Integration of response surface methodology (RSM), machine learning (ML), and artificial intelligence (AI) for enhancing properties of polymeric nanocomposites‐A review DOI Creative Commons
Yasir Raza, Hassan Raza, Arslan Ahmed

et al.

Polymer Composites, Journal Year: 2025, Volume and Issue: unknown

Published: May 12, 2025

Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for optimization fabrication enhancement properties polymeric nanocomposites. It analyzes recent accomplishments, methodologies, future possibilities in this interdisciplinary field. Polymers their nanocomposites are garnering attention because cost‐effectiveness, biodegradability, non‐toxicity. Polymeric have been employed several technical applications; nevertheless, restricted mechanical, electrical, thermal impeded extensive use. Numerous additives, including clay, fiber, two‐dimensional materials such as graphene or MoS 2 , were extensively nanofillers to enhance qualities. The effects filler concentration thoroughly examined by conventional approaches; however, via statistical techniques may be more suitable. method produces accurate results with a reduced number tests. Diverse techniques, Taguchi RSM, alongside ML algorithms, can ascertain optimal concentration, type, method, characterization, process parameters properties, manufacturing, efficiency polymers polymer‐based superior compared methods. Nonetheless, ML/AI also utilized attain additional improvements requisite thermal, electrochemical properties. Recent advancements emphasized, use is proposed progress. Highlights Summarized Presented process, production, additive various polymers. ML‐based efficiency. Future directions: AI improve nanocomposite

Language: Английский

Rationally Designed High-Temperature Polymer Dielectrics for Capacitive Energy Storage: An Experimental and Computational Alliance DOI

Pritish S. Aklujkar,

Rishi Gurnani,

Pragati Rout

et al.

Progress in Polymer Science, Journal Year: 2025, Volume and Issue: unknown, P. 101931 - 101931

Published: Feb. 1, 2025

Language: Английский

Citations

1
Insights into Synthesis and Optimization Features of Reverse Osmosis Membrane Using Machine Learning DOI Open Access
Weimin Gao, Guang Wang, Junguo Li

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(4), P. 840 - 840

Published: Feb. 14, 2025

Reverse osmosis membranes have been predominantly made from aromatic polyamide composite thin-films, although significant research efforts dedicated to discovering new materials and synthesis technologies enhance the water-salt selectivity of in past decades. The lack breakthroughs is partly attributed limited comprehensive understanding relationships between membrane features their performance. Insights into intrinsic reverse (RO) based on metadata were obtained using explainable artificial intelligence understand unify efforts. related chemistry, structure, modification methods, performance RO derived dataset more than 1000 membranes. Seven machine learning (ML) models constructed evaluate performances, applicability for tasks was assessed metadata. contribution analyzed, ranking importance revealed. This work holds promise analysis, evaluating against state art developing an inverse design strategy discovery high-performance

Language: Английский

Citations

0
Tuning membrane surface wetting behaviour via dual-nanomaterial functionalization for efficient water purification DOI
Shuai Liang, Zhibo Ma,

Zhonghua Fan

et al.

Journal of Membrane Science, Journal Year: 2025, Volume and Issue: unknown, P. 123970 - 123970

Published: March 1, 2025

Language: Английский

Citations

0
Machine learning in membrane science: Bridging materials, structures, and performance for next-generation membrane design DOI
Lijun Liang, Dan Lu, Yuhuan Qin

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: unknown, P. 133091 - 133091

Published: April 1, 2025

Language: Английский

Citations

0
PLGA-based long-acting injectable (LAI) formulations DOI
Kinam Park

Journal of Controlled Release, Journal Year: 2025, Volume and Issue: unknown, P. 113758 - 113758

Published: April 1, 2025

Language: Английский

Citations

0
Advances in Porous Materials for Transuranic Element Separation DOI

Li-Ying Wang,

Jipan Yu,

Zhirong Liu

et al.

Acta Chimica Sinica, Journal Year: 2025, Volume and Issue: 83(4), P. 401 - 401

Published: Jan. 1, 2025

Language: Английский

Citations

0
Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework DOI Creative Commons
Bokai Liu, Pengju Liu, Weizhuo Lu

et al.

International journal of mechanical system dynamics, Journal Year: 2025, Volume and Issue: unknown

Published: May 20, 2025

ABSTRACT The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements predictive modeling properties. However, the lack interpretability machine learning (ML)‐based informatics presents a major barrier its practical adoption. This study proposes novel quantitative computational framework that integrates ML models with explainable (XAI) techniques enhance both accuracy property prediction. systematically incorporates structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, real‐world deployment. It is validated through representative case on prediction high‐performance concrete (HPC) compressive strength, utilizing comparative analysis such as Random Forest, XGBoost, Support Vector Regression (SVR), Deep Neural Networks (DNNs). results demonstrate XGBoost achieves highest (), while SHAP (Shapley Additive Explanations) LIME (Local Interpretable Model‐Agnostic provide detailed insights into importance interactions. Additionally, deployment trained cloud‐based Flask‐Gunicorn API enables real‐time inference, ensuring scalability accessibility for industrial research applications. proposed addresses key limitations existing approaches by integrating advanced techniques, handling nonlinear interactions, providing scalable strategy. contributes development interpretable deployable AI‐driven informatics, bridging gap between data‐driven predictions fundamental science principles.

Language: Английский

Citations

0
Integration of response surface methodology (RSM), machine learning (ML), and artificial intelligence (AI) for enhancing properties of polymeric nanocomposites‐A review DOI Creative Commons
Yasir Raza, Hassan Raza, Arslan Ahmed

et al.

Polymer Composites, Journal Year: 2025, Volume and Issue: unknown

Published: May 12, 2025

Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for optimization fabrication enhancement properties polymeric nanocomposites. It analyzes recent accomplishments, methodologies, future possibilities in this interdisciplinary field. Polymers their nanocomposites are garnering attention because cost‐effectiveness, biodegradability, non‐toxicity. Polymeric have been employed several technical applications; nevertheless, restricted mechanical, electrical, thermal impeded extensive use. Numerous additives, including clay, fiber, two‐dimensional materials such as graphene or MoS 2 , were extensively nanofillers to enhance qualities. The effects filler concentration thoroughly examined by conventional approaches; however, via statistical techniques may be more suitable. method produces accurate results with a reduced number tests. Diverse techniques, Taguchi RSM, alongside ML algorithms, can ascertain optimal concentration, type, method, characterization, process parameters properties, manufacturing, efficiency polymers polymer‐based superior compared methods. Nonetheless, ML/AI also utilized attain additional improvements requisite thermal, electrochemical properties. Recent advancements emphasized, use is proposed progress. Highlights Summarized Presented process, production, additive various polymers. ML‐based efficiency. Future directions: AI improve nanocomposite

Language: Английский

Citations

0