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AutoMat: Automated materials discovery for electrochemical systems

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Abstract

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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Acknowledgments

The information, data, or work presented herein were funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), US Department of Energy, under Award No. DE-AR0001211. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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  1. These authors contributed equally to this work.

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, USA

    Emil Annevelink, Rachel Kurchin, Lance Kavalsky, Valentin Sulzer, Shang Zhu, Jiankun Pu, Adarsh Dave, Hongyi Lin & Venkatasubramanian Viswanathan

  2. Citrine Informatics, Redwood City, USA

    Eric Muckley, Vinay I. Hegde, James Saal & Bryce Meredig

  3. Northeastern University, Boston, USA

    David Farina

  4. Massachusetts Institute of Technology, Cambridge, USA

    Matthew Johnson & Alan Edelman

  5. Julia Computing, Somerville, USA

    Dhairya Gandhi, Christopher Rackauckas & Viral Shah

  6. Deep Forest Sciences, Fremont, USA

    Bharath Ramsundar

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  1. Rachel Kurchin
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Correspondence to Venkatasubramanian Viswanathan.

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Annevelink, E., Kurchin, R., Muckley, E. et al. AutoMat: Automated materials discovery for electrochemical systems. MRS Bulletin 47, 1036–1044 (2022). https://doi.org/10.1557/s43577-022-00424-0

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