The
pursuit of designing smart and functional materials is of paramount
importance across various domains, such as material science, engineering,
chemical technology, electronics, biomedicine, energy, and numerous
others. Consequently, researchers are actively involved in the development
of innovative models and strategies for material design. Recent advancements
in analytical tools, experimentation, and computer technology additionally
enhance the material design possibilities. Notably, data-driven techniques
like artificial intelligence and machine learning have achieved substantial
progress in exploring various applications within material science.
One such approach, ChatGPT, a large language model, holds transformative
potential for addressing complex queries. In this article, we explore
ChatGPT’s understanding of material science by assigning some
simple tasks across various subareas of computational material science.
The findings indicate that while ChatGPT may make some minor errors
in accomplishing general tasks, it demonstrates the capability to
learn and adapt through human interactions. However, issues like output
consistency, probable hidden errors, and ethical consequences should
be addressed.