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    The Energy Storage and Distributed Resources Division (ESDR) works on developing advanced batteries and fuel cells for transportation and stationary energy storage, grid-connected technologies for a cleaner, more reliable, resilient, and cost-effective future, and demand responsive and distributed energy technologies for a dynamic electric grid.

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    We work closely with academic, government and industry partners to conduct foundational and applied research that provides the groundwork for the development of transformative new energy technologies in the areas of energy storage and conversion, electrical grid, advanced materials for the energy infrastructure, science of manufacturing and water-energy nexus.

    Visit our focus areas and research groups at the right to find out more.

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    • Transportation
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    • Grid Integration
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Publications

Publications by Category

  • Applied Energy Materials
  • Energy Conversion
  • Energy Storage
  • Grid Integration
  • Laser Technologies
  • Thermal Energy

2020

Bartel, Christopher J., Amalie Trewartha, Qi Wang, Alexander Dunn, Anubhav Jain, and Gerbrand Ceder."A critical examination of compound stability predictions from machine-learned formation energies."npj Computational Materials 6.1 (2020). DOI
Dylla, Maxwell T., Alexander Dunn, Shashwat Anand, Anubhav Jain, and G. Jeffrey Snyder."Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials."Research 2020 (2020) 1 - 8. DOI

2019

Tshitoyan, Vahe, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A. Persson, Gerbrand Ceder, and Anubhav Jain."Unsupervised word embeddings capture latent knowledge from materials science literature."Nature 571.7763 (2019) 95 - 98. DOI

2018

Ward, Logan, Alexander Dunn, Alireza Faghaninia, Nils E. R. Zimmermann, Saurabh Bajaj, Qi Wang, Joseph Montoya, Jiming Chen, Kyle Bystrom, Maxwell Dylla, Kyle Chard, Mark Asta, Kristin A. Persson, G. Jeffrey Snyder, Ian Foster, and Anubhav Jain."Matminer: An open source toolkit for materials data mining."Computational Materials Science 152 (2018) 60 - 69. DOI

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