General-Purpose Models for the Chemical Sciences: LLMs and Beyond
Abstrak
Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years.
Topik & Kata Kunci
Penulis (9)
Nawaf Alampara
Anagha Aneesh
Martiño Ríos-García
Adrian Mirza
Mara Schilling-Wilhelmi
Ali Asghar Aghajani
Meiling Sun
Gordan Prastalo
Kevin Maik Jablonka
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓