Opinion
Autonomous laboratories for accelerated materials discovery: a community survey and practical insights
We share the results of a survey on automation and autonomy in materials science labs, which highlight a variety of researcher challenges and motivations. We also propose a framework for levels of laboratory autonomy from L0 to L5.
Tutorial Review
Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept
Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.
Communication
Stability and transferability of machine learning force fields for molecular dynamics applications
We benchmark GNN models for MLFF-MD and introduce new metrics beyond conventional force and energy errors. Our approach, demonstrated on lithium-ion conductors, aims to broaden ionic conductor screening for batteries.
Accepted Manuscript -
Paper
Leveraging GPT-4 to transform chemistry from paper to practice
Paper
Pellet dispensomixer and pellet distributor: open hardware for nanocomposite space exploration via automated material compounding
We present do-it-yourself instruments that can be both adopted and adapted to fit your self-driving lab.
About this collection
This new themed collection, Guest Edited by Prof. Janine George, Prof. Claudiane Ouellet-Plamondon and Prof. Kristofer Reyes, represents a collaboration between the editors of Digital Discovery and the Acceleration Consortium, organisers of the Accelerate Conference. The goal of the conference is to explore the power of self-driving labs (SDLs), which combine AI, automation, and advanced computing to accelerate materials and molecular discovery.
This themed collection of articles features work from the contributors to the 2023 and 2024 Accelerate Conferences, encompassing papers that cover any aspect of this process