Speaker
Description
Electric vehicles and renewable energy storage demand batteries that are cheaper, longer-lasting, and more powerful than current lithium-ion technology. Battery chemistry happens at the atomic level, where quantum physics rules apply. Traditional computers struggle to model these quantum interactions accurately. Quantum computing represents a promising paradigm shift in how we model battery materials at the atomic and molecular level.
While classical computational methods like Density Functional Theory (DFT) have enabled significant progress in battery research, they face fundamental limitations when modeling the strongly correlated electronic systems that govern battery chemistry.
Quantum computers promise to overcome these limitations by directly simulating quantum mechanical behavior rather than approximating it. Battery performance depends on electrochemical processes occurring at atomic scales where quantum mechanics dominates. The fundamental challenge is solving the many-body Schrödinger equation for systems containing dozens to hundreds of electrons.
Thus, this paper provides a comprehensive technical analysis of how quantum computing is being applied to battery modeling, the specific algorithms being deployed, current results from industry partnerships, and the transformative potential for next-generation energy storage.
Quantum computing could revolutionize battery technology by directly simulating the quantum behavior of atoms and molecules, potentially leading to batteries that are cheaper, more powerful, and longer lasting, which would transform present-day electric vehicles and renewable energy storage.
| Apply for student award at which level: | PhD |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |