Paper
Programmable aerosol chemistry coupled to chemical imaging establishes a new arena for automated chemical synthesis and discovery
Parallel, inhomogeneous and inherently stochastic, the aerosol medium holds exceptional promise in the unfolding era of digitisation as a platform for synthesis and discovery tailored to programmable execution and rapid computational analysis.
Paper
A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals
We present a self-driving fluidic lab with a modular hardware and software for data-driven synthesis optimization of eco-friendly colloidal semiconductor nanocrystals.
Paper
ACES-GNN: can graph neural network learn to explain activity cliffs?
We introduce an activity-cliff explanation supervision training strategy to enhance both predictivity and explainability for graph neural networks in molecular structure and activity relationship modeling.
About this collection
Digital Discoveryis proud to present this collection of invited contributions from early career researchers who are making significant contributions to machine learning, robotics and AI for the acceleration of discovery. Congratulations to all of the featured researchers!