Ab Initio Computation for Materials Characterization Elements of ICME Workshop, UIUC, July 2014
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Transcript of Ab Initio Computation for Materials Characterization Elements of ICME Workshop, UIUC, July 2014
Ab Initio Computation for Materials Characterization
Elements of ICME Workshop, UIUC, July 2014
Maria ChanCenter for Nanoscale Materials &CEES Energy Frontier Research CtrArgonne National Laboratory
Collaborators• Lynn Trahey, Zhenzhen Yang,
Mali Balasubramanian, Mike Thackeray, Tim Fister, Argonne National Lab
• Jeff Greeley, Purdue University• Eric Shirley, NIST• Chris Wolverton, Northwestern University• Chris Buurma, Tadas Paulauskas, Robert Klie, University
of Illinois at Chicago• Hadi Tavassol, Maria Caterello, Andy Gewirth, David
Cahill, UIUC
Materials Characterizationstimulus
material signal
???machinery
Materials Characterizationstimulusmaterial =
unknownarrangement
of atoms
+ electronic/magnetic
state
???machinery
signal
Materials Characterizationstimulus
material signal
???machinery
photons (visible, x-ray, infrared) electrons, voltage, magnetic field, etc
synchrotrons, microscopes, spectrometers, etc
Materials Characterizationstimulus
material
signal: absorption, scattering, diffraction,
image, current, etc???
machinery
Ab initio materials modeling
{Properties}(e.g. energy, voltage,band structure etc)
DFT, QMC, etc
Life goal of a computational materials scientist
Skepticalexperimental collaborator
Confidentexperimental collaborator
Case studies: Li-ion & Li-O2 batteries
porous air electrode
electrolyteLithiumanode
Li+
Oxygen
Li-ion battery
Li+
cathodeelectrolyteanode
Li-O2 (“Li-Air”) battery
Some x-ray characterization techniques• x-ray diffraction– crystal structures, lattice parameters
• pair-distribution function– local coordination up to ~10Å
• x-ray absorption/inelastic scattering– local electronic environment
= X-rays
Other modes of characterization
= current or voltageElectrochemical characterization
= electron beamElectron microscopy
X-RAY DIFFRACTION (XRD) 2d sin = n
Image credit: Wikipedia
Li-air (Li-O2) battery
porous air electrode
electrolyteLithiumanode
Li+
Oxygen
2Li+O2Li2O2 or2Li+½O2Li2O or?
How do electrocatalysts affect Li-O2 reaction?
MnO2: put Li/Li+O into tunnels?
MnO2
MnO2x2
1x1
ramsdellite-MnO2
2x1
Li DFT CalculationsPBE+U~200 structures
Li2O & Li2O2~ 3V
Li0.5MnO2
3.2-3.5 VLiMnO2
2.5-2.7 V
Energetics (& experience) suggest Li insertion into tunnels likely
increasing voltage
OLiMn
Li2O & Li2O2~ 3V
increasing voltage
Li0.5.0.25Li2O.
MnO2
3.3V
0.125Li2O.MnO2
2.9 V
LixOy insertion into tunnels also plausible
Li2O2 unit3.1 V Li0.5MnO2
3.2-3.5 VLiMnO2
2.5-2.7 V
OLiMn
Trahey et al, Adv Energy Mat 2013, Ch. 5 in “The Li-air Battery” Ed. Imanishi 2014
Predictions: LixOy go into tunnel, O removal kinetically limited
Does this actually happen?Synchrotron XRD shows lattice parameter changes, but crystal structure mostly remains
Ref: Yang, Trahey, Chan, et al, in preparation
In-situ XRD changes during cycling
Lattice parameter changes
MnO2
a b
c
In-situ lattice parameters (a=b, c) change during cycling
DFT also captures volume changes
hydr
ated
MnO
2
(H2O
) 0.12
5MnO
2
(Li 2O) 0.125
MnO 2
Li 0. 5MnO 2
Li 0. 25(Li 2
O) 0.125MnO 2
XRD: Johnson et al, J Power Sources 1997
(com
pare
d to
pur
e
MnO
2 )
but not individual lattice parameter changes, i.e. a/c ratio
0 20 402
3
4
Time (hr)Vo
ltage
(V) a
In-situ XRD data+DFT model consistent with Li+O co-insertion
bc d
e f
Amount of Li2O in tunnel Amou
nt o
f Li i
n tu
nnel
0 20 402
3
4
Time (hr)Vo
ltage
(V) a
But precise ratio not obtained
bc d
e f
Amount of Li2O in tunnel Amou
nt o
f Li i
n tu
nnel
?
Need another technique e.g. x-ray absorption
Moral of the story• XRD is good for observing structural changes
during a process for a mostly crystalline material
• DFT calculations give approximate volume changes, but not perfectly accurate
• Other techniques that measure electronic structures may be needed
X-RAY DIFFRACTION (XRD) & NON-RESONANT INELASTIC X-RAY SCATTERING (NIXS)
Image credit: Tim Fister
A tale of two structures: Li2O2
H (eV/O2) Féher FöpplPBE -5.69 -6.24HSE06 -5.90 -6.52
Experimental -6.57(9), -6.557
O-O distance 1.28Å 1.55Å
Formation energies fromdensity functional theory calculations
Chan et al, J. Phys. Chem. Lett., 2, 2483 (2011)
Which one is the actual structure of Li2O2? DFT predicts Föppl – verification?
Both proposed from XRD in 1950’s
X-ray diffraction patterns
Errors (“Residuals”) × 3
Synchrotron vs “lab” XRD
Cu K
calculated
(ab initioBethe-SalpeterEquation)
NIXS better distinguishes between two
measured
Moral of the story• XRD refinement is not always
perfect!• DFT formation energies are strong
indicators of relative phase stability, but independent verification is a bonus
• Synchrotron XRD give additional information over lab XRD
• NIXS is sensitive to local structures
PAIR DISTRIBUTION FUNCTION (PDF) & ELECTROCHEMISTRY
Image credit: Billinge, Z. Kristallogr. 219 (2004) 117
X-ray Powder Diffraction
Structure function
Pair distribution function
Lithiating cr-Si – atomistic picture?
Carbon, transition metal oxides: Li goes into empty sites
Si
Li
?
carbon
Wen and Huggins, J. Solid State Chem 37, 271 (1981)
0 1 2 3 4 5x in LixSi
00.
1
0.2
0
.3
0.
4V
vs L
i/Li+
…. which don’t form at room temperature(data is at 415C)
LixSi: complex crystalline phases
Li ?
Si
relax1 by 1
lowest energy
SiLiDFT simulation of Li insertion
Si
SurfaceLi
Li sites
repeat
Evolution of atomic configurations as amount of Li increases
increasing Li content
Corroboration with PDF from APS Computed Si-Si
radial distribution functionEx-situ measurements
(at APS)
Baris Key et al JACS 2011
(111)
(110)
Goldman, Long, Gewirth, NuzzoAdv. Func. Mater. 2011
Compare surface orientations:DFT simulation results
(100) (111) (110)
Different orientations: similar expansion at full lithiation
Anisotropy in lithiation voltages V(110) > V(111)
insertion through (110) is more thermodynamically favorable
voltage anisotropic expansion?
How does voltage difference lead to anisotropic expansion?
time
Solution to diffusion equation
Note: Li enters side surfaces >> top surface isotropic diffusion coefficient
crys
talli
ne S
i
amor
phou
s Li xS
i
10 m
Chan, Wolverton & Greeley, JACS 2012
Orientation-dependent voltage subsequently validated by experiment
Pharr et al Nano Lett. 2012, 12, 5039
Moral of the story• PDF is suitable for
amorphous/disordered materials and can be used for qualitative verification of DFT simulations
• Prediction of a yet-unmeasured quantity is paramount for verification of any new modeling approach!
ELECTROCHEMISTRY AND SURFACE STRESS MEASUREMENTS
2t 0g g – g6(1 )Yt C
Au
Li
What Li/Au surface processes occur before lithiation?
Au: model electrode
model system: gold surface
Initiation of Li deposition @ ~ 1 V
LiClO4 PC
1. onset ~ 1V
ionic liquid
Large voltage range for Li deposition
LiClO4 PC
2. broad reductive feature
ionic liquid
Overlayer (upd) models
1.1V
obtained from genetic algorithm using DFT
Li
Au
Voltage curve from overlayer model
Multilayers
Li
Au
Considered 1-5 Li layers
LiClO4 PCStress during deposition
3. stress: compressive & magnitude increases with more Li
Stress from Li overlayers stress is compressive magnitude increases
with amount of Li magnitude comparable
to experiment
Surface alloy: substitutional models
Simple cluster expansion describes energetics well: each surface/subsurface Li
lowers energy by 1.2/1.5 eV Li-Li nearest neighbor raises
energy by 0.13-0.17 eV other terms <0.05 eV
Alternative surface alloy model 1heating overlayer model to 500K0.98 V
subsurface Li
Au adatoms
Alternative surface alloy model 2overlayer + subsurface Li 0.87 Vsmall compressive stress
surface alloy models may explain stripping peak
Tavassol, Chan, Catarello, Greeley, Cahill, Greeley, Gewirth, J Electrochem Soc 2013
Moral of the story
• Observing multiple properties (current and stress in this case) under the same stimulus gives tighter constraints on explanation
• DFT allows reasonable predictions of surface stress
… and a lot more!• Vibrational spectroscopy (Raman, FTIR)• Nuclear resonance (NMR)• Other x-ray: absorption, fluorescence,
photoelectron, etc• Neutrons (~x-rays in some ways)
Keys to linking ab initio modeling with characterization
1. Figuring out how to get an atomistic model – global minimization e.g genetic algorithm, disorder sampling, cluster expansion, step-by-step simulations, experimental images
Keys to linking ab initio modeling with characterization
2. Calibrating the accuracy of predicted quantities
Keys to linking ab initio modeling with characterization
3. Enjoy it!
Funding: Center for Electrical Energy Storage (CEES): Tailored Interfaces, DOE Energy Frontier Research Center at Argonne National Laboratory, Northwestern University, and University of Illinois at Urbana Champaign, funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. US Department of Energy Sunshot Program (DOE-EE00005659). Center for Nanoscale Materials, supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract No. DE-AC02-06CH11357. The authors also acknowledge grants of computer time from the Fusion cluster in the Laboratory Computing Resource Center at Argonne National Laboratory.
This talk has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne"). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Acknowledgements