A user's guide to your first self-driving liquid handling lab.

Area of Science:

• Laboratory automation
• Scientific experimentation
• Machine learning in science

Background:

• Traditional experimental methods face challenges in optimization and data collection due to complex variable interactions.
• Trial-and-error approaches are increasingly inefficient for modern scientific research.
• High costs and steep learning curves limit the adoption of advanced laboratory automation.

Purpose of the Study:

• To democratize access to self-driving laboratory (SDL) methodologies.
• To provide comprehensive computational and hardware implementation guidance for researchers.
• To enable self-driven experimental workflows through accessible technology.

Main Methods:

• Integration of machine learning (ML) and active learning (AL) with laboratory automation.
• Development of open-source, low-cost liquid handling platforms.
• Provision of detailed tutorials and build guides for practical implementation.

Main Results:

• Comprehensive coverage of computational skills and hardware for SDL workflows.
• Availability of practical templates for researchers adopting SDL methodologies.
• Empowerment of researchers to implement self-driven experimental approaches.

Conclusions:

• Self-driving laboratories (SDL) offer a powerful approach to enhance experimental productivity and data quality.
• Democratizing access through open-source, low-cost platforms lowers barriers to adopting advanced automation.
• This work provides a practical roadmap for researchers to build and implement their own SDL systems.