These capabilities have made them promising tools in research and education . From the emergence of the most capable models , researchers have derived and built chemistry-specific applications tackling predictive modeling , optimization , assistance with experimental design , and automatic experimentation , among others . These models also encode large amounts of chemistry knowledge, showing that they are capable of passing college-level exams and answering basic chemical questions , as well as writing code with chemistry-relevant applications . These works highlight the potential for future tools powered by LLMs, and how chemists will, similar to the adoption of computers, embrace them. Following these advances in LLMs and more generally, generative AI, more attention has been paid to education and learning in chemistry . Promising potential applications have been discussed and implemented , that highlight promising opportunities while also discussing potential negative effects of already publicly-accessible tools . Indeed, authors cite issues such as threats to the traditional essay writing for evaluation of understanding , plagiarism concerns , and more broadly the effect of Generative AI on student's learning and understanding .