AI-Accelerated Development of Gradient Polymer Nanocomposite Thin Films

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Year : 2026 | Volume : 14 | 02 | Page :
    By

    M. Nithin srinivas,

  • S. N. Padhi,

  1. M.Tech Student, Department of Mechanical Engineering, Koneru Laksmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India
  2. Professor, Department of Mechanical Engineering, Koneru Laksmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India

Abstract

Gradient polymer nanocomposite thin films are an active field of materials research due to the fact that it enables scientists to de-facto regulate the optical, electrical, and mechanical properties of a film by merely altering its composition on a layer-by-layer basis. This type of control opens the gate to the improved flexible electronics, long lasting protective coats, and the new smart gadgets. The problem is, though, that it is a tedious process of trial and error to develop these gradients. It can be that researchers need to test dozens of experimental combinations before discovering the appropriate mixture of materials. This paper presents an AI-based solution that will help speed up that process significantly. We combine high-throughput film fabrication, machine learning models, and automated optimization tools in our framework to assist with the task of designing gradient nanocomposite films in a fraction of the standard time. We constructed a database of 420 movies where the concentrations of silver nanoparticles and graphene oxide are at various proportions, with a thickness range of 100150 to 500 nanometers thick. Based on this information, convolutional neural networks were trained to examine microscopy images and property measurements, with 94 percent accuracy depending on the prediction of the performance of a film. Then we combined genetic algorithms with surrogate models so that we could rapidly find the optimal pattern of gradients. This optimization step identified promising designs in only 48 hours 95 percent time savings versus the normal research techniques. The optimized films recorded remarkable progress when subjected to experimentation that was characterized by an increase in conductivity, transparency as well as much greater flexibility. In general, this machine-assisted AI-based approach transforms the principles of developing thin films to be less of a guesswork and more of a forecast-based design.

Keywords: Gradient thin film, polymer nanocomposites, artificial intelligence, machine learning, high-throughput synthesis, flexible electronics.

How to cite this article:
M. Nithin srinivas, S. N. Padhi. AI-Accelerated Development of Gradient Polymer Nanocomposite Thin Films. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
M. Nithin srinivas, S. N. Padhi. AI-Accelerated Development of Gradient Polymer Nanocomposite Thin Films. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239803


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Ahead of Print Subscription Original Research
Volume 14
02
Received 29/12/2025
Accepted 05/02/2026
Published 07/04/2026
Publication Time 99 Days
29

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