General-Purpose Models for the Chemical Sciences: LLMs and Beyond
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917099191926784 |
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| author | Alampara, Nawaf Aneesh, Anagha Ríos-García, Martiño Mirza, Adrian Schilling-Wilhelmi, Mara Aghajani, Ali Asghar Sun, Meiling Prastalo, Gordan Jablonka, Kevin Maik |
| author_facet | Alampara, Nawaf Aneesh, Anagha Ríos-García, Martiño Mirza, Adrian Schilling-Wilhelmi, Mara Aghajani, Ali Asghar Sun, Meiling Prastalo, Gordan Jablonka, Kevin Maik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | General-Purpose Models for the Chemical Sciences: LLMs and Beyond Alampara, Nawaf Aneesh, Anagha Ríos-García, Martiño Mirza, Adrian Schilling-Wilhelmi, Mara Aghajani, Ali Asghar Sun, Meiling Prastalo, Gordan Jablonka, Kevin Maik Machine Learning Materials Science Chemical Physics 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. |
| title | General-Purpose Models for the Chemical Sciences: LLMs and Beyond |
| topic | Machine Learning Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2507.07456 |