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.

翻译：自动化实验设施的发展和实验数据的数字化为化学实验室的根本性进步带来了诸多机遇。由于许多实验室任务涉及预测和理解先前未知的化学关系，基于实验数据训练的机器学习方法能够显著加速传统的设计-构建-测试-学习循环。本展望文章旨在帮助化学研究者理解并开始将机器学习预测模型应用于各类实验室任务，包括实验设计、合成优化与材料表征。此外，本文介绍了基于大语言模型的人工智能助手如何帮助研究者获取化学或数据科学领域的背景知识，并加速发现流程的多个环节。我们通过三个不同领域的案例研究，阐明如何利用机器学习模型与人工智能助手来减少耗时的实验操作与人工数据分析。最后，我们强调了当前存在的挑战，这些挑战需要实验与计算科学界持续开展协同攻关方能解决。