MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641049976 A) filed by K. M. Eazhil; Dr. Pankaj Kumar; Manoj Kumar R; E. Janani; Ramesh Krishnan; J S Javith Saleem; and Dr. Palanivendhan on April 20, 2026, for Quantum Inspired Explainable Ai Framewok For Accelerated Material Discovery And Process Optimization.

Inventors include K. M. Eazhil; Dr. Pankaj Kumar; Manoj Kumar R; E. Janani; Ramesh Krishnan; J S Javith Saleem; and Dr. Palanivendhan.

The application for the patent was published on June 12, 2026, under issue no. 24/2026.

Abstract: ABSTRACT The rapid advancement of materials science demands innovative computational approaches to accelerate discovery and optimize industrial processes. This work proposes a QuantumInspired Explainable Artificial Intelligence (QXAl) framework that integrates quantuminspired optimization techniques with advanced machine learning models to efficiently explore high-dimensional material design spaces. By leveraging concepts such as probabilistic state representation and parallel search mechanisms, the framework enables faster identification of novel materials with enhanced properties while significantly reducing experimental costs and time. Additionally, the system incorporates multi-stage optimization strategies to refine material characteristics and improve process parameters, leading to higher efficiency, productivity, and performance in manufacturing environments. A key contribution of this framework is the seamless integration of explainable AI techniques to ensure transparency and interpretability in decision-making. Through feature attribution, interpretable modeling, and visualization tools, the system provides meaningful insights into the relationships between input variables and predicted outcomes. An adaptive feedback mechanism further enhances the model by continuously learning from experimental and simulation data, ensuring robustness and accuracy over time. The proposed approach not only accelerates innovation in material discovery but also supports sustainable development by optimizing resource utilization and minimizing waste, making it a practical and scalable solution for real-world industrial applications.

Disclaimer: Curated by HT Syndication.