Prompt engineering of GPT-4 for chemical research: what can/cannot be done?

Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis

“…Chemistry education and teacher education will benefit from AI chatbots similarly to any other domain. Learners can refine information to knowledge via learning discussions, check facts, and prompt definitions for concepts [40,62]. However, one must be aware of the limitations of LLMs and analyze or triangulate the generated information before using it.…”

“…AI chatbot-assisted information-seeking has also arrived in the field of chemistry. There are already some peer-reviewed articles and preprints that offer preliminary benchmarks of the possibilities and limitations of AI chatbots for chemistry research [61,62] and learning [9,[63][64][65][66]. Preliminary results indicate that AI chatbots can scaffold learning by enhancing chemical information seeking significantly.…”

“…They offer an interface to dynamic personalized learning discussions that support the development of conceptual knowledge and foster critical thinking [63], such as analyzing and evaluating, which are higher-order cognitive skills (HOCSs) [67]. For example, the user can prompt answers for basic-level questions, acquire new chemical insights through learning discussions, or obtain support to plan chemical experiments [62,68]. This enables the building of personal learning environments and provides equal possibilities for all to expand their zone of proximal development (ZPD) [69][70][71][72], which supports sustainable education goals (SDG4) [73].…”

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

“…37,[44][45][46][47][48][49][50][51] More recently, large language models (LLMs) coupled with prompt engineering have gained increasing popularity in automating scientific text mining for chemical information due to their more user-friendly nature. [52][53][54][55][56] A crucial aspect of text mining for classifying text based on chemical domain involves topic modeling, 57 which is the identification of underlying themes in large sets of scientific text. For tasks of this nature, prompt engineering typically requires a priori definition of the possible latent topics.…”

Large Language Models for Inorganic Synthesis Predictions

“…Pretrained LLMs have been investigated for a wide variety of chemical tasks, , such as extracting structured data from the literature, − writing numerical simulation software, and education . LLM-based workflows have been used to plan syntheses of organic molecules , and metal–organic frameworks (MOFs). ,− Recent work has benchmarked materials science , and general chemical knowledge − of existing LLMs, and there are efforts to develop chemistry/materials-specific LLMs. , Fine-tuning LLMs on modest amounts of data improves performance for specific tasks, while still taking advantage of the general pretraining to provide basic symbol interpretation and output formatting guidance. Chemical applications of LLM fine-tuning have addressed property regression and classification of organic molecules, − and been used to improve the water-harvesting behavior of MOFs …”