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Project: Designing next generation batteries with AI

Description: A sustainable energy economy requires an immediate shift to renewable
technologies, and batteries are vital to counterbalance the intermittency of solar and wind.
Batteries consist of an anode, cathode, and an electrolyte; while the anode/cathode selection
determines the theoretical energy density, the electrolyte choice often controls battery lifetime.
Furthermore, electrolyte selection can determine how fast a battery charges, battery operation
temperature, and safety. The conflicting requirements for electrolytes often mean that
electrolytes are discovered using trial-and-error approaches; slowing down research progress and
time to commercialization. Here, data science and the ability of AI to extract meaning from
complex data is of vital interest. We have a multitude of data science + battery projects. One
example project involves the development of natural language processing (NLP) and computer
vision algorithms to extract battery data from the literature to build machine learning models.
Although the scientific literature contains decades of relevant battery information, utilization of
this data has been stifled by the lack of NLP tools capable of digesting the information. The
second example project is focused on developing ML tools and applying these tools to discover
novel electrolyte compounds for novel battery chemistries. Combining your data science
expertise with the battery (and materials) domain knowledge that you will obtain from our
laboratory will provide you with a skillset to exploit the battery sector that is expected to grow to
over 100 billion dollars by 2025.

Mentor: Chibueze Amanchukwu is a Neubauer Family Assistant Professor in the Pritzker School of
Molecular Engineering at the University of Chicago, and holds a joint appointment in the
Chemical Sciences and Engineering Division at Argonne National Laboratory. His research
involves the design, synthesis, and understanding of ion transport in electrolytes for batteries and
electrocatalytic applications. His team is especially interested in correlating bulk solvation
properties to electrochemical interfacial phenomena, and they use data science tools coupled
with synthesis and novel characterization approaches to answer these questions. He obtained his
B.S. in chemical engineering at Texas A&M University, PhD in chemical engineering as a
NDSEG Fellow at MIT and was a TomKat Center Postdoctoral Fellow at Stanford University.