fluid ; Carbon dioxide ; Composite material ; Polyethylene ; Chemical engineering ; Organic chemistry ; Chemistry ; Engineering Polymer Foaming and Composites ; Polymer crystallization and properties ; Phase Equilibria and Thermodynamics
Authors: CH. Saikrupa | G. Chandramohan Reddy | Sriram Venkatesh
Source: Hybrid Advances (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Application of machine learning based algorithm to predict performance of turning Al–SiC-MWCNT using cryogenically treated textured insert This work intends to improve dry machining of Al–SiC-MWCNT (Aluminum–Silicon Carbide-Multi-Walled Carbon Nanotube) composites with cryogenically treated textured cutting tool inserts. The study aims to optimize machining parameters such feed rate, cutting speed, depth of cut, and nanoparticle concentration to assess their effects on surface roughness and power utilization. These characteristics are key indications of machining processes' product quality and energy efficiency. Textured tools and solid lubrication have been studied; however, lubrication supply systems and high-temperature endurance are still issues. Cryogenic treatment is a strong option that addresses these issues by greatly improving cutting tools' hardness and strength. The L27 Taguchi Orthogonal Array was used to design the experiment. The machining trials included different feed rates, cutting speeds, depths of cut, and nanoparticle concentrations. The machining process's surface roughness and power use were analyzed. A Support Vector Machine (SVM) model was created for predictive study of surface roughness, giving a data-driven way to evaluate machining performance. The SVM model's prediction accuracy and error margin were used to measure its efficacy. The Support Vector Machine model was quite accurate, with a margin of error under 5 %. The model's R 2 values of 0.87 and 0.90 for power consumption and surface roughness prediction show strong correlation and dependability. The findings imply that cryogenically treated textured cutting tools boost machining efficiency by lowering surface roughness and optimizing power usage. These findings support the use of cryogenic treatment and machine learning models in advanced machining procedures for composite materials. Insert (composites) ; Materials science ; Computer science ; Mechanical engineering ; Composite material ; Engineering Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Advanced Surface Polishing Techniques
Authors: Fatima Zehra | Tarab Fatima | Manika Khanuja
Source: Advanced Materials Technologies (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Colorimetric Sensing of Mercury with Machine Learning Based on Nanozyme Activity of Chitosan Functionalized MXene/ZIF‐L Nanocomposite Abstract Mercury is a highly toxic, global neurotoxin and carcinogen, causing significant risk to human health and environmental safety in small concentrations. This research paper explores the innovative, eco‐friendly, and inexpensive colorimetric detection of mercury ions (Hg 2+ ) by MXene/ZIF‐L (MXZL) nanocomposite with machine learning (ML). MXZL nanocomposite has a high surface area of 211.19 m 2 g −1 , making it compatible for functionalization with chitosan. The sensing principle is based on the bio‐catalytic property of chitosan functionalized MXene/ZIF‐L (CS‐MXZL) that oxidizes 3,3′,5,5′‐tetramethylbenzidine (TMB) with H 2 O 2 and gives a blue colour. The chitosan‐captured Hg 2+ ions MXZL surface do not exhibit this property. The colorimetric assay with nanozyme activity achieves a limit of detection (LOD) of 50 nM, analyzed via a UV−vis spectrophotometer. This is integrated with ML models, namely Linear Regression, Decision Tree Regression, Support Vector Regression (SVR), k‐NN, and XGBoost, to accurately estimate analyte concentrations. The k‐NN model demonstrated superior predictive accuracy, recording Mean Absolute Error (MAE) of 0.1200, Root Mean Square Error (RMSE) of 0.1350, and the Coefficient of Determination (R 2 ) score of 0.9693. Hence, the developed ML‐enhanced colorimetric platform offers a cost‐effective, portable solution for mercury detection, advancing efforts in environmental sustainability and hazardous materials management. Chitosan ; Nanocomposite ; Mercury (programming language) ; Chemistry ; Nanotechnology ; Materials science ; Computer science ; Organic chemistry MXene and MAX Phase Materials ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Ewan R. S. Wallace | Nathan C. Frey | Joshua A. Rackers
Source: Journal of Chemical Information and Modeling (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Strain Problems Got You in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule cocrystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug design. In this work we present a tool for calculating ligand strain with a high accuracy. StrainRelief uses a MACE neural network potential (NNP), trained on a large database of density functional theory (DFT) calculations to estimate ligand strain of neutral molecules with quantum accuracy. We show that this tool estimates strain energy differences relative to DFT to within 1.4 kcal/mol, more accurately than alternative NNPs. These results highlight the utility of NNPs in drug discovery, and provide a useful tool for drug discovery teams. Strain (injury) ; Twist ; Ligand (biochemistry) ; Quantum ; Mathematics ; Computer science ; Physics ; Quantum mechanics ; Biology ; Geometry ; Genetics ; Anatomy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Materials Characterization Techniques
Authors: Jikun Yin | Bo Wang | Tie Wang | Zhiyong Tang
Source: Fundamental Research (article, 2025-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection The superstructures formed by the self-assembly of nanoparticles (NPs) can exhibit unique photonic collective properties (structural color, localized surface plasmon resonance [LSPR]), enhance the interaction between light and matter, and open up new possibilities for photonic sensing. Many photonic biosensors have addressed the limitations of current bioanalytical methods with their non-invasive nature, real-time monitoring, and high sensitivity. In recent years, the construction of photonic biosensors using super-structured materials could further enhance the sensors in terms of sensitivity, processing capacity, ease of use, and miniaturization. Superstructure-based photonic biosensors can analyze complex samples, but their development still needs to overcome limitations related to target binding specificity, long-term stability, and signal decoding efficiency. The development of artificial intelligence (AI) provides new opportunities to solve these problems. Deep learning (DL) algorithms can independently extract multi-dimensional data features such as spectra and images, distinguish weak biological signals from noise, optimize detection parameters, and achieve real-time dynamic calibration. In this review, we provide the photonic collective characteristics of superstructures and the applications of biosensors in intelligent diagnosis. The applications of superstructured photonic sensors in disease diagnosis, drug delivery, and cell imaging are summarized. The colorimetric, fluorescence-based sensor technologies assisted by DL are discussed along with challenges faced in integrating AI with superstructure-based photonic biosensors. As this field continues to evolve, the integration of AI and superstructure-based photonic biosensors will undoubtedly play a pivotal role in shaping the future of medical diagnostics and therapeutic interventions. Photonics ; Biosensor ; Photonic crystal ; Signal processing ; Surface plasmon resonance ; SIGNAL (programming language) Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Derrick Mirindi | David Sinkhonde | Frédéric Mirindi
Source: Manufacturing Letters (article, 2025-08-01)
Field: Materials Science — Polymers and Plastics

Application of machine learning to predict the properties of wood- composite made from PET, HDPE, and PP fibres Plastic composites provide an eco-friendly substitute for conventional construction materials. Indeed, recycling waste plastic represents a progressive approach to waste management with the aim of mitigating the growing issue of pollution in urban environments. Our research aims to review the physical properties, including water absorption (WA) and thickness swelling (TS), and mechanical properties, such as the internal bond (IB), the modulus of rupture (MOR), and the modulus of elasticity (MOE), of the latest findings made of wood panels combined with plastic. We are focusing on three types of plastic, namely polyethylene terephthalate (PET), polypropylene (PP), and high-density polyethylene (HDPE). In addition, we employed machine learning (ML) algorithms, including the hierarchical clustering dendrogram, the Pearson correlation coefficient, the support vector regression, the random forest (RF), and the decision tree (DT) for prediction analysis. For instance, the results indicate that combining HDPE with wood pulp fiber increases the MOR (42.45 MPa) and MOE (66.7 MPa), respectively. Furthermore, mixed plastics such as PET, HDPE, PP, and LDPE improve the dimensional stability by reducing the WA (0.32 %) and TS (0.18 %), respectively. In most cases, these results meet the minimum standard requirement for general-purpose boards, according with the American National Standard for Particleboard (ANSI/A208.1-1999), the European standard (EN 312), and Brazilian Association of Technical (ABNT NBR) standard. In addition, the dendrogram identifies three primary clusters with varying Euclidean distances, indicating the performance of wood-plastic panels for both physical and mechanical properties. Notably, the dimensional stability among panels is stronger than that of mechanical properties. The correlation matrix is important for selecting an appropriate plastic. The SVR, RF, and DT algorithms make predictions by analyzing the properties of the panel. For instance, the DT algorithm shows that when WA is less than 25 %, the predicted value of TS is 0.24 %; in addition, when the value is between 25 % and 75 %, TS is equal to 7.92 %; also, when WA is greater than 75 %, TS is predicted to be at 13.7 %. This innovative method of utilizing ML and DL for prediction opens new possibilities for the use of plastic in panel production, as it allows for the selection of suitable materials and fabrication techniques to create a wood-plastic composite. High-density polyethylene ; Composite number ; Composite material ; Materials science ; Polymer science ; Artificial intelligence ; Computer science ; Polyethylene Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Mechanical Behavior of Composites
Authors: Lin Han | Zhendong Li | Zhunyun Tang | Xiaoxia Wang | Jin Li | Chaoyu He | Chao Tang | Tao Ouyang
Source: Journal of Applied Physics (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Notable impact of magnetic order and flat phonon mode on the thermal transport properties of 2D magnetic semiconductor CrSBr Monolayer CrSBr, an air-stable magnetic semiconductor, hosts great potential applications in spintronics, optoelectronics, and magnetic imaging. A deep understanding of its thermal transport behavior, particularly the influence of magnetic order, is essential for revealing the microscopic mechanisms of spin–phonon interactions in two-dimensional magnets. In this study, we systematically investigate the effect of magnetic order on the thermal transport properties of monolayer CrSBr by using machine learning potentials combined with the Boltzmann transport equation. The calculations show that the magnetic order significantly affects the thermal conductivity of monolayer CrSBr. Around the Curie temperature (145 K), the thermal conductivity along the x-axis is 80.79, 89.63, and 58.91 W/mK for ferromagnetic, antiferromagnetic type I, and antiferromagnetic type II phases, respectively. Along the y-axis, the corresponding values are 37.38, 45.77, and 19.20 W/mK. Such substantial variation mainly arises from differences in phonon lifetimes attributable to the distinct anharmonic effects present in CrSBr, influenced by its magnetic order. Meanwhile, it is found that the specific flat phonon modes in monolayer CrSBr could induce strong four-phonon scattering rates. As a result, the lattice thermal conductivity is reduced by up to 36.7% compared to the case where only three-phonon scattering is considered. These findings not only emphasize the critical role of magnetic order and flat phonon modes in governing the thermal transport behavior of monolayer CrSBr but also provide important guidelines for designing thermal management strategies via magnetic order. Thermal conductivity ; Phonon ; Monolayer ; Antiferromagnetism ; Scattering ; Boltzmann equation ; Phonon scattering ; Curie temperature Thermal properties of materials ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Anna Katharina Picha | Sara Tkaczyk | Thierry Langer | Marcus Wieder | Stefan Boresch
Source: The Journal of Physical Chemistry Letters (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials Allowing atoms or molecules to disappear is a critical step in alchemical free energy simulations (FES). The necessary tricks are well understood when using force fields. Over the past few years, neural network potentials (NNPs) have seen rapid development. Their potentially higher accuracy compared to force fields makes them attractive for use in FES. Here, we outline a method for gradually decoupling atoms and molecules in systems that are fully described by NNPs. Specifically, we show that manipulating the neighbor list is equivalent to using soft-core potentials in force-field-based FES. Since constructing the neighbor list is a central step, regardless of the NNP's inner workings, our approach is agnostic to NNP architecture. We validate the correctness of our methodology by demonstrating cycle closure for a model problem and report solvation free energies obtained with the MACE-OFF23(S/M) NNP. Solvation ; Correctness ; Artificial neural network ; Decoupling (probability) ; Energy (signal processing) ; Closure (psychology) ; Potential energy Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Shuangping Han | Kai Song | Pengyu Zan | Changzhi Yu | Ao Li | Haitao Zhou | Chengbing Qin | Liantuan Xiao
Source: Optics Express (article, 2025-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-accuracy disposable micro-optical anti-counterfeiting labels based on single-molecule quantum coherence In this work, we introduce an innovative approach to single-molecule quantum coherence (SMQC)-based disposable micro-optical anti-counterfeiting labels. This method facilitates the editing and reading of anti-counterfeiting with single molecules used as the anti-counterfeiting information labels. The label is meticulously crafted through inkjet printing technology, while its authentication is achieved via frequency domain imaging. Through a validation process including experimental demonstration, numerical simulation, and neural network analysis, we demonstrate the feasibility of this approach, further validate the integrity of the miniature anti-counterfeiting information storage, and verify the signal extraction accuracy with the recognition accuracy of the labels is consistently above 99.995%. The combination of SMQC-based disposable micro-optical anti-counterfeiting technology is expected to enable more precise preparation of single-molecule-array chips, thus providing a crucial foundation for the advancement of high-tech and intelligent manufacturing industries. Optics ; Coherence (philosophical gambling strategy) ; Optical coherence tomography ; Quantum ; Quantum optics ; Quantum imaging ; Quantum cryptography ; Materials science ; Optoelectronics ; Physics ; Quantum information ; Quantum network ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Information and Cryptography ; Photonic and Optical Devices
Authors: Bing He | Zhicong Lai | Da Wang | Xiaotong Liu | Yue Liu | Miao Xu | Bowei Pu | Qisen Wang | R. Wang | Maxim Avdeev | Siqi Shi
Source: Acta Materialia (article, 2025-10-12)
Field: Materials Science — Materials Chemistry

MCTSGT: A graph theory-based Monte Carlo tree strategy for configuration search in disordered structures Monte Carlo method ; Search algorithm ; Graph theory ; Monte Carlo tree search ; Local search (optimization) ; Tree (set theory) ; Graph ; Energy landscape Machine Learning in Materials Science ; Software Engineering Research ; Advanced Multi-Objective Optimization Algorithms