Topic: AI-Driven Synthesis and Optimization of Advanced Functional Materials

The integration of artificial intelligence (AI) into the field of chemical synthesis is reshaping the way functional materials are designed, discovered, and optimized. With increasing data availability, improved algorithmic accuracy, and the rise of automated and autonomous laboratories, AI is becoming a valuable tool to accelerate material innovation while reducing trial-and-error in synthesis and process development.

This Special Issue of AI-Driven Synthesis and Optimization of Advanced Functional Materials aims to provide a focused platform for recent advances in AI-assisted strategies for the rational design and synthesis of advanced functional materials. We welcome contributions that highlight how machine learning, data-driven modeling, or AI-guided experimentation contribute to understanding structure–function relationships, predicting synthetic pathways, and optimizing material properties for energy, environmental, biomedical, and electronic applications.

The issue seeks to foster interdisciplinary discussion between chemists, materials scientists, and data scientists. Through this collection, we hope to highlight both the capabilities and current limitations of AI in synthesis science, and to encourage new methodologies that integrate chemical intuition with computational intelligence.

Topics of interest include, but are not limited to:

● Machine learning models for predicting reaction outcomes or synthetic accessibility;

● AI-assisted discovery and optimization of catalytic systems;

● Data-driven design of porous, hybrid, or polymeric functional materials;

● Integration of AI with automated or high-throughput synthesis platforms;

● Inverse design strategies for materials with targeted properties;

● Explainable AI approaches for interpreting chemical data and mechanisms;

● Benchmark datasets, algorithm validation, and reproducibility in AI-chemistry research.

Artificial intelligence; machine learning; functional materials; synthesis optimization; inverse design; data-driven discovery; high-throughput experimentation; catalytic systems