Trend Analysis on the Construction of AI-Based New Material Synthesis Processes and Process Optimization

Abstract

: Traditional new material synthesis predominantly adopts an empirical trial-and-error R&D model, which suffers from deficiencies such as insufficient standardization of process flows, complex parameter coupling mechanisms, prolonged development cycles, and poor preparation stability, making it difficult to meet the demands of high-end new materials for efficient, green, and large-scale industrial production. Artificial intelligence technology provides crucial support for the paradigm innovation of material R&D, effectively breaking through the developmental constraints of traditional synthesis processes. This paper focuses on AI-powered intelligent synthesis of new materials, conducting a comparative analysis of the paradigm differences between traditional and intelligent synthesis approaches. It establishes a comprehensive intelligent synthesis process system for new materials, integrating data processing, intelligent modeling, path design, parameter optimization, closed-loop verification, and process standardization. Centered around five application scenarios—formula optimization, dynamic parameter regulation, streamlined green processes, defect tolerance management, and mass production process adaptation—the study systematically outlines the application pathways of AI in optimizing synthesis processes. Additionally, it identifies existing bottlenecks in intelligent synthesis technology regarding data, models, engineering implementation, and industry standards, while forecasting future trends in technology, industry, and the industrial ecosystem. The research clarifies the development direction of intelligent synthesis technology, offering theoretical reference for the intelligent R&D of new materials and the industrialization upgrade of processes.