Machine Learning for Self-Driving Laboratories
This Special Topic focuses on the transformative impact of machine learning in self-driving laboratory environments and closed-loop experimentation systems. It aims to highlight the development, implementation, and optimization of advanced machine learning algorithms that drive autonomous decision-making, predictive modeling, and adaptive control in lab automation. Emphasizing interdisciplinary research, the topic explores how integrating machine learning methodologies can significantly enhance the efficiency, accuracy, and productivity of experimental science. By fostering innovations bringing artificial intelligence into the physical world, this topic seeks to advance the frontiers of research across diverse scientific disciplines, demonstrating the critical role of machine learning in modern laboratory automation and scientific research.
Hardware AI through (but not limited to):
- Active Learning Algorithms Development
- Benchmarking of Machine Learning Algorithms
- Data Management and Workflow Development
- Multi-Objective and Multi-Fidelity Learning
- Control Algorithms and Orchestration in Lab Automation
- AI for Scientific Robots
- Machine Learning for Molecular and Materials Synthesis
- Data Science for Multi-modal Characterization
- Large Language Models for Scientific Discovery
- Human-Machine Interactions in Self-Driving Laboratories
- Optimization of Closed-Loop Experimentation System
Guest Editors
Shijing Sun, University of Washington
Jie Xu, Argonne National Lab
Benji Maruyama, Air Force Research Laboratory (AFRL)
Mahshid Ahmadi, University of Tennessee
Martin Seifrid, North Carolina State University
Yang Cao, University of Toronto
How to Submit
- Please submit through the online submission system.
- Under manuscript type → select Article, as appropriate.
- Under manuscript information → Title/Abstract → select “Invited Submission: No”
- Under manuscript information → Manuscript classification → select Special Topic: “Machine Learning for Self-Driving Laboratories”
Please note that papers will be published as normal when they are ready in a regular issue of the journal and will populate on a virtual collection page within a few days of publication. Inclusion in the collection will not cause delay in publication.