Self Driving Labs

From Data to Discovery

Self driving labs, which combine artificial intelligence and machine learning with automated laboratories, are transforming materials discovery, process optimization, and chemical manufacturing – enabling faster, smarter decision-making.

At AM Academy, we provide the tools and insights to help you leverage AI-driven approaches in your work.

Explore more today and unlock the power of machine learning in materials science!

Videos

Overview of AMLearn

Master advanced materials synthesis and automation. In this overview video, explore how AMLearn simplifies complex processes, accelerates research, and empowers you with the knowledge to innovate faster.

Articles

Journal Articles

This article details the first use of AM's platform - combining machine learning and nanomaterials synthesis - to rapidly optimise material performance, discover new structures and improve productivity - all at once.

A Machine Learning - Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

This article introduces a self-optimizing flow chemistry system that uses machine learning to efficiently balance multiple reaction metrics, such as yield and environmental impact, accelerating chemical process development.

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

This article presents a strategy that uses an automated liquid handling, with predictive modeling and active machine learning in a closed-loop system to automate small-scale batch pH adjustments for complex samples, which was enabled through AM's automation framework.

A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

This study integrates a machine learning workflow with a flow chemistry platform to optimize reaction conditions for lithium–halogen exchange reactions, which which was enabled through AM's automation framework.

Webinars

The Basics of Self-Driving Labs: How Machine Learning Accelerates R&D Workflows

Learn from the developer of AMLearn, Dr. Mohammed Jeraal (Lead Engineer), as he distills how different tools, from Bayesian optimisation to neural networks are implemented to tackle real-world challenges in chemical process optimisation.