Data‐Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile–Butadiene–Styrene/Carbon Nanotube Binary Composites

Electrically Conductive Nanocomposite Materials In article number 2200399, Boseok Kang, Jong Hwan Ko, and co‐workers have developed an artificial intelligence (AI) system predicting various physical properties of carbon nanotube (CNT) / acrylonitrile‐butadiene‐styrene (ABS) nanocomposites by training the model with data collected from online literature. The AI model not only predicts physical properties of the nanocomposites when manufacturing information is given, but also conversely determines manufacturing conditions for the nanocomposites with desired physical properties. This demonstrates that AI‐supported material development systems would be expanded into various applications in composite manufacturing.