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Persson – LBNLV.E.7 Modeling - Predicting and Understanding New Li-ion Materials Using Ab Initio Atomistic Computational Methods (LBNL)
V.E.7 Modeling - Predicting and Understanding New Li-ion Materials Using
Ab Initio Atomistic Computational Methods (LBNL)
Kristin Persson (Staff Scientist, LBNL)
Lawrence Berkeley National Laboratory
Advanced Energy Technology
1 Cyclotron Rd, MS 70R0108B
Berkeley, CA 94720
Phone: (510) 486-7218
E-mail: kapersson@lbl.gov
Start Date: September 2008
Projected End Date: September 2010
Objectives
· Predict new chemistries and crystal structures for
improved electrodes as defined by the goals of
USABC.
· Understand rate-limiting behavior in current electrode
materials in order to target and design optimal
diffusion properties in new materials.
Technical Barriers
Investigating electrode materials with atomistic
modeling require rigorous benchmarking as well as insight
into the materials chemistry and its effect on electrode
performance. Futhermore, the relevant surfaces of the
electrodes are generally not well characterized in terms of
chemistry and structure. Both the cathode materials as well
as the graphitic anode have solid electrolyte interfaces
(SEIs) that depend on the synthesis conditions as well as
the electrolyte composition and the conditions under which
the battery is operated. Our team systematically
benchmarks the calculations and monitors the assumptions
under which the modeling is done to make sure that the
results are relevant and provide useful information and
insights for electrode materials design.
Technical Targets
· Understand the rate limiting bottlenecks in the carbon
materials for the negative electrode.
· Investigate the origin of the electrochemical signature
of ordered and disordered high-voltage spinel.
· Create an automated high-throughput materials design
environment at LBNL.
Accomplishments
· We have evaluated graphite surfaces for Li absorption
and found that, while the [0001] surface is the most
stable surface facet – it does not absorb Li in its
perfectly crystalline defect-free state.
· We have identified the origin of the different
electrochemical profiles in ordered and disordered
Li x Ni 0.5 Mn 1.5 O 4 spinel.
· We have successfully launched the first Google-like
materials search engine from LBNL/MIT. The web
site contains over 15,000 computed compounds for
general searches as well as a structure prediction
application and a Li-battery electrode materials
explorer.
Introduction
There is increasing evidence that many of the
performance limiting processes present in electrode
materials are highly complex reactions occurring on the
atomic level. The Persson group at LBNL is studying these
processes using first-principles density-functional theory
(DFT) modeling tools. By understanding the underlying
reasons for the electrode materials performance we can
suggest improvements or design schemes directed at the
root cause of the process.
Last year our group together with the Kostecki group
at LBNL showed excellent inherent diffusivity of Li in
graphite. However, most carbons exhibit sluggish kinetics,
which caused our group to look to the graphite surfaces for
the kinetic bottleneck in Li intercalation. We have now
found the most stable surface in graphite – the 0001 facet –
does not absorb Li in its perfectly crystalline defect-free
state. This presents a significant kinetic bottleneck for Li
intercalation.
This year we have also joined the BATT focus group
on the high-voltage spinel. In this context we have used
first-principles calculations and analyses on the high
voltage Li(Ni 0.5 ,Mn 1.5 )O 4 spinel to explain the relationship
between cation interactions, ordering and structure and
their effect on the materials performance. Our work
unravels a complicated coupling between the Li
arrangement and the underlying cation lattice, which
FY 2011 Annual Progress Report 651 Energy Storage R&D