# Synergizing Chemical and AI Communities for Advancing Laboratories of the Future

## TL;DR

This paper explores how AI and machine learning can transform modern chemical labs by speeding up experiments and data analysis.

## Contribution

The paper introduces AI agents and ML models to streamline chemical research through predictive modeling and automation.

## Key findings

- ML models can accelerate the design-build-test-learn cycle in chemical experiments.
- AI agents can assist researchers in acquiring background knowledge and accelerating discovery.
- Case studies demonstrate ML and AI reducing manual experiments and data analysis.

## 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.