AutoMat: Automated materials discovery for electrochemical systems

E Annevelink and R Kurchin and E Muckley and L Kavalsky and VI Hegde and V Sulzer and S Zhu and JK Pu and D Farina and M Johnson and D Gandhi and A Dave and HY Lin and A Edelman and B Ramsundar and J Saal and C Rackauckas and VR Shah and B Meredig and V Viswanathan, MRS BULLETIN (2022).

DOI: 10.1557/s43577-022-00424-0

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, mesoscale, and continuum simulations. We present an automated workflow, AutoMat, which 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.

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