Synergizing chemical and AI communities for advancing laboratories of the future

• URL: http://arxiv.org/abs/2510.16293v1
• Date: Sat, 18 Oct 2025 01:36:27 GMT
• Title: Synergizing chemical and AI communities for advancing laboratories of the future
• Authors: Saejin Oh, Xinyi Fang, I-Hsin Lin, Paris Dee, Christopher S. Dunham, Stacy M. Copp, Abigail G. Doyle, Javier Read de Alaniz, Mengyang Gu,
• Abstract summary: Machine learning (ML) approaches trained on experimental data can substantially accelerate the conventional design-build-test-learn process.<n>This article aims to help chemists understand and begin to adopt ML predictive models for a variety of laboratory tasks.<n>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.
• Score: 0.939037015153925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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