arxiv
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.
For details:
Synergizing chemical and AI communities for advancing laboratories of the future
Saejin Oh 1,*, Xinyi Fang 2,*, I-Hsin Lin 3, Paris Dee 4, Christopher S. Dunham 1, Stacy M. Copp 3,5,6,7, Abigail G. Doyle 4, Javier Read de Alaniz 8, Mengyang Gu 2
1) BioPACIFIC Materials Innovation Platform, University of California, Santa Barbara, Santa Barbara, CA, 93106 USA
2) Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA, 93106 USA
3) Department of Materials Science and Engineering, University of California, Irvine, CA, 92697 USA
4) Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095 USA
5) Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, 92697 USA
6) Department of Physics and Astronomy, University of California, Irvine, CA, 92697 USA
7) Department of Chemistry, University of California, Irvine, CA, 92697 USA
8) Department of Chemistry and Biochemistry, University of California, Santa Barbara, Santa Barbara, CA, 93106 USA
*These authors contributed equally to this work.
arxiv
https://doi.org/10.48550/arXiv.2510.16293
