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General-purpose models (GPMs), including large language models, show promise for chemical sciences. These AI models can handle diverse, small datasets, accelerating discovery despite current prototype phases.

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Area of Science:

  • Chemical Sciences
  • Artificial Intelligence
  • Data Science

Background:

  • Data-driven techniques offer significant potential for advancing chemical sciences.
  • Conventional machine learning struggles with the unique challenges of chemical data, such as small, diverse, and fuzzy datasets.

Purpose of the Study:

  • To review the fundamental principles of general-purpose models (GPMs).
  • To explore recent and emerging applications of GPMs in the chemical sciences.

Main Methods:

  • Review of general-purpose models (GPMs), including large language models.
  • Analysis of GPM capabilities in handling diverse, low-data-volume chemical datasets.
  • Examination of current and potential applications in chemical research.

Main Results:

  • GPMs demonstrate the ability to perform tasks without direct training and adapt to various data formats.
  • These models can effectively operate with limited amounts of data, a common challenge in chemistry.
  • Emerging applications in chemical sciences are showing promising results, though many are in early development.

Conclusions:

  • GPMs represent a new class of AI models with the potential to revolutionize chemical sciences.
  • The flexibility and data-handling capabilities of GPMs address key limitations of traditional machine learning in this field.
  • Continued development and interest are expected to mature GPM applications, accelerating scientific discovery in chemistry.