Open-Science Materials Acceleration Platforms for Clean Energy Material Design Spaces

Abstract

Conventional materials synthesis schemes can be labor and time-intensive, which significantly impedes the pace of new materials discovery and their applications. To achieve innovative solutions to global challenges, such as climate change and an ever-growing sustainable energy demand, a new paradigm of science movement arose in the past five years to adapt traditional materials science workflows to ones with the potential to accelerate the pace of materials discovery. This novel approach to science has been called “Materials Acceleration Platforms” (MAPs) or “Self-driving Laboratories” (SDLs) and it relies on autonomous robotic systems designed to conduct scientific experiments and research with minimal human intervention. However, new initiatives of MAPs are still too costly, and their rigid design limits their implementation. It is also essential to acknowledge and address the diverse needs and challenges of different scientific fields. In this context, open-hardware principles have allowed the use of laboratory automation to be more accessible and more easily implemented for a variety of applications. In this work, workflows with various levels of automation of experimental and computational tasks are presented implementing a combination of off-the-shelf open-source robotic platforms, high-throughput, small scale characterization tools, and custom open-hardware solutions. A semi-automated protocol was designed for the synthesis, physical and electrochemical characterization of novel electrolytes based on deep eutectic solvents and organic redox active molecules for use in electrochemical storage systems. Next, a human-in-the-loop SDL was developed to explore a large design space for the investigation of CdSe nanoparticles’ optical properties at a fraction of the time compared to traditional techniques. This work implements a repurposed multi-tool, open-hardware 3D printer platform, Jubilee, reconfigured for sonochemical processing. Furthermore, data-driven methods were also used to obtain a holistic view of the space and learn trends in the data based on all variables tested. Finally, a new python-based control library for Jubilee is presented, along with the broadening of tool library with new synthesis, processing, and characterization tools. These efforts enabled the creation of closed-loop workflows, such as the benchmarking protocol for SDLs, an autonomous color-mixing problem. These tools hold immense scientific and educational value for both new materials discovery and more interdisciplinary skill development. The increased access through low-costs solution will broaden the application space of this new science paradigm, empowering a larger number of (materials) scientists to take advantage of the precision of automation, and enabling automation tools to be included in hands-on educational curricula.