Synergizing chemical and AI communities for advancing laboratories of the future
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909854319247360 |
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| author | Oh, Saejin Fang, Xinyi Lin, I-Hsin Dee, Paris Dunham, Christopher S. Copp, Stacy M. Doyle, Abigail G. de Alaniz, Javier Read Gu, Mengyang |
| author_facet | Oh, Saejin Fang, Xinyi Lin, I-Hsin Dee, Paris Dunham, Christopher S. Copp, Stacy M. Doyle, Abigail G. de Alaniz, Javier Read Gu, Mengyang |
| contents | The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories. As many laboratory tasks involve predicting and understanding previously unknown chemical relationships, machine learning (ML) approaches trained on experimental data can substantially accelerate the conventional design-build-test-learn process. This outlook article aims to help chemists understand and begin to adopt ML predictive models for a variety of laboratory tasks, including experimental design, synthesis optimization, and materials characterization. Furthermore, this article introduces how artificial intelligence (AI) agents based on large language models can help researchers acquire background knowledge in chemical or data science and accelerate various aspects of the discovery process. We present three case studies in distinct areas to illustrate how ML models and AI agents can be leveraged to reduce time-consuming experiments and manual data analysis. Finally, we highlight existing challenges that require continued synergistic effort from both experimental and computational communities to address. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synergizing chemical and AI communities for advancing laboratories of the future Oh, Saejin Fang, Xinyi Lin, I-Hsin Dee, Paris Dunham, Christopher S. Copp, Stacy M. Doyle, Abigail G. de Alaniz, Javier Read Gu, Mengyang Applications Artificial Intelligence Machine Learning The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories. As many laboratory tasks involve predicting and understanding previously unknown chemical relationships, machine learning (ML) approaches trained on experimental data can substantially accelerate the conventional design-build-test-learn process. This outlook article aims to help chemists understand and begin to adopt ML predictive models for a variety of laboratory tasks, including experimental design, synthesis optimization, and materials characterization. Furthermore, this article introduces how artificial intelligence (AI) agents based on large language models can help researchers acquire background knowledge in chemical or data science and accelerate various aspects of the discovery process. We present three case studies in distinct areas to illustrate how ML models and AI agents can be leveraged to reduce time-consuming experiments and manual data analysis. Finally, we highlight existing challenges that require continued synergistic effort from both experimental and computational communities to address. |
| title | Synergizing chemical and AI communities for advancing laboratories of the future |
| topic | Applications Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.16293 |