Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915424588791808 |
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| author | Ding, Hu Hua, Pengxiang Huang, Zhen |
| author_facet | Ding, Hu Hua, Pengxiang Huang, Zhen |
| contents | The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities Ding, Hu Hua, Pengxiang Huang, Zhen Chemical Physics Artificial Intelligence Machine Learning The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention. |
| title | Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities |
| topic | Chemical Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.17456 |