updates on high-throughput dfpt · limgas p2ir si li3sb k2o ga3os be3p2 csau znse mgo agcl sic ywn3...
TRANSCRIPT
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Updates on high-throughput DFPT
Guido Petretto, Matteo Giantomassi, Henrique P. C. Miranda,Michiel J. van Setten, David Waroquiers, Shyam Dwaraknath,
Donald Winston, Xavier Gonze, Kristin A. Persson, Geoffroy Hautier,Gian-Marco Rignanese
MODL, Institute of Condensed Matter and Nanosciences, Université catholique de Louvain,1348 Louvain-la-neuve, Belgium
22/05/2019
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 1
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Outline
1 Introduction
2 High-throughput DFPT
3 Phonons database
4 Abinit for HT
5 Further developments
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 2
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Outline
1 Introduction
2 High-throughput DFPT
3 Phonons database
4 Abinit for HT
5 Further developments
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 3
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Aim of the project
Diffusion of large databases based on DFT calculations
⇓High-throughput workflows for Abinit
⇓DFPT phonon band structures at Materials Project
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 4
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Where were we?
ABIDEV 2017:
Infrastructure to run high-throughput calculations with Abinit
dependencies on different python frameworks
high-throughput framework: Abiflows
Preliminary results:
Convergence study
Workflows at NERSC
ABIDEV 2019:
Results
Materials project
Problems encountered
Next steps
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 5
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Where were we?
ABIDEV 2017:
Infrastructure to run high-throughput calculations with Abinit
dependencies on different python frameworks
high-throughput framework: Abiflows
Preliminary results:
Convergence study
Workflows at NERSC
ABIDEV 2019:
Results
Materials project
Problems encountered
Next steps
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 5
-
Outline
1 Introduction
2 High-throughput DFPT
3 Phonons database
4 Abinit for HT
5 Further developments
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 6
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High-throughput framework
DFT + DFPT code
Abinit python analysis framework Workflow manager
Abinit workflows framework
Generic material analysis framework
Available on Github:https://github.com/abinit/abiflows
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 7
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DFPT workflow
Extended workflow to cover all possible DFPT calculation available
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 8
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DFPT workflow
Extended workflow to cover all possible DFPT calculation available
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 8
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Convergence study
Find optimal k-points and q-points sampling for high-throughputPetretto, Gonze, Hautier, Rignanese, Comp. Mat. Sci., 144, 331 (2018)
Set of 48 semiconductors
Various sizes, crystal symmetries, gaps
Several K and Q grids
Statistic on error with respect to dense grids:
relative and absolute errormean and maximum error
NaLi2Sb Ca(CdP)2 CdS SrLiP InS GaN RbYO2 Si4P4RuSiO2 BP AlSb LiZnP MgCO3 ScF3 ZnGeN2 CsBrLiMgAs P2Ir Si Li3Sb K2O Ga3Os Be3P2 CsAuZnSe MgO AgCl SiC YWN3 SrO PbF2 RuS2MgSiP2 SiO2 GaP Be2C SnHgF6 MgMoN2 ZnO VCu3S4ZrSiO4 Ba(MgP)2 Ba(MgAs)2 Ca(MgAs)2 C RbI FeS2 ZnTe
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 9
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Convergence study: grids density
absolute and relative errors on ω, Eat, Z∗ and �
1500 points per reciprocal atom ⇒ Nkpt*Natoms w 1500⇒ All materials converged with 0.5 cm−1 MAE and 0.6% MARE
Better using a Q-grid commensurable with K-grid
⇒ Smoother close to Γ
UL LK KW WXΓ Γ0
25
50
75
100
125
150
175
Fre
quen
cy(c
m−
1)
AgCl - qpt 6× 6× 6AgCl - qpt 12× 12× 12
0.3 0.6 0.9 1.2 1.5
600800
100012001400160018002000
kpra
ω
0.3 0.6 0.9 1.2 1.5εMARE (%)
600800
100012001400160018002000
qpra
ω
512 1000 1728 2744 4096kpra
−0.5+0.5
−0.5+0.5
−0.5+0.5
−0.5+0.5
−0.5+0.5
−0.5+0.5
Err
oron
ω(%
)38 cm−1
46 cm−1
62 cm−1
103 cm−1
147 cm−1
156 cm−1AgCl - qpt: U
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 10
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Convergence study: symmetry of the grid
Convergence rate of phonon frequencies ω for symmetric versusnon-symmetric grids
500 1000 1500 2000 2500 3000 3500kpra
10−2
10−1
100
101
MxA
RE
(%)
Si - brkSi - sym
BP - brkBP - sym
ZnO - brkZnO - sym
⇓Grids should preserve the symmetries of the system
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 11
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Results validation
Validation versus experimental data
Vibrational entropy at 300K
0 20 40 60 80 100 120 140
Sexp (J/(K mol))
0
20
40
60
80
100
120
140
Ssi
m(J
/(K
mol
))
InS
MgCO3
SiO2
K2O
AlSb
ZnSe
ZrSiO4
BP
MgOSiO2
C
GaN
CdS
ScF3
SrO
Si
RbI
GaP
PbF2
SiC
Be2C
ZnO
CsBr
ZnTe
RuS2FeS2
AgCl
CB
e 2C
SiC S
iB
PS
iO2
MgO
GaN
SiO
2
Zn
OG
aPF
eS2
Ru
S2
−15−10−5
05
101520
Err
or(%
)
SrO
Cd
SA
lSb
MgC
O3
InS
Zn
Se
Zn
Te
ZrS
iO4
AgC
lS
cF3
K2O
CsB
rP
bF
2
Rb
I
−15−10−5
05
101520
Err
or(%
)
Γ phonon frequencies
0 200 400 600 800 1000 1200
ω (cm−1)
−40
−30
−20
−10
0
10
20
Err
or(%
)
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 12
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Outline
1 Introduction
2 High-throughput DFPT
3 Phonons database
4 Abinit for HT
5 Further developments
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 13
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Materials Project phonons database
Open access database: 1521 semiconductor materials (and growing. . . )1508 of those materials with less than 13 atomic sites
∼ 5M CPU-hours
Interatomic forceconstants (DDB files)
Phonon dispersion
Born effective charges
Dielectric tensor
Thermodynamic prop.:
∆F , ∆Eph, Cv , S
Γ X YΣ Γ Z Σ N P Y Z X P0
500
1000
Fre
quen
cy(c
m−
1) β-cristobalite
Γ X M Γ Z R A Z X R M A
stishovite
Γ M K Γ A L H A L M K H0
500
1000
Fre
quen
cy(c
m−
1) α-quartz
Petretto, Dwaraknath, Miranda, Winston, Giantomassi, Van Setten, Gonze, Persson, Hautier, Rignanese, Scientific Data (2018)
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 14
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Materials Project phonons database
Open access database: 1521 semiconductor materials (and growing. . . )1508 of those materials with less than 13 atomic sites
∼ 5M CPU-hours
Interatomic forceconstants (DDB files)
Phonon dispersion
Born effective charges
Dielectric tensor
Thermodynamic prop.:
∆F , ∆Eph, Cv , S
Γ X YΣ Γ Z Σ N P Y Z X P0
500
1000
Fre
quen
cy(c
m−
1) β-cristobalite
Γ X M Γ Z R A Z X R M A
stishovite
Γ M K Γ A L H A L M K H0
500
1000
Fre
quen
cy(c
m−
1) α-quartz
Petretto, Dwaraknath, Miranda, Winston, Giantomassi, Van Setten, Gonze, Persson, Hautier, Rignanese, Scientific Data (2018)
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 14
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Materials Project phonons database
All the data available on the websiteand through REST service.
Interactive visualization of the phonondispersion using the phononwebsite
http://henriquemiranda.github.io/
phononwebsite/
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 15
http://henriquemiranda.github.io/phononwebsite/http://henriquemiranda.github.io/phononwebsite/
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Outline
1 Introduction
2 High-throughput DFPT
3 Phonons database
4 Abinit for HT
5 Further developments
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 16
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Reliability: workflows
For the 1521 phonon band structures:
Relax workflow
Phonon workflow (+ anaddb)
Out of all the submitted workflows only ∼ 30 did not completesuccessfully
Main reasons:
Too slow relaxation/relaxation did not converge
Too small gap (switched to metallic)
Presence of La
Poor choice of materials
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 17
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Reliability: workflows
For the 1521 phonon band structures:
Relax workflow
Phonon workflow (+ anaddb)
Out of all the submitted workflows only ∼ 30 did not completesuccessfully
Main reasons:
Too slow relaxation/relaxation did not converge
Too small gap (switched to metallic)
Presence of La
Poor choice of materials
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 17
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Reliability: results
Warnings available in the database:
Negative ω close to Γ: 24 materials
ASR break >30 cm−1: 72 materials
CNSR break >0.2e: 92 materials
Wave Vector
0
10
20
30
40
50
60
Freq
uenc
y (c
m1 )
0 20 40 60 80asr value
0
50
100
150
200
250
300
350
400
Num
ber o
f mat
eria
ls
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75chneut value
0
200
400
600
800
1000
Num
ber o
f mat
eria
ls
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 18
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Problems encountered
Current MP cluster: KNL nodes
not optimizedreserve full noderelatively poor performancesdifficult to fine tune parallelization at high-throughput level
Relax - ionmov 22: seems faster but may fail at small tolmxf (1e−6)
⇒ switch ionmov at python level.Autoparal for DFPT
always gives the maximum number of preocesses allowedoften parallelizing over just the k-points is advantegeous
⇒ could be improved?Memory
moving to larger materials already caused a few jobs to fail due tomemory issues
⇒ might be needed to rely on estimation of total memory
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 19
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Outline
1 Introduction
2 High-throughput DFPT
3 Phonons database
4 Abinit for HT
5 Further developments
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 20
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More data on the MP database
Calculations have proceed: almost 500 more materials
More physical quantities will be extracted from the phonon data:
Sound velocity as slope of acoustic modes
Low-frequency dielectric permittivity tensor �αβ(ω)
Thermal displacement ellipsoids (Debye-Waller)
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35Wave Vector
0
20
40
60
80
100
120
140
160
Freq
uenc
y (c
m1 )
0.7071, 0.0000, 0.7071 - X
0 200 400 600 800 1000Frequency (cm 1)
20
0
20
40
60
()
Re{ 00}Re{ 11}Re{ 22}Im{ 00}Im{ 11}Im{ 22}
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 21
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Phonons for metals
Extend the calculation to metals as well. Materials project:
24356 metals8242 with less than 6 atoms
Potential issues:
Denser k-point grids
Q-point grids?
Smearing
Kohn anomalies
Fourier interpolation 0
20
40
60
80
100
Freq
uenc
y (c
m )-1
Γ X
Transverse
Longitudinal
non-uniform gridspline interpolationuniform grid (20 q-points)uniform grid (25 q-points)
ε=0.09
He, Liu, Li, Rignanese, Zhou (2019)
⇓New convergence study required
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 22
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Volume: Grüneisen parameters
Phonons at different volumes (e.g. ±2%) ⇒ GrüneisenTools already available in Abinit (netcdf) and Abipy
Preferable to have separate workflows
g = GrunsNcFile.from_ddb_list(["-2_DDB", "+0_DDB", "+2_DDB"])
g.plot_phbands_with_gruns(with_doses=None)
g.plot_gruns_scatter()
phbst.plot_phbands(units="cm-1")
M K A L H A L M K HWave Vector
0
50
100
150
200
250
300
Freq
uenc
y (c
m1 )
0 50 100 150 200 250 300Frequency (cm 1)
4
2
0
2
4
Grun
eise
n
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 23
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Volume: Quasi-Harmonic Approximation
Tools for QHA implemented in Abipy:
Standard QHA object
generated from GSR and PHDOS netcdf files
Fittings
Thermal expansion coefficient
Interface to Phonopy for further functionalities
Cheaper QHA: QHA-3P (Nath et al. arXiv:1807.04669)
several electronic energies at different V and 3 phonon calculations
extrapolate phonon contribution at other V
Satisfactory results
⇒ Interesting for high-throughput
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 24
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Volume: Quasi-Harmonic Approximation
Example QHA-3P for Si
qha = QHA.from_files(gsr_paths, dos_paths)
qha3p = QHA3P.from_files(gsr_paths, gruns_path, ind_doses=[1,2,3])
fig = qha.plot_thermal_expansion_coeff()
qha3p.plot_thermal_expansion_coeff(ax=fig.axes[0])
0 200 400 600 800 1000 1200 1400 1600
T (K)
0.0
0.5
1.0
1.5
α(K−
1)
×10−5
qha
qha3p
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 25
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Thank you for your attention
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 26
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High-throughput framework
What do we need for high-throughput with ?
Python interface to DFT codes
Inputs
pseudopotentials and cutoffs
automatic generation
Workflow management
Database interface
Workflows
Error handling and data analysis
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 27
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Materials Project phonons database: rester
Fetching DDB files from MP and analyze results with Abipy
ddb = abilab.DdbFile.from_mpid("mp-1265") # MgO
phbst, phdos = ddb.anaget_phbst_and_phdos_files(ndivsm=20, nqsmall=20,
lo_to_splitting=True)
phbst.plot_phbands(units="cm-1")
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 28
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q-points convergence
Use subgrids of the q-point grid to check the convergence w.r.t. qpt
smoothed average
average for each q density
smoothed max
Material dependent
Suggest good convergence level at 1500 qppa
reducing by a factor 2 may lead to sizeable errors on average
G. Petretto (MODL, UCL) High-throughput DFPT 22/05/2019 29
IntroductionHigh-throughput DFPTPhonons databaseAbinit for HTFurther developments