Editorial
Introduction to “Accelerate Conference 2022”
Keith A. Brown, Fedwa El Mellouhi, and Claudiane Ouellet-Plamondon introduce the themed collection on the 2022 Accelerate Conference.
Perspective
Autonomous cementitious materials formulation platform for critical infrastructure repair
Autonomous systems can greatly increase the efficiency and speed of the development of cement materials for infrastructure repair.
Perspective
What is missing in autonomous discovery: open challenges for the community
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery.
Perspective
The laboratory of Babel: highlighting community needs for integrated materials data management
In this perspective, we highlight the need to integrate data management infrastructure across scales to best take advantage of advancements in automated and autonomous experimental methods in materials science.
Paper
A human-in-the-loop approach for visual clustering of overlapping materials science data
Our divide and conquer approach to enable the visual split or merge decision for each pair of Gaussian pairs.
Paper
Towards a modular architecture for science factories
Advances in robotic automation, high-performance computing, and artificial intelligence encourage us to propose large, general-purpose science factories with the scale needed to tackle large discovery problems and to support thousands of scientists.
Paper
Robotically automated 3D printing and testing of thermoplastic material specimens
An automated platform to explore parameters for pellet-based 3D printing and characterize the samples for weight, impact strength and surface quality.
Paper
Driving school for self-driving labs
Self-driving labs benefit from occasional and asynchronous human interventions. We present a heuristic framework for how self-driving lab operators can interpret progress and make changes during a campaign.
Paper
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
We used synthetically generated crystals to train ResNet-like models to enhance the prediction of space groups from ICSD powder X-ray diffractograms. The results show improved generalization to unseen structure types compared to previous approaches.
Paper
A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform
A sonochemical Materials Acceleration Platform was implemented to synthesize CdSe nanocrystals under 625 unique conditions (in triplicate) in less than 6 weeks. The modularity of the workflow is adaptable to a variety of applications.
Paper
A fully automated platform for photoinitiated RAFT polymerization
The use of robotic instrumentation and Python scripts allows for fully automated and robust combinatorial polymer synthesis.
About this collection
This new themed collection represents a collaboration between the editors of Digital Discovery and the Acceleration Consortium, organisers of the Accelerate Conference. The goal of the conference was 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, Guest Edited by Prof. Keith A. Brown (Boston University, USA), Prof. Fedwa El Mellouhi (Hamad Bin Khalifa University, Qatar), and Prof. Claudiane Ouellet-Plamondon (École de Technologie Supérieure, Canada), features contributions that cover various aspects of this process, whether specifically presented at the conference or not.
Examples include, realization of new SDLs; fundamental studies of the operation of SDLs; sustainable, resilient, low carbon, materials and chemical discoveries made using SDLs.