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Materials Genome Initiative for Soft Matter: Computational ... … · 1 Materials Genome Initiative...
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Materials Genome Initiative for Soft Matter: Computational Challenges
Peter T. Cummings1Department of Chemical & Biomolecular Engineering, Vanderbilt University2Center for Nanophase Materials Sciences, Oak Ridge National Laboratory
Materials Genome Initiative Grand Challenges Summit: Soft MaterialsInstitute for Bioscience & Biotechnology Research, Rockville, Maryland
September 19-20, 2013Center for Nanophase Materials Sciences!" #!$ %&'() *!"&#*!+ +!,#%!"#%-
Simulation-Based Engineering and Science
Activity 2008-2010 International comparative study
Chaired by Sharon Glotzer– Life sciences and medicine– Materials– Energy and sustainability
Strategic directions workshop Chaired by Peter Cummings
One of key precursor studies for MGI
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http://www.nsf.gov/mps/ResearchDirectionsWorkshop2010/RWD-color-FINAL-usletter_2010-07-16.pdf
Soft Matter
Wikipedia definition “[S]ubfield of condensed matter comprising a variety of physical states that
are easily deformed by thermal stresses or thermal fluctuations” Examples: Liquids, colloids, polymers, foams, gels, granular materials, some
biomaterials Common feature: Predominant physical behaviors occur at energy scale
comparable with kBT – Quantum aspects generally unimportant– Weak dispersion interactions frequently dominate
Can be difficult to predict properties directly from atomic or molecular constituents
– Soft matter often self-organizes into mesoscopic physical structures that are much larger than microscopic scale yet much smaller than the macroscopic scale
• Macroscopic properties are result of mesoscopic sructure
Contrast with hard materials– Energy scales large by comparison to kBT – Relatively easy to predict properties since atoms/molecules are typically in crystalline
lattice with no mesoscopic structuring
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MGI for Soft Matter
Characteristics of soft matter make MGI-like screening very difficult To accomplish
computationally, we typically have to perform multiple steps, depending on nature of system Simplest case: relatively simple liquid with no mesoscopic structure
– From chemistry, gather or derive forcefields– Initialize simulation (molecular dynamics or Monte Carlo, as appropriate)– Equilibrate simulation– Perform production runs of simulations– Statistically average to obtain properties
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Chemistry and conditions Properties
Case in Point
Room-temperature ionic liquids Ionic liquids (i.e., molten salts) made of organic ions whose ability to
crystallize is frustrated by organic groups Relevant in multiple energy technologies
Energy-efficient separations, carbon capture, energy storage as electrolytes for supercapacitors
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C O NH S F
C4mpip+
Tf2N-
Ionic Liquids: Designer Solvents for a Cleaner World, James F. Wishart, President’s FY 2005 Budget Request to Congress
Case in Point: RTILs RTILs
~ 9 billion possible RTILs Structured on nanoscale but not mesoscale (based on SAXS and SANS)
Atomistic description is appropriate and sufficient
Perfect candidates for screening? E.g., for solubility of carbon dioxide
Problems: Only ~100 forcefields available for RTILs
Hence, to implement MGI-like screening of RTILs needs automated forcefield development capability
Contrast with screening of metal-organic frameworks 137,953 hypothetical MOF structures generated Each MOF evaluated for methane storage at 35 bar
and 298 K using grand canonical Monte Carlo ~300 MOFs had higher methane-storage capacity at
35 bar than current world record
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Wilmer, et al., Large-scale screening of hypothetical metal–organic frameworks. Nature Chemistry, 4 (2011) 83-89. doi:10.1038/nchem.1192
Self Assembly in Soft Matter
Both RTILs and MOFs share simplifying feature that atomistic description is appropriate No mesoscopic ordering in RTIL, crystalline (macroscopic) structure for
MOFs
What about soft matter systems that self-assemble into mesoscopic structures that determine macroscopic properties? Examples
Amphiphilic molecules/polymers Lipids that assemble into bilayers
Technical challenges Time to self-assemble Scale of mesoscopic structures
Usual approach Coarse-graining
– Systematic, ad hoc, models that exist only at the coarse-grained level
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Coarse-Graining8
Noid, W. G., “Perspective: Coarse-grained models for biomolecular systems,” JCP, 139 (2013) 090901
NCBL!
DOXY!
OXY!
ALKE!
Superposition of fully atomistic and coar se-gra ined models of n-dodecane. A 3:1 mapping is used, where each CG bead represents 3 CH2 groups or 2 CH2 groups and a CH3 group
Mapping of fully atomistic model of skin lipid to CG model. Courtesy of Clare McCabe
E.g., protein folding
Self-Assembling Soft Matter
Goal
For simple case... E.g., ionic liquids
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Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
PropertiesEquilibrate atomistic simulation
Production runs
Statistical averaging
Chemistry and conditions Properties
Self-Assembling Soft Matter
Goal
For simple case... E.g., ionic liquids
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Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
PropertiesEquilibrate atomistic simulation
Production runs
Statistical averaging
Chemistry and conditions Properties
Self-Assembling Soft Matter Bottom-up coarse-grained properties estimation
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Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
Properties
Equilibrate atomistic simulation
Production runs
Statistical averaging
Inversion to atomistic
CG forcefield
Statistical averaging
CG production
runs
Self-Assembling Soft Matter Bottom-up coarse-grained properties estimation
Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
Properties
Equilibrate atomistic simulation
Production runs
Statistical averaging
Inversion to atomistic
CG forcefield
Statistical averaging
CG production
runs
Self-Assembling Soft Matter Bottom-up coarse-grained properties estimation
Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
Properties
Equilibrate atomistic simulation
Production runs
Statistical averaging
Inversion to atomistic
CG forcefield
Statistical averaging
CG production
runs
GPU computing
Self-Assembling Soft Matter Bottom-up coarse-grained properties estimation
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Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
Properties
Equilibrate atomistic simulation
Production runs
Statistical averaging
Inversion to atomistic
CG forcefield
Statistical averaging
CG production
runs
Self-Assembling Soft Matter Within this component of overall workflow...
Specialized methods to accelerate calculations Monte Carlo methods (e.g., configurational bias) Parallel tempering Rare events techniques ....
Specialized methods to accommodate self assembly Constant stress ensemble ....
Specialized property sampling methods E.g., free energy methods
Equilibrate atomistic simulation
Production runs
Self-Assembling Soft Matter Bottom-up coarse-grained properties estimation
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Chemistry Atomistic forcefield(s)
gather
derive
Initialize atomistic simulation
Properties
Equilibrate atomistic simulation
Production runs
Statistical averaging
Inversion to atomistic
CG forcefield
Statistical averaging
CG production
runs
Self-Assembling Soft Matter Multiple methods for obtaining CG forcefield
Structure matching E.g., iterated Boltzmann inversion*
– D. Reith, H. Meyer, and F. Müller-Plathe, Macromolecules 34 (2001) 2335
Force matching S. Izvekov and G. A. Voth, J. Phys. Chem. B 109 (2005)
2469; J. Chem. Phys. 123 (2005) 134105
Choice of CG method strongly coupled to properties to be calculated CG simulations cannot reproduce all properties of atomistic system
Method should preserve properties of interest
Statistical averaging
CG forcefield
*Chan, E. R.; Striolo, A.; McCabe, C.; Cummings, P. T. Glotzer, S. C., “Coarse-grained force field for simulating polymer-tethered silsesquioxane self-assembly in solution,” JCP 127 (2007) 114102
Challenges
MGI-like computational screening for soft matter relies on complex scientific workflows How to automate? One acceleration scenario, assuming transferable CG forcefields
Chemistry
Properties
Inversion to atomistic
CG forcefield
Statistical averaging
Equilibrate CG simulation
CG production
runs
Initialize CG
simulationgather
Challenges
Coarse-graining often means running many similar atomistic simulations Data management = reusability
Hybrid materials....
The three V’s Validation, validation, validation
Atomic, molecular and nanoscale experimental probes (especially X-ray and neutron sources at DOE and NIST) provide capabilities for validation of computational approaches, particularly to nanoscale structure and dynamics
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Hard materials Soft materialsHybrid materials
Met
al-o
rgan
ic
fram
ewor
ks
Poly
hedr
al
olig
imer
ic
sils
esqu
ioxa
nes
Challenges
Interfaces
Critically important in multiple applications Energy storage, catalysis, separation processes,... Combines issues of soft and hard materials, often synergistically
– Fluid Interface Reactions Structure and Transport (FIRST) Energy Frontier Research Center
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Hard materials Soft materials
Nano-porous electrodes Micropores (<2nm) increase specific surface area Anomalous increase of capacitance is observed experimentally
Understanding Supercapacitors21
Largeot, C.; Portet, C.; Chmiola, J.; Taberna, P.-L.; Gogotsi, Y.; Simon, P. J. Am. Chem. Soc. 2008, 130
REVIEW ARTICLE
nature materials | VOL 7 | NOVEMBER 2008 | www.nature.com/naturematerials 849
(zone III) were !tted with equation (5). For micropores (<1 nm), it was assumed that ions enter a cylindrical pore and line up, thus forming the ‘electric wire in cylinder’ model of a capacitor. Capacitance was calculated from43
ln0
r 0 (6)/ = (6)
where a0 is the e"ective size of the ion (desolvated). #is model perfectly matches with the normalized capacitance change versus pore size (zone I in Fig. 4). Calculations using density functional theory gave consistent values for the size, a0, for unsolvated NEt4
+ and BF4
– ions43.#is work suggests that removal of the solvation shell is required
for ions to enter the micropores. Moreover, the ionic radius a0 found by using equation (6) was close to the bare ion size, suggesting that ions could be fully desolvated. A study carried out with CDCs in a
solvent-free electrolyte ([EMI+,TFSI–] ionic liquid at 60 °C), in which both ions have a maximum size of about 0.7 nm, showed the maximum capacitance for samples with the 0.7-nm pore size46, demonstrating that a single ion per pore produces the maximum capacitance (Fig. 5). #is suggests that ions cannot be adsorbed on both pore surfaces, in contrast with traditional supercapacitor models.
MATERIALS BY DESIGN#e recent !ndings of the micropore contribution to the capacitive storage highlight the lack of fundamental understanding of the electrochemical interfaces at the nanoscale and the behaviour of ions con!ned in nanopores. In particular, the results presented above rule out the generally accepted description of the double layer with solvated ions adsorbed on both sides of the pore walls, consistent with the absence of a di"use layer in subnanometre pores. Although recent studies45,46 provide some guidance for developing materials with improved capacitance, such as elimination of macro- and mesopores and matching the pore size with the ion size, further material optimization by Edisonian or combinatorial electrochemistry methods may take a very long time. #e e"ects of many parameters, such as carbon bonding (sp versus sp2 or sp3), pore shape, defects or adatoms, are di$cult to determine experimentally. Clearly, computational tools and atomistic simulation will be needed to help us to understand the charge storage mechanism in subnanometre pores and to propose strategies to design the next generation of high-capacitance materials and material–electrolyte systems47. Recasting the theory of double layers in electrochemistry to take into account solvation and desolvation e"ects could lead to a better understanding of charge storage as well as ion transport in ECs and even open up new opportunities in areas such as biological ion channels and water desalination.
REDOX-BASED ELECTROCHEMICAL CAPACITORS
MECHANISM OF PSEUDO-CAPACITIVE CHARGE STORAGESome ECs use fast, reversible redox reactions at the surface of active materials, thus de!ning what is called the pseudo-capacitive behaviour. Metal oxides such as RuO2, Fe3O4 or MnO2 (refs 48, 49), as well as electronically conducting polymers50, have been extensively studied in the past decades. #e speci!c pseudo-capacitance exceeds
0
5
10
15
1.5 M1.5 M1.5 M1.0 & 1.4 M1.7 M
I IV
D
E
F
CB
A
Average pore size (nm)
0 1 2 3 4 5 15 25 35 45
II III
Norm
alize
d ca
paci
tanc
e (
F cm
–2)
+–
––
–
–
––
–
–
––
–
–
–
–
––
–
–
–
b d
a
–
–
–
–
–
–
Figure 4 Specific capacitance normalized by SSA as a function of pore size for different carbon samples. All samples were tested in the same electrolyte (NEt4
+,BF4–
in acetonitrile; concentrations are shown in the key). Symbols show experimental data for CDCs, templated mesoporous carbons and activated carbons, and lines show model fits43. A huge normalized capacitance increase is observed for microporous carbons with the smallest pore size in zone I, which would not be expected in the traditional view. The partial or complete loss of the solvation shell explains this anomalous behaviour42. As schematics show, zones I and II can be modelled as an electric wire-in-cylinder capacitor, an electric double-cylinder capacitor should be considered for zone III, and the commonly used planar electric double layer capacitor can be considered for larger pores, when the curvature/size effect becomes negligible (zone IV). A mathematical fit in the mesoporous range (zone III) is obtained using equation (5). Equation (6) was used to model the capacitive behaviour in zone I, where confined micropores force ions to desolvate partially or completely44. A, B: templated mesoporous carbons; C: activated mesoporous carbon; D, F: microporous CDC; E: microporous activated carbon. Reproduced with permission from ref. 44. © 2008 Wiley.
Norm
alize
d ca
paci
tanc
e (
F cm
2 )
0.66
7
8
9
10
11
12
13
4.3 Å
7.6 Å
7.9 Å
2.9 Å
EMI cation
TFSI anion
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0.7 0.8Pore size (nm)
0.9 1
–
+
1.1
FluorineCarbon
NitrogenHydrogen
OxygenSulphur
H
HH
H
H
HH
H
FF
F
FF
F
ON
CO
OSS
C
O
HC C
C C
C CN N
Figure 5 Normalized capacitance change as a function of the pore size of carbon-derived-carbide samples. Samples were prepared at different temperatures in ethyl-methylimidazolium/trifluoro-methane-sulphonylimide (EMI,TFSI) ionic liquid at 60 °C. Inset shows the structure and size of the EMI and TFSI ions. The maximum capacitance is obtained when the pore size is in the same range as the maximum ion dimension. Reproduced with permission from ref. 46. © 2008 ACS.
New results
Traditional view
0 1 2 3 4 5 average pore size (nm)
0
5
10
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Chmiola, J.; Yushin, G.; Gogotsi, Y.; Portet, C.; Simon, P.; Taberna, P. -
L., Science, 2006, 313, 1760.
1.5 M solution of tetraethylammonium tetrafluoroborate in acetonitrile
MD Simulation
Goal is to reconcile conflicting experiments Simulation setup
Nanoslits filled with room-temperature ionic liquids (RTILs)– Electrolyte: [emim+][Tf2N-] – Electrode: slit-shaped pore – Three layers of graphene sheets– Slit width (d): 0.67 ~ 1.8 nm– Applied potential: ~ 1.41 V – NPT: 333K, 1atm
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emim+ Tf2N-
8.5×5.5×2.8 Å3 10.9×5.1×4.7 Å3
d
6.1 nm 3.5 nm
2.1 nm
MD Simulation23
Cathode Anode
Simulation setup
Capacitance Dependence on Micropore Size
Normalized capacitance as a function of slit width (C-d curve)
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• 1st peak agrees will with experiments
• d > 1.0nm: 2nd peak is a new feature
• d = 0.7 ~ 1.0 nm: anomalous increase• d < 0.7 nm: decreases with pore size
Feng, G., & Cummings, P. T. (2011). Supercapacitor Capacitance Exhibits Oscillatory Behavior as a Function of Nanopore Size. The Journal of Physical Chemistry Letters, 2, 2859–2864. doi:10.1021/jz201312e