  Large-scale electrification is vital to addressing the climate crisis, but
several scientific and technological challenges remain to fully electrify both
the chemical industry and transportation. In both of these areas, new
electrochemical materials will be critical, but their development currently
relies heavily on human-time-intensive experimental trial and error and
computationally expensive first-principles, meso-scale and continuum
simulations. We present an automated workflow, AutoMat, that accelerates these
computational steps by introducing both automated input generation and
management of simulations across scales from first principles to continuum
device modeling. Furthermore, we show how to seamlessly integrate
multi-fidelity predictions such as machine learning surrogates or automated
robotic experiments "in-the-loop". The automated framework is implemented with
design space search techniques to dramatically accelerate the overall materials
discovery pipeline by implicitly learning design features that optimize device
performance across several metrics. We discuss the benefits of AutoMat using
examples in electrocatalysis and energy storage and highlight lessons learned.
