Layer-by-Layer Nanoarchitectonics: A Method for Everything in Layered Structures

Materials, Journal Year: 2025, Volume and Issue: 18(3), P. 654 - 654

Published: Feb. 1, 2025

The development of functional materials and the use nanotechnology are ongoing projects. These fields closely linked, but there is a need to combine them more actively. Nanoarchitectonics, concept that comes after nanotechnology, ready do this. Among related research efforts, into creating through formation thin layers on surfaces, molecular membranes, multilayer structures these have lot implications. Layered especially important as key part nanoarchitectonics. diversity components used in layer-by-layer (LbL) assemblies notable feature. Examples LbL introduced this review article include quantum dots, nanoparticles, nanocrystals, nanowires, nanotubes, g-C3N4, graphene oxide, MXene, nanosheets, zeolites, nanoporous materials, sol–gel layered double hydroxides, metal–organic frameworks, covalent organic conducting polymers, dyes, DNAs, polysaccharides, nanocelluloses, peptides, proteins, lipid bilayers, photosystems, viruses, living cells, tissues. examples assembly show how useful versatile it is. Finally, will consider future challenges

Language: Английский

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Performance analysis of activation functions in molecular property prediction using Message Passing Graph Neural Networks

Chemical Physics, Journal Year: 2025, Volume and Issue: unknown, P. 112591 - 112591

Published: Jan. 1, 2025

Language: Английский

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Application of machine learning in polyimide structure design and property regulation

High Performance Polymers, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Polyimide (PI) is widely used in modern industry due to its excellent properties. Its synthesis methods and property research have significantly progressed. However, the design regulation of PI structures through traditional technologies are slow expensive, which make it difficult meet practical demand materials. With rapid development high-throughput computing data-driven technology, machine learning (ML) has become an important method for exploring new Data-driven ML envisaged as a decisive enabler PIs discovery. This paper first introduces basic workflow common algorithms ML. Secondly, applications material properties prediction, assisting computational simulation inverse desired reviewed. Finally, we discuss main challenges possible solutions research.

Language: Английский

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Supercapacitor Materials Database Generated using Web Scrapping and Natural Language Processing

Journal of Molecular Graphics and Modelling, Journal Year: 2025, Volume and Issue: 136, P. 108980 - 108980

Published: Feb. 13, 2025

Language: Английский

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Introduction to Machine Learning for Predictive Modeling II

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 3 - 26

Published: Jan. 1, 2025

Language: Английский

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Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data

Polymers, Journal Year: 2025, Volume and Issue: 17(5), P. 694 - 694

Published: March 5, 2025

The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches optimizing their composition properties. This study aimed at the application of machine learning prediction optimization functional properties composites based on a thermoplastic matrix with various fillers (two types fibrous, four dispersed, two nano-dispersed fillers). experimental methods involved material production through powder metallurgy, further microstructural analysis, mechanical tribological testing. analysis revealed distinct structural modifications interfacial interactions influencing key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate strength. Carbon fibers 20 wt. % improved (by 17–25 times) reducing tensile strength elongation. Basalt 10 provided an effective balance between reinforcement 11–16 times). Kaolin 2 greatly enhanced 45–57 moderate reduction. Coke maximized 9−15 acceptable Graphite ensured wear, as higher concentrations drastically decreased Sodium chloride 5 offered improvement 3–4 minimal impact Titanium dioxide 3 11–12.5 slightly Ultra-dispersed PTFE 1 optimized both work analyzed in detail effect content learning-driven prediction. Regression models demonstrated high R-squared values (0.74 density, 0.67 strength, 0.80 relative elongation, 0.79 intensity), explaining up to 80% variability Despite its efficiency, limitations include potential multicollinearity, lack consideration external factors, need validation under real-world conditions. Thus, approach reduces extensive testing, minimizing waste costs, contributing SDG 9. highlights use polymer design, offering data-driven framework rational choice fillers, thereby sustainable industrial practices.

Language: Английский

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Artificial Neural Network Prediction of pH in Nylon 66 Salt Solutions: A Machine Learning Approach

Journal of Applied Polymer Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

ABSTRACT This study investigates the application of artificial neural networks (ANNs) to predict pH Nylon 66 salt solutions. The ANN was optimized by systematically evaluating key parameters—including number hidden layers and neurons, activation function type, optimization algorithm—using a dataset values measured across varying temperatures concentrations. results showed that when 24, neurons in each layer 253, mean squared error (MSE) between predicted experimental data reached 10 −4 . ReLU lbfgs algorithm were identified as most effective for prediction task. demonstrated superior predictive accuracy with determination coefficient ( R 2 ) exceeding 0.99, outperforming traditional first‐order ionization theory. research provides robust method controlling synthesis process highlights potential complex chemical systems.

Language: Английский

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An advanced deep learning approach for energy absorption prediction in porous metals across diverse strain rate scenarios

Computational Materials Science, Journal Year: 2025, Volume and Issue: 253, P. 113862 - 113862

Published: April 5, 2025

Language: Английский

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Machine learning for structure-guided materials and process design

Materials & Design, Journal Year: 2024, Volume and Issue: unknown, P. 113453 - 113453

Published: Nov. 1, 2024

Language: Английский

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A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: April 11, 2024

Abstract Natural language processing techniques enable extraction of valuable information from large amounts published literature for the application data science and technology, i.e. machine learning in field materials science. Nevertheless, automated full-text documents remains a complex task. We propose document-level natural pipeline comprehensive on layered cathode sodium-ion batteries. The enhances entity recognition with contextual supplementary while capturing article structure. Finally, heuristic multi-level relationship algorithm is employed relation to extract experimental parameters performance relationships respectively. successfully extracted dataset containing 5265 records 1747 documents, encompassing essential such as chemical composition, synthesis parameters, electrochemical properties. By implementing our pipeline, we have made significant progress overcoming challenges associated scarcity battery informatics. datasets provide resource further research development materials.

Language: Английский

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