doi.org/10.26434/chemrxiv.13270184.v2

Benchmarking binding energy calculations for organic structure-directingagents in pure-silica zeolitesDaniel Schwalbe-Koda, Rafael Gomez-Bombarelli

Submitted date: 21/01/2021 • Posted date: 22/01/2021Licence: CC BY-NC-ND 4.0Citation information: Schwalbe-Koda, Daniel; Gomez-Bombarelli, Rafael (2020): Benchmarking bindingenergy calculations for organic structure-directing agents in pure-silica zeolites. ChemRxiv. Preprint.https://doi.org/10.26434/chemrxiv.13270184.v2

Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) forzeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective moleculesthat maximize binding towards a given zeolite framework. Over the last decades, a variety of methods andlevels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus onthe best calculation strategy for high-throughput virtual screening undertakings. In this work, we comparebinding affinities from density functional theory (DFT) and force field calculations for 272 zeolite-OSDA pairsobtained from static and time-averaged simulations. Enabled by automation software, we show that bindingenergies from the frozen pose method correlate best with DFT time-averaged energies. They are also lesssensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationallyexpensive. Furthermore, we demonstrate that a broader exploration of the conformation space from moleculardynamics simulations does not provide significant improvements in binding energy trends over single-pointcalculations. The code and benchmark data are open-sourced and provide robust andcomputationally-efficient guidelines to calculating binding energies in zeolite-OSDA pairs.

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Benchmarking binding energy calculations for organic structure-directingagents in pure-silica zeolites

Daniel Schwalbe-Koda and Rafael Gomez-Bombarellia)

Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge,MA 02139

(Dated: 20 January 2021)

Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) forzeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective moleculesthat maximize binding towards a given zeolite framework. Over the last decades, a variety of methods andlevels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus onthe best calculation strategy for high-throughput virtual screening undertakings. In this work, we comparebinding affinities from density functional theory (DFT) and force field calculations for 272 zeolite-OSDApairs obtained from static and time-averaged simulations. Enabled by automation software, we show thatbinding energies from the frozen pose method correlate best with DFT time-averaged energies. They arealso less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as lesscomputationally expensive. Furthermore, we demonstrate that a broader exploration of the conformationspace from molecular dynamics simulations does not provide significant improvements in binding energytrends over single-point calculations. The code and benchmark data are open-sourced and provide robust andcomputationally-efficient guidelines to calculating binding energies in zeolite-OSDA pairs.

I. INTRODUCTION

Zeolites are nanoporous materials widely used in catal-ysis, separation, sorption and many other industrially-relevant applications.1 These metastable polymorphsare typically crystallized by adding inorganic cationsand organic molecules to amorphous precursor gels inhydrothermal conditions,2–4 although organic-free ap-proaches are also possible.5–12 In particular, a combina-tion of electrostatic effects and dispersion interactions al-lows the molecules to act as organic structure-directingagents (OSDAs) that template the pore structure ofthe zeolite and determine its topology.4,13,14 By explor-ing a variety of OSDAs under different synthesis con-ditions, several new zeolites have been discovered overthe last decades.15 Nevertheless, only around 250 differ-ent topologies have been experimentally identified,16,17 amajority of which rely on OSDAs to be realized. Access-ing known and new zeolites with desired pore structuresand compositions through OSDA design is still an open-ended problem, often relying on trial and error.18

Computational simulation of zeolite-OSDA interac-tions has been an important resource to rationalize thedesign of templates, explain synthesis outcomes, or locatepreferential placements for OSDAs.14,19 Early molecularmodeling works relied on shape-matching methods20–23

to assess the goodness-of-fit between an OSDA and a ze-olite. Later, it was shown that host-guest interactionscomputed from atomistic simulations are good predic-tors of synthesis outcomes for zeolites.24–30 This descrip-tor has enabled theory-driven discovery of many zeolitesover the last years.15,31–36

a)Electronic mail: rafagb@mit.edu

Yet, the interaction energy between guests and hostsis strongly dependent on the level of theory and com-putational pipeline employed. Methods based on quan-tum mechanics, such as density functional theory (DFT)calculations, offer mostly parameter-free descriptions ofthe total energy once a suitable exchange-correlationfunctional is chosen. However, the large size of typicalzeolite-OSDA systems demands considerable computa-tional resources, preventing the use of DFT for OSDAscreening purposes. A more cost-effective approach isto use force fields (FF) to compute interatomic inter-actions. Several parametrizations have been employedto model host-guest interactions in zeolites, includingCVFF,25,28,37–39 Dreiding,40–43 COMPASS,44 UFF,45,46

and others.24,47–51

Even after a level of theory is appropriately cho-sen, different ways to calculate interaction energies be-tween zeolites and OSDAs remain. As an example, onecould calculate binding affinities by computing energiesby performing structural optimizations,24,44,51, molecu-lar dynamics (MD) simulations,40,41,43,47,49,50,52 or cal-culating van der Waals (vdW) contributions by freez-ing host and guest structures.25,39,45,46,53,54 Furthermore,optimizations and dynamics can be performed at con-stant pressure50,51 or volume.25,39,43,45,46,52 In screeningapproaches, calibrating strategies to obtain consistent,computationally-efficient results often make up for bi-ases in simulation methods, matching experimental re-sults better than higher levels of theory with less system-atic errors.55,56 This is especially important in a diverse,combinatorial chemical space such as the one providedby pairings of OSDAs and zeolites.

In this work, we compare methods to calculate bind-ing energies of OSDAs in zeolites and propose guidelinesto compute these interactions in high-throughput virtualscreening approaches. In particular, the following contri-

2

butions are put forward:

1. Different simulation constraints, structural opti-mization algorithms and initial conditions of thehosts significantly change the trends in binding en-ergies for all simulation methods;

2. Binding energies of zeolite-OSDA pairs from theDreiding FF are best calculated by constant vol-ume simulations with the frozen pose method, asopposed to structural optimizations or MD simula-tions;

3. A reference dataset of 272 OSDA-zeolite pairs cal-culated with DFT and FF approaches can enablefurther benchmarks with other FF parametriza-tions, software packages or simulation pipelines;

4. An open-source Python interface to the GeneralUtility Lattice Program (GULP), GULPy, to en-able faster generation of input files, parsing of out-puts, structure manipulation, and execution of FFcalculations.

II. METHODS

A. Simulation Details

DFT calculations were performed using the Vi-enna Ab-initio Simulation Package (VASP),57,58 ver-sion 5.4.4, within the projector-augmented wave (PAW)method.59,60 The Perdew–Burke–Ernzerhof (PBE) func-tional within the generalized gradient approximation(GGA)61 was used as the exchange-correlation func-tional. vdW interactions were taken into account throughGrimme’s D3 corrections.62,63 Several benchmarks haveshown that including dispersion corrections in DFT cal-culations is imperative to accurately predict trends inbinding energies between zeolites and guests.53,54,64–72

Although D3 is known to overbind the guest speciesto zeolites,54,70–72 it provides an excellent balance be-tween cost and accuracy for binding energies compared tohigher levels of theory such as random phase approxima-tion, Møller-Plesset perturbation theory, or many-bodydispersion. The kinetic energy cutoff for plane waves wasrestricted to 520 eV. Integrations over the Brillouin zonewere performed using Monkhorst-Pack k-point meshes73

(Γ-centered for hexagonal unit cells) with a uniform den-sity of 64 k-points/A−3 (see Table S1 for complete k-point meshes for each zeolite). For isolated molecules, avacuum of 15 A thickness was employed in all directionsto avoid unphysical interactions between periodic images.A stopping criterion of 10−6 eV was adopted for the self-consistent field (SCF) cycle energy convergence. Relax-ation of unit cell parameters and atomic positions wasperformed until the Hellmann–Feynman forces on atomswere smaller than 10 meV/A. Ab initio MD (AIMD)simulations were performed in the NPT ensemble with

Langevin dynamics74,75 within the Parrinello-Rahmanmethod76,77, and 0.5 fs timesteps. The sampling temper-ature was fixed at 400 K to simulate typical hydrother-mal conditions in zeolite synthesis.3 The fictitious lat-tice mass was set to 1,000 atomic mass units, and allLangevin friction coefficients were set to 1 ps−1. The ex-ternal pressure has been set to 0 kbar to allow compar-isons with similar works where this value is implicit.50,51

Ground-state geometries were thermalized by randomlydisplacing the atoms by up to 0.02 A in each Cartesiancoordinate before being used as initial configurations forAIMD calculations. AIMD simulations were performedfor 500 fs, with only the last 200 fs considered for pro-duction. Despite the short time lengths, this is enoughto have well-equilibrated trajectories (see Fig. S1).

FF simulations were performed using the General Util-ity Lattice Program (GULP), version 5.1.1,78,79 throughthe new GULPy package.80 MD simulations were per-formed in the NVT and NPT ensembles with modifiedNose-Hoover dynamics81 using the Leapfrog Verlet in-tegrator, 0.5 fs timesteps, temperature of 400 K, and 0kbar of external pressure. Fully optimized geometrieswere used as initial configurations for MD calculations.All MD trajectories consisted of a production run of 5ps preceded by a 5 ps equilibration run. The Dreid-ing force field82 was used to model interactions betweenthe zeolite and the OSDA. It has been widely used forOSDA screening41–43 and some of its predictions havebeen experimentally verified33,83. Initial FF optimiza-tion of unloaded zeolites was performed with the Sanders-Leslie-Catlow (SLC) parametrization.84 Whereas severalother parametrizations have been proposed for pure-silicazeolites,85–89 SLC is still widely used in the field due tothe good correlation of predicted energies with experi-mental enthalpies of formation.90

Initial zeolite structures were downloaded from the In-ternational Zeolite Association (IZA) database and pre-optimized using either DFT or SLC, as described above.Conformers for OSDAs were generated using RDKit91

with the MMFF94 force field,92,93 and further optimizedusing the BP86-D3/def2-SVP94–96 level of theory as im-plemented in ORCA.97,98 Subsequent optimizations ofthese geometries with VASP did not change the finalstructure of the isolated molecule. Nonetheless, theenergies derived from these calculations were necessaryto adopt the same reference method (PBE-D3/PAW)throughout the work. Generation of OSDA-zeolite poseswas performed using the VOID package.99 A completedescription of the docking strategy, software and param-eters is found in Ref. 99.

B. Calculation of binding energies

Typically, host-guest interactions are described interms of binding energies, since free energies of bindingare typically unavailable from simple simulations. A gen-eral expression for the binding energy (Eb) of a molecule

3

FIG. 1. a, Workflow of calculations performed to obtain different binding energies for each of the OSDA-zeolite pairs in thedataset. Resulting energies for poses (Ep), zeolite hosts (Eh) and OSDA guests (Eg) are shown in the figure. b, Schematic on

the calculation of E(frz)h and E

(frz)g energies within the frozen pose scheme. The host and guest geometries are kept fixed at the

relaxed positions obtained in the pose optimization and a single point calculation is carried for each of the isolated systems.

in a host is given by

Eb = Ep − Eh − nEg, (1)

in which Ep is the energy of the zeolite-OSDA pose, Eh

is the energy of the pure-silica, unloaded zeolite host, Eg

is the energy of the guest template, and n is the numberof guests per pose. Whereas the interaction energy be-tween docked guests could be subtracted from Eq. (1),it is often useful to include this term when computingthe stabilization provided by the OSDA towards the ze-olite. It is well-known that guest-guest interactions in-fluence the product zeolite, particularly when more thanone molecule is packed into the same cavity.39,100 There-fore, removing these contributions from the final binding

energy expression may not be representative of the actualstabilization obtained in experimental settings.

The literature contains multiple examples on how tocalculate each of the terms in Eq. (1). For example,the energies can be obtained by time-averaging the host-guest energies along an MD simulation,40,41,43,47,49,50,52

⟨E

(MD)b

⟩=

⟨E(MD)

p

⟩−

⟨E

(MD)h

⟩− n

⟨E(MD)

g

⟩, (2)

or by subtracting energies resulting from structural opti-mizations (opt),24,44,51

E(opt)b = E(opt)

p − E(opt)h − nE(opt)

g . (3)

4

Moreover, the resulting geometries from structural opti-mizations can be frozen in their relaxed positions, andthe energy components calculated for the isolated hostand guests without further relaxation,

E(frz)b = E(opt)

p − E(frz)h −

∑i

E(frz)g,i , (4)

where the summation is performed for all guests dockedin the host. In this scenario, there are different val-ues of Eh and Eg for each pose, since the final atomicstructure is dependent on the host-guest interactions.Here, we refer to this strategy, widely used in the zeoliteliterature,25,39,45,46,53,54 as the “frozen pose” method. Fi-nally, structural relaxations and MD simulations can beperformed at constant pressure or volume, adding an ad-ditional degree of freedom for each of these simulations.Fig. 1 schematizes different pathways for obtaining allthese energies.

III. RESULTS

A. Correlations between binding energy calculations

To compare the different methods of calculating bind-ing energies, we created a dataset of 272 zeolite-OSDAposes from 164 unique complexes, which cover 60 neu-tral OSDAs and 55 zeolite frameworks (Fig. S2 and Ta-bles S2-3). Different loadings were considered for pairsin which the molecule was small compared to the zeolitecavity, following the method described in Ref. 99. Onlyneutral OSDAs were simulated due to ambiguities on howto calculate DFT energies for charged systems withoutconsidering the presence of heteroatoms. It is unclearwhether the charge-compensating background potentialadded in the DFT calculation may affect the lattice pa-rameters and energy references for the pose. Further-more, typical approaches employ FFs without explicitlyconsidering charges, even for cationic molecules. There-fore, this dataset of neutral molecules docked in pure-silica zeolites allows exploring variations of binding ener-gies with a focus on OSDA/zeolite shapes and sizes.

DFT-optimized structures of zeolites and moleculeswere used as inputs for the docking scheme. We laterrepeated this docking step for some SLC-optimized zeo-lites as substrates to analyze the effects of different initialhost lattices on the binding energies (Sec. III C). For eachof the 272 poses created from DFT substrates, we carriedstructural optimizations at constant pressure using DFT,and calculated binding energies using Eq. 3. Nonethe-less, the physical reality is best described by a dynamicsimulation at the NPT ensemble, with the temperatureset to ranges of typical hydrothermal conditions. To ob-tain such binding energies from ab initio MD without in-curring into excessive computational cost, we performedAIMD simulations for 40 different complexes whose posescontained less than 80 atoms. All other energies listed in

Fig. 1 are calculated for all 272 poses. A summary ofthe calculation tree is shown in Fig. 2a (see Fig. S3 fora complete description).

After performing all calculations, binding energies ob-tained for the same initial docked structures were com-pared. If the property of interest in the benchmark werethe mean absolute error (MAE) between the methods,systematic shifts due to parametrization inaccuracies orenergy rescaling due to dynamic effects (e.g. samplingtemperature in MD simulations) would largely influencethe final results. Instead, a theoretical screening methodshould quantify trends of host-guest binding energies toinform OSDA selection, as in many other computationalscreening approaches.41,43,101 To quantify the correlationin the ordering between two different methods, the Spear-man’s rank correlation coefficient (ρ) is employed as afigure of merit. It correlates two variables according tothe rank of the data points, and is invariant to transla-tions and rescalings of the sets under comparison.102 Anincreasing, monotonic relationship between host-guest in-teractions from two different methods has ρ = 1. SinceDFT was chosen as the reference method, a higher cor-relation with DFT suggests that the method is better incapturing trends in binding energies.

Fig. 2b summarizes all pairwise correlation coeffi-cients. First, we observe that binding energies from DFToptimizations at constant pressure, henceforth denotedDFT (opt, P), correlate well with those from DFT (MD,P, N = 40) simulations (ρ = 0.82). The best FF strategyto calculate binding energies is the frozen pose method atconstant volume, whose correlation coefficient with DFT(MD, P) is 0.78. MD-derived energies from simulationswith the NVT ensemble have a similar correlation withDFT (MD, P) (ρ = 0.77). These values are in excellentagreement with the baseline of ρ = 0.82 from the twoDFT methods. The same trend is observed if DFT (opt,P, N = 272) energies are adopted as reference, with aneven higher statistical power due to the larger numberand diversity of poses. FF (frz, V) outperforms othermethods by achieving a correlation of ρ = 0.68 withDFT (opt, P) binding affinities, followed by FF (MD,V) (ρ = 0.55). Although MD simulations in principleallow sampling a larger fraction of molecular conforma-tions within the guest, average binding energies derivedfrom constant volume MD simulations are extremely cor-related with their frozen pose counterparts (ρ = 0.88, seealso Fig. S4). This suggests that further exploring thephase space beyond the local minimum does not signif-icantly change the trends in binding energies for mostcases. To ensure this subsampling effect was not due toshort trajectories, we increased the total time of the FF-based MD simulation to 30 ps, 5 ps of which were ded-icated to an initial equilibration run. Nevertheless, wedid not find significant changes in the average energiesobtained from longer trajectories. This result has impor-tant consequences. It demonstrates that frozen pose cal-culations are slightly better predictors of reference bind-ing energies than MD simulations while also being orders

5

1.00 0.82 0.54 0.52 0.54 0.77 0.63 0.78

0.82 1.00 0.29 0.22 0.30 0.55 0.43 0.68

0.54 0.29 1.00 0.92 0.70 0.45 0.47 0.36

0.52 0.22 0.92 1.00 0.68 0.40 0.52 0.30

0.54 0.30 0.70 0.68 1.00 0.60 0.40 0.62

0.77 0.55 0.45 0.40 0.60 1.00 0.70 0.88

0.63 0.43 0.47 0.52 0.40 0.70 1.00 0.63

0.78 0.68 0.36 0.30 0.62 0.88 0.63 1.00

Spearman’s ρ1.00.80.60.40.2

b

DFT

, P

DFT, P

FF, P

FF, P

MD MD MD

opt

opt

frz

frz frz

frz

opt

opt opt opt

MD

MD

MD

FF, V

FF, V

DFT PBE-D3

SLC FF

amolecule

conformer generation

zeolite + OSDA pose

Dreiding FF MD

DFT PBE-D3

IZA database

zeolite

Dreiding FF frz

Dreiding FF

structural optimization

single calculation

DFT BP86-D3

DFT PBE-D3 MD

DFT PBE-D3 MD

DFT PBE-D3

DFT PBE-D3 MD

FIG. 2. a, Dependency tree of the atomistic simulations performed in this work. The docking algorithm generates poses fromhost structures optimized using PBE-D3 or SLC and ligands optimized with BP86-D3. Then, subsequent calculations withDreiding and PBE-D3 are performed for each pose. b, Correlation matrix of binding energies calculated with different methods.The value of Spearman’s correlation coefficient is reported in each element of the matrix.

of magnitude faster to compute.

Additionally, we also observe in Fig. 2b that the corre-lation between FF binding energies from constant pres-sure calculations and DFT is much worse than the oneobtained by their constant volume counterparts. Al-though the physical reality is in principle better describedby a constant pressure constraint, the Dreiding FF doesnot correctly capture the behavior of the isolated silicateframeworks, often leading to unphysical distortions in thezeolite structure. As such, the configuration space sam-pled by FF calculations at constant pressure is furtherfrom the ground truth than that from constant volumecalculations, leading to much poorer predictions of bind-ing energy trends. Indeed, an analysis of the density ofpure-silica zeolites shows that constant pressure FF op-timizations lead to structures which are 45% denser, onaverage, than their experimental counterparts (see Fig.S5).

B. Stability of structural optimizers for reference hosts

Even at constant volume calculations, structural op-timizations of unloaded zeolites affect the binding en-ergies of zeolite-OSDA pairs by changing the host ref-erence energy Eh in Eq. (1). If the minimization al-gorithms employed in the atomic relaxation are unableto find the global energy minimum for a given struc-ture, then all binding energies for that zeolite will be

lower than the ground truth, as Eh > E(global)h . While

the stability of geometry optimization algorithms has

been studied before,103,104 their effects are investigatedhere to quantify the errors in binding energies of ze-olites. We compared the Dreiding energies of pure-silica, unloaded zeolites optimized at constant volumethrough four algorithms: the conjugate gradient (CG)method, rational function optimization (RFO), Broy-den–Fletcher–Goldfarb–Shanno algorithm (BFGS), andsymmetry-lowering method (Lower), all as implementedin GULP. To accelerate the convergence of the struc-tural relaxation, we started the optimization with theCG, BFGS or Lower methods, and switched to the RFOmethod when the norm of the gradient (|G|) was smallerthan a threshold of choice. In principle, if the algorithmswere equally effective in finding the global energy mini-mum, all structures would converge to the same groundstate. However, the results indicate that the algorithmsoften disagree on the equilibrium energies and geome-tries. Fig. 3 shows the distribution of energies for zeoliteswith respect to the minimum energy configuration for thesame framework found among all four optimization runs.No optimization scheme outperforms the others across allzeolites. Whereas at least 75% of the energy differencestend to be smaller than 1 kJ/mol SiO2, some algorithmstend to overestimate the energy of the ground state ofa zeolite by up to 100 kJ/mol SiO2. Changing the |G|threshold also leads to different energy minima. If we useBFGS as initial optimizer, but switch to RFO at differentvalues of |G|, the outcomes of the simulation are differ-ent. We did not find a threshold for |G| that outperformsothers, nor did we find significant advantage in not us-ing RFO altogether. In fact, avoiding switching to RFOoften increases the number of steps necessary to reach a

6

local energy minimum.

While optimization energies E(opt,V )h can get trapped

in local minima, MD simulations allow atoms to moveand avoid these higher energy traps. Thus, reference en-

ergies⟨E

(MD,V )h

⟩should be more robust to the optimiza-

tion methods used to generate the initial structures. Fig.

3 compares⟨E

(MD,V )h

⟩differences according to the al-

gorithm that generated the structure used as input forthe MD simulation. MD energies lower the differencesbetween the optimizers, but the discrepancies are still ashigh as 10 kJ/mol SiO2 for some systems. This suggeststhat structural relaxations can affect even time-averagedenergy references. Therefore, finding the global groundstate energy of different hosts is unlikely without a thor-ough combination of different minimizers. These system-atic shifts explain the existence of binding energies oflarge absolute value, such as the ones observed in ACOzeolite (see Table S3). Rather than exposing a flaw inthe calculation, they suggest that even after multiple op-timization attempts, the isolated host has not convergedto its ground state geometry. As a consequence, all bind-ing energies for that particular host are biased if Eq. (3)is used to compute the binding affinity of an OSDA to-wards a zeolite. When OSDAs are ranked across a singlezeolite framework according to their binding energy,99

this is often not a problem. Since the energy referenceis shifted by the same amount for all poses, the trendsof binding energies along a single host are preserved, butcomparison for different hosts is hindered. Nevertheless,non-systematic errors are undesirable in high-throughputdiscovery of materials and should be avoided. The roleof energy references in binding energy calculations doesnot alter the conclusions drawn from Fig. 2b. If we ana-lyze the correlations between binding energies on a singleframework (see Fig. S6 for an analysis on SOD zeolite),the same trends with respect to DFT energies are stillobserved. The only difference is that a larger correlationbetween FF binding energies at constant pressure andvolume is found, and also between FF and DFT bindingenergies at constant pressure.

C. Variability of binding energies according to the initialsubstrate parameters

Another constraint required in constant volume sim-ulations is fixing the host lattice parameters prior todocking. Since methods such as DFT and SLC lead todifferent equilibrium lattice constants, often with SLCpredicting denser zeolites (see Fig. S5b), host-guest in-teractions are also affected by the choices of unit cells.To compare the influence of the initial host parametersin the final binding energy, we performed the dockingprocedure a second time for 81 different complexes us-ing either the DFT or SLC geometry of the host (Fig.S3d) as an input. The VOID package generated an av-erage of 14 poses for each host geometry and guest con-

Lower + RFO

<10-2 10-1 100 101 102

CG + RFO (|G| < 0.10)

BFGS + RFO (|G| < 0.05)

BFGS + RFO (|G| < 0.15)

opt MD

E - Emin (kJ/mol SiO2)

FIG. 3. Distribution of Dreiding FF energies of unloaded zeo-lite frameworks (E) with respect to the minimum energy ob-tained among all optimization algorithms for the same frame-work (Emin). The shaded box represents the interquartilerange, the vertical line is the median, and the whiskers spanthe range of the distribution. DFT-optimized frameworkswere used as inputs for the Dreiding FF optimization.

former, which has been shown to generate binding ener-gies that qualitatively correlate with typical experimen-tal outcomes.99 Host energies were obtained by relaxingeach unloaded zeolite with the four optimization schemesshown in Fig. 3 and selecting the resulting energy min-imum as the reference value. Unit cells from DFT- andSLC-optimized frameworks were assigned different ref-erence energies, since all relaxations were performed atconstant volume. Then, we selected the strongest bindingaffinities among all poses created with the given guests,hosts and their lattice parameters.99 Fig. 4 comparesthe best binding energies for each complex according tothe starting host. Ideally, small changes in unit cell pa-rameters should not significantly affect the ground statehost-guest interactions, as long as the configuration spaceis thoroughly explored. However, we observe that opti-mization and MD binding energies vary significantly asa function of the initial substrate. The MAE betweenthe best binding energies is 52.7 and 50.3 kJ/mol OSDAfor opt and MD methods, respectively. In contrast, thebest binding energies from the frozen pose method areconsistent across different initial zeolite structures, witha MAE of 10.1 kJ/mol OSDA, five times better than theother methods. This suggests that binding energies fromthe frozen pose method are more robust to variations ofunit cell parameters, rendering them a desirable choicefor high-throughput computational workflows.

7

b c

Eb (SLC host)

E b (

DFT

hos

t)

Eb (SLC host) Eb (SLC host)

opt

MAE: 52.7 MAE: 50.3 MAE: 10.1

MD frza

-350 -175 0 175-350

-175

0

175

-350 -175 0 175 -350 -175 0 175

FIG. 4. Correlation between the minimum FF-based binding energy (Eb) for complexes with different initial host lattices, ascalculated by the a, optimization, b, MD, and c, frozen pose methods. The unloaded zeolites were independently optimizedusing DFT and FF-SLC before the docking (see Fig. 2a). The MAE indicates that the frozen pose method is more robust tovariations of the initial conditions. All values are given in kJ/mol OSDA.

IV. DISCUSSION

For decades, host-guest interactions have been mod-eled using a variety of methods. Yet, high-throughputscreening methods typically require selecting parametersthat yield computationally-efficient results and can be de-ployed robustly without manual supervision. While con-stant pressure simulations better represent the synthesisconditions of zeolites, Dreiding FF binding energies atthe NPT ensemble do not correlate well with their DFTcounterparts. Rather, FF simulations at constant vol-ume show good correlation with DFT optimizations andMD simulations at constant pressure. This might be alimitation on the Dreiding FF, which is unable to cor-rectly describe the unloaded zeolite framework when vol-ume relaxation is allowed. Several other general-purposeforce fields and parametrizations specific to zeolites havebeen proposed and could be benchmarked according tothe guidelines discussed here.

Even when binding energies are compared across dif-ferent simulation pathways and optimization methodswithin a single FF parametrization, results vary dras-tically. We have shown that energies from structureoptimizations and MD simulations are more susceptibleto initialization issues than frozen pose methods. Thehigher correlation between the latter and the DFT bind-ing energies is also supportive of this robustness. Further-more, contrary to intuition, a larger sampling of the con-figuration space through MD simulations does not nec-essarily lead to significant changes in trends of bindingenergies when compared to the frozen pose method. Inpractice, this conclusion opens an opportunity for sim-ulating zeolite-OSDA pairs in larger scales. One of themajor bottlenecks of zeolite-OSDA simulations is to sim-ulate long MD trajectories of guests docked inside thehost for a variety of loadings and initial configurations, ashas been typically done in screening works the field.41–43

We propose to replace MD simulations by frozen posemethods within FF calculations, drastically reducing thetime necessary to perform computations while increasingthe robustness of the final binding energy with respectto the choice of optimization algorithms and unloadedzeolite geometry. Even if FF (MD, V) binding ener-gies are better predictors of experimental outcomes thanDFT (MD, P), which has yet to be verified, the use ofthe frozen pose method is still justified by its correlationwith the former. Moreover, we show that the absolutevalues of the binding energy tend to be more transfer-able across different initial configurations through thismethod, suggesting it can also be used to compare theinfluence of each molecule in stabilizing different zeoliteframeworks.99

It is important to note that the chemical space usedin this analysis is comprised of neutral molecules. How-ever, most known OSDAs are positively charged and di-rect the formation of zeolites with heteroatoms in theirbackbone. Theoretical studies on how OSDAs affect theposition of the heteroatoms have been developed,105–110

although at a high computational cost. Nevertheless,we suggest that the current analysis should be transfer-able to cationic OSDAs as well. OSDAs are often mod-eled without charges in FFs, and vdW interactions tendto dominate the templating effects.2,13,24,33,41–43,45,83 Itis also assumed that trends in binding energy in pure-silica frameworks hold with changes in zeolite compo-sition. More rigorous analyses would be necessary tosimulate charged OSDAs in zeolites through DFT calcu-lations. Typical methods of charge-compensating back-ground potentials may shift energy differences dependingon the system, and combinatorial studies on heteroatomdistribution are prone to be computationally expensive.

Finally, benchmarks enable the development of high-throughput computation infrastructures by providingclear guidelines for simulating materials in large scales.

8

To support further dissemination of these ideas, we arereleasing the Python interface to the GULP code used toperform these calculations as the package GULPy, as wellas the data generated in this article.111 They lay downstandards to test and automate calculation workflows ofOSDAs and zeolites with increased reliability.

V. CONCLUSIONS

In summary, we benchmarked different methods to cal-culate binding energies of OSDAs in zeolites by perform-ing DFT and FF calculations for 272 zeolite-OSDA pairs.We showed that Dreiding FF binding energies calculatedwith the frozen pose method correlate best with DFT en-ergies. This method offers additional robustness to thebinding energy with respect to the choice of geometryoptimization algorithms and initial docking conditions.On the other hand, a larger sampling of the phase spacefrom MD simulations does not provide significant bene-fits, since MD-based binding energies correlate very wellwith those from the frozen pose method. Remarkably,simulations at constant volume significantly outperformthose at constant pressure within the Dreiding FF. Thismight result from the inability of this parametrization tocorrectly model the unloaded, pure-silica zeolite struc-ture. These results provide reliable parameters for high-throughput computation of binding energies for zeolitesand OSDAs. This benchmark, code and data aims toenable robust, large-scale screenings of OSDAs with sig-nificantly less computational overhead.

VI. ACKNOWLEDGEMENTS

This work was supported by the MIT Energy Initiative(MITEI) and MIT International Science and TechnologyInitiatives (MISTI) Seed Funds. D.S.-K. was addition-ally funded by the MIT Energy Fellowship. We thankE. Olivetti, M. Moliner, Y. Roman-Leshkov, Z. Jensen,S. Kwon, and S. Bagi for the fruitful discussions. Thecomputations in this paper were executed at the Mas-sachusetts Green High-Performance Computing Centerwith support from MIT Research Computing.

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111Available under the MIT License at https://github.com/

learningmatter-mit/Zeolite-Reproducibility-Binding.

download fileview on ChemRxiv20210120_Zeolite-Binding-Benchmark.pdf (2.43 MiB)

Supporting Information for: Benchmarking binding energy calculations for organic

structure-directing agents in pure-silica zeolites

Daniel Schwalbe-Koda and Rafael Gomez-Bombarellia)

Department of Materials Science and Engineering, Massachusetts Institute of

Technology, Cambridge, MA 02139

(Dated: 20 January 2021)

a)Electronic mail: rafagb@mit.edu

1

I. SUPPLEMENTARY FIGURES

50 100 150 200 250 300 350 400 450Time (fs)

-0.1

0.0

0.1

0.2

0.3

0.4

Δ E (1

0-3

kJ/m

ol)

Figure S1. Gradients of the averaged energy for all AIMD trajectories studied in this article.

∆E = E(tn+1) − E(tn). The average energy E is defined by taking the running average of the

potential energy of the system using a window size of 9 time steps. The fact that all gradients

oscillate between ±10−4 kJ/mol after 300 fs suggests that the trajectories are well-equilibrated to

enable binding energies to be extracted.

2

H2NNH2

NH

NH

NH2H2N

N

N

NH

HN

NH

NH2

HN

NH2

O NH

N NHNH2H2N

NH2H2N

H2N NH2

HN

N

NH2

N

N

N

HN

NH

NN

NHN

HN NH N

NN

NH

H2N

HN

NH

NH2

N

P

N

HN

HN

N

HON

OH

OH

O

O

O

O

O

HN

HN

NH2

N

N

N

N

NH2

NH2

H2N

NH2

NH2

NH2

NH2

NN

NH2

O

O

O

O

O

O

N

NH2

NH

HN

N

NH

N N

NN

NH2NH

H2NNN

PNH2

H2N

Figure S2. Neutral OSDAs studied in this article. All zeolite-OSDA pairs are described in Table

S3.

3

structural optimization

single calculation

OSDA

conformer generation

DFT BP86-D3

DFT BP86-D3

Eg optV/P

FF

Eg optV/P

DFT

Eg MDV/P

DFT

Eg MDV/P

FF

DFT PBE-D3

zeolite

zeolite

a b

c dOSDA

IZA database

SLC FF

DFT PBE-D3

DFT PBE-D3 MD

Dreiding FF

Dreiding FF MD

DFT PBE-D3

DFT PBE-D3 MD

Dreiding FF

Dreiding FF MD

Eh optV/P

FF

Eh optP

DFT

Eh MDP

DFT

Eh MDV/P

FFDreiding FF

Dreiding FF MD

Eh optV

FF

Eh MDV

FF

DFT PBE-D3

zeolite + OSDA

VOID docking

pose

DFT PBE-D3 MD

Dreiding FF

Dreiding FF MD

Dreiding FF frz

VOID docking

DFT BP86-D3

zeolite OSDA

SLC FF

VOID docking

Ep optV/P

FF

Ep optP

DFT

Ep MDP

DFT

Ep MDV/P

FF

Ep frzV/P

FF

Figure S3. Dependency tree of calculations performed for a, zeolites, b OSDAs, and c poses in

this work. The docking step from c was performed independently with DFT- or SLC-optimized

substrates, as shown in d. Energies derived from each step follow the notation from Fig. 1 in the

main text.

4

Figure S4. Pairwise relationships between binding energies calculated from different methods. Each

point corresponds to each one of the 272 initial poses. Energies are given in kJ/mol OSDA.

5

a

b0

5

10

15

Nor

mal

ized

Fre

quen

cy Dreiding / IZASLC / IZA

1.0 1.2 1.4 1.6 1.8Density Ratio

0

20

40

60

Nor

mal

ized

Fre

quen

cy IZA / DFTSLC / DFT

Figure S5. Distribution of densities from a Dreiding force field (FF)- and b density functional

theory (DFT)-optimized zeolites normalized by the experimental densities. All frameworks are

taken in their pure-silica, unloaded forms. Optimizations are carried at constant pressure.

6

Figure S6. Pairwise relationships between binding energies calculated from different methods for

35 different poses having SOD zeolite as the host. Both the a, Spearman’s correlation coefficient

and b, the pair plots show mostly the same trends of Fig. 2 and S4.

7

II. SUPPLEMENTARY TABLES

Table S1: k-points mesh for each of the zeolites studied in this work. All meshes were constructed

using a uniform k-point density of 64 k-points/A−3.

Host k-points mesh Host k-points mesh Host k-points mesh

ACO 2 × 2 × 2 AEL 3 × 1 × 1 AFI 1 × 1 × 3

AFN 1 × 1 × 2 AFO 2 × 1 × 3 AFR 1 × 1 × 3

AFY 2 × 2 × 3 ASV 2 × 2 × 1 ATN 1 × 1 × 4

ATO 1 × 1 × 5 ATS 1 × 1 × 4 AWO 2 × 1 × 1

AWW 1 × 1 × 3 BEC 1 × 1 × 1 BPH 2 × 2 × 2

CGF 1 × 1 × 3 CHA 1 × 1 × 1 CZP 2 × 2 × 1

DFT 3 × 3 × 2 DOH 1 × 1 × 2 EAB 1 × 1 × 1

GME 1 × 1 × 2 GON 1 × 1 × 5 JRY 3 × 2 × 1

JSW 1 × 2 × 1 LAU 1 × 1 × 3 LEV 1 × 1 × 1

LOS 2 × 2 × 2 LTA 2 × 2 × 2 MOR 1 × 1 × 3

MRE 3 × 1 × 1 MTW 1 × 4 × 2 NAT 1 × 1 × 3

OFF 2 × 2 × 3 OWE 1 × 3 × 2 PHI 2 × 1 × 1

PON 2 × 2 × 1 RHO 1 × 1 × 1 RRO 3 × 2 × 1

RTE 1 × 1 × 3 RTH 2 × 1 × 2 RUT 1 × 1 × 2

SAS 1 × 1 × 2 SAV 1 × 1 × 2 SBN 3 × 3 × 1

SFE 2 × 4 × 1 SFN 1 × 4 × 1 SFO 1 × 1 × 3

SOD 2 × 2 × 2 SSY 4 × 1 × 1 STI 1 × 1 × 1

THO 1 × 3 × 3 TON 1 × 1 × 4 VET 1 × 1 × 5

ZON 3 × 1 × 1

Table S2: SMILES strings for each OSDA studied in this work.

Guest ID Guest SMILES

1 CN

2 CNC

3 CCN

Continued on the next page

8

Guest ID Guest SMILES

4 NCCN

5 CCCN

6 CCNC

7 CCNCC

8 NCCCN

9 CCCCN

10 CC(C)N

11 CN(C)C

12 CP(C)C

13 CCCNCCC

14 C1CCNC1

15 CCN(C)C

16 CC(C)CN

17 NCCNCCN

18 C1CCNCC1

19 C1COCCN1

20 c1ccncc1

21 C1CNCCN1

22 CN1CCCC1

23 Cn1ccnc1

24 NC1CCCC1

25 C1CNCCNC1

26 CN1CCNCC1

27 CCN(CC)CC

28 C1CCCNCC1

29 CCP(CC)CC

30 NC1CCCCC1

31 CC1CCNCC1

Continued on the next page

9

Guest ID Guest SMILES

32 CN1CCCCC1

33 c1c[nH]cn1

34 NCCNCCNCCN

35 C1CCCNCCC1

36 CC(C)NC(C)C

37 Cc1ncc[nH]1

38 C[C@H](N)CN

39 C1CN2CCN1CC2

40 CN(C)CCN(C)C

41 C1CN2CCC1CC2

42 OCCN(CCO)CCO

43 C[C@@H](N)CN

44 CN1CCN(C)CC1

45 CN(C)c1ccncc1

46 C[C@H](CN)CCCN

47 C[C@@H](CN)CCCN

48 CCN(C(C)C)C(C)C

49 CC(C)(C)CC(C)(C)N

50 C1N2CN3CN1CN(C2)C3

51 CN1C(C)(C)CCCC1(C)C

52 N[C@H]1CC[C@H](N)CC1

53 C1COCCOCCOCCOCCOCCN1

54 C1COCCOCCOCCOCCOCCO1

55 C[C@H]1CNC[C@H](C)C1

56 C[C@@H]1CNC[C@H](C)C1

57 C1C[C@H]2CC[C@@H]1CNC2

58 N[C@@H]1C[C@H]2CC[C@@H]1C2

59 CN1CCCN(C)CCN(C)CCCN(C)CC1

60 N[C@]12C[C@H]3C[C@H](C[C@H](C3)C1)C2

10

Table S3: Binding energies (in kJ/mol OSDA) for each one of the 272 poses studied in this paper.

SMILES strings for each one of the guest IDs are given in Table S2. Loading is the number of

OSDAs per unit cell in that pose.

Host Guest Loading DFT

MD

P

DFT

opt

P

FF

MD

P

FF

opt

P

FF

frz

P

FF

MD

V

FF

opt

V

FF

frz

V

ACO 4 1 -28.9 -54.4 -35.4 -2034.8 112.2 -12.4 -1232.2 -39.2

ACO 4 2 - -48.6 285.0 -703.0 68.3 -13.6 -608.1 -40.2

ACO 4 3 - -39.7 344.2 -293.3 69.7 -2.1 -383.9 -24.9

AEL 36 1 - -102.0 287.4 507.3 -26.9 -126.7 75.0 -152.0

AEL 7 1 - -105.5 -38.4 1.3 -61.0 -112.8 -11.6 -115.3

AFI 18 1 - -112.0 -99.5 -68.6 -130.6 -81.2 -35.7 -86.8

AFI 39 1 - -108.8 -181.7 -195.0 -156.8 -72.1 -71.9 -99.5

AFI 25 1 - -125.8 -42.5 -45.3 -156.7 -90.9 -108.3 -102.0

AFI 19 1 - -103.3 -150.2 -152.8 -132.5 -60.3 -88.9 -78.0

AFI 10 1 - -90.6 -106.8 -117.6 -118.9 -61.5 -81.1 -70.4

AFI 5 1 - -97.5 -162.3 -169.4 -114.5 -54.7 -76.5 -67.7

AFI 26 1 - -108.2 -132.3 -139.9 -155.5 -66.0 -44.4 -94.2

AFI 2 1 - -29.7 -93.3 -107.8 -86.6 -20.5 -24.8 -53.4

AFI 2 3 - -63.2 6.2 23.5 -14.9 -43.4 -51.2 -55.2

AFI 47 1 - -139.7 -106.7 -29.8 -179.9 -105.2 -144.1 -127.1

AFI 46 1 - -139.1 -196.0 -181.2 -171.1 -108.2 -149.2 -137.6

AFI 52 1 - -141.7 -186.4 -201.9 -192.7 -106.6 -138.6 -128.3

AFN 10 5 - -46.4 376.1 367.0 58.6 56.5 46.2 -18.1

AFN 8 1 - -82.5 129.4 115.9 -65.5 -108.8 -117.0 -123.2

AFO 13 1 - -108.2 427.6 343.0 -18.0 -95.5 -138.0 -150.2

AFR 20 2 - -47.9 -99.8 -106.7 -107.3 -65.5 -75.4 -75.6

AFY 9 1 -44.8 -46.6 -47.2 123.7 -102.6 -47.2 -16.2 -80.2

AFY 13 1 -62.7 -69.3 -29.0 145.9 -130.2 -77.6 -13.3 -98.5

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P

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V

FF

opt

V

FF

frz

V

AFY 27 1 -77.5 -68.5 21.1 110.6 -135.6 -83.6 36.7 -106.1

ASV 2 1 -95.4 -69.1 -56.6 -65.6 -77.6 -76.2 -85.2 -85.6

ASV 2 2 - -69.0 -58.4 -68.4 -82.9 -78.2 -86.4 -86.5

ATN 27 1 56.3 36.3 1847.8 1840.7 360.1 426.5 411.7 91.9

ATO 11 2 - -74.7 86.9 136.9 11.7 -48.4 -151.8 -79.7

ATS 36 1 - -131.8 279.0 318.5 -55.2 -71.4 -69.0 -117.9

ATS 36 2 - -87.8 339.4 324.6 -43.5 -82.4 -67.6 -102.0

ATS 48 1 - -84.6 625.3 639.8 53.3 -123.3 -136.9 -109.7

AWO 14 8 - -65.4 378.1 288.9 23.4 17.7 -12.5 -50.3

AWO 7 4 - -55.8 353.4 266.1 16.5 -81.0 -69.1 -88.9

AWW 28 1 - -105.1 332.3 322.8 11.8 -136.3 -133.1 -139.9

BEC 39 4 - -95.0 -45.8 -57.6 -95.8 -96.2 -104.5 -118.0

BEC 40 1 - -103.0 -135.3 -141.6 -159.8 -117.6 -124.1 -124.9

BPH 27 1 - -52.9 -177.2 -231.7 -143.0 -76.4 -94.3 -95.9

BPH 27 2 - -73.1 -60.5 -82.5 -112.6 -90.4 -104.4 -107.9

CGF 33 4 - -79.9 -5.4 2.4 -69.1 -89.5 -95.9 -96.5

CHA 14 3 - -61.2 27.7 -63.9 -127.3 -52.3 -81.8 -85.2

CHA 18 2 - -71.8 -16.1 -96.6 -119.5 -85.1 -99.9 -103.8

CHA 21 2 - -72.6 -100.4 -149.2 -125.4 -99.3 -103.5 -103.7

CHA 19 2 - -66.9 -79.4 -158.5 -124.9 -86.1 -95.7 -95.9

CHA 36 2 - -98.8 1.3 -61.8 -144.8 -119.2 -127.2 -127.6

CHA 48 2 - -125.1 100.4 17.9 -115.6 -139.2 -152.0 -162.2

CHA 22 2 - -76.7 -141.6 -229.2 -116.6 -85.9 -98.3 -98.5

CHA 56 2 - -106.6 20.1 -42.0 -122.1 -121.0 -134.1 -134.9

CHA 23 5 - -38.3 149.7 105.9 -6.8 -53.4 -25.1 -52.4

CHA 23 7 - -30.0 98.1 78.9 -27.8 -77.8 -79.0 -86.9

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P

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MD

V

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opt

V

FF

frz

V

CHA 33 7 - -45.6 -10.9 -36.4 -59.5 -71.3 -77.4 -81.2

CZP 34 1 - -69.0 115.3 316.1 -27.2 -169.7 -4.7 -118.3

DFT 21 1 46.5 -4.5 838.1 853.2 191.8 150.4 138.8 58.6

DFT 2 1 -78.8 -74.1 187.1 194.1 36.8 -49.2 -47.5 -53.9

DFT 4 1 -89.4 -76.8 284.4 420.1 28.2 -64.8 -76.8 -88.8

DOH 41 6 - 87.4 1748.7 1771.4 380.8 799.5 800.8 390.9

EAB 18 3 - -87.5 -73.2 -83.5 -26.6 -90.4 -112.1 -117.2

EAB 39 3 - -98.0 145.4 170.2 -2.0 -85.7 -97.8 -119.0

EAB 21 3 - -83.9 -73.9 -63.3 -71.3 -88.1 -109.1 -118.2

EAB 19 3 - -84.4 -51.1 63.3 -78.3 -103.2 -112.0 -111.6

EAB 5 4 - -72.8 -90.8 24.9 -61.5 -76.2 -96.0 -98.9

EAB 13 3 - -83.3 129.7 225.2 18.8 -93.5 -77.0 -107.1

EAB 27 3 - -95.3 111.1 115.0 3.9 -91.7 -110.7 -116.3

EAB 4 3 - -63.9 -114.9 -123.2 -82.2 -76.6 -95.2 -100.6

GME 39 1 - -80.4 159.5 155.5 -46.3 -85.7 -99.2 -99.3

GME 21 1 - -59.0 9.1 14.1 -80.9 -69.3 -81.0 -85.3

GME 15 3 - -62.4 229.5 224.9 3.7 -49.0 -49.7 -63.7

GME 15 4 - -57.9 193.0 199.4 -5.6 -60.6 -49.4 -67.3

GME 6 5 - -40.0 211.6 223.9 2.2 -56.8 -48.1 -58.2

GME 7 5 - -30.2 304.0 396.6 33.1 -33.4 -23.4 -49.9

GME 29 1 - -86.6 67.3 72.8 -90.6 -98.0 -102.6 -113.2

GME 29 2 - -71.5 397.8 402.6 -31.0 -38.8 -47.4 -93.5

GME 11 4 - -57.8 92.1 88.8 -33.8 -65.3 -73.8 -74.7

GME 56 2 - -76.4 393.3 396.9 1.5 -47.0 -44.4 -85.0

GME 37 3 - -77.9 183.6 172.9 -38.6 -81.7 -92.3 -95.8

GME 34 1 - -103.5 58.0 77.0 -136.3 -131.5 -142.4 -155.2

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GME 20 3 - -84.5 113.8 112.0 -46.4 -91.8 -101.0 -101.4

GON 39 1 - -48.0 461.9 492.9 110.3 215.1 93.9 -42.7

JRY 7 1 - -68.9 312.3 404.5 14.9 -102.8 -129.8 -113.0

JRY 7 2 - -97.3 100.5 226.8 1.6 -104.9 -137.7 -114.1

JSW 39 3 - -102.1 295.7 289.3 9.2 -101.8 -111.9 -129.9

JSW 39 4 - -98.2 278.8 271.8 0.3 -97.6 -107.8 -126.0

LAU 14 1 - -84.0 43.7 51.5 -78.1 -72.0 -97.3 -101.6

LAU 14 2 - -81.2 12.7 -23.3 -100.9 -72.9 -100.1 -104.4

LAU 41 2 - -69.7 196.6 186.3 -36.2 -99.2 -112.2 -121.2

LAU 39 1 - -84.3 219.0 269.9 -6.7 -97.2 -110.0 -118.1

LAU 20 1 - -85.6 -51.2 -56.3 -124.2 -94.3 -105.1 -105.3

LEV 28 5 - -110.0 3.3 103.8 -45.7 -132.2 -114.1 -143.5

LOS 19 3 - -44.9 663.9 806.3 172.8 135.3 127.1 28.6

LOS 5 3 - -50.7 354.4 338.4 146.5 101.1 209.4 7.7

LOS 7 3 - 1.0 687.4 685.9 239.2 228.0 285.3 59.0

LOS 11 4 - -14.8 657.0 644.9 218.3 184.9 176.2 72.9

LOS 42 2 - -102.4 830.7 834.2 152.3 -14.8 154.4 -68.8

LOS 20 3 - -65.5 348.5 338.1 171.1 50.0 40.7 -4.9

LOS 20 4 - -49.1 558.6 749.1 173.9 119.4 114.2 48.7

LTA 35 1 - -47.4 -152.2 -160.5 -167.5 -95.1 -99.0 -106.5

LTA 21 1 - -33.1 -110.0 -121.3 -121.8 -75.5 -86.0 -86.3

LTA 53 1 - -151.0 -11.1 -26.8 -233.3 -182.0 -172.7 -214.5

LTA 50 1 - -51.0 -150.5 -160.1 -160.3 -85.8 -103.2 -103.5

LTA 50 2 - -54.8 687.5 675.2 179.9 44.8 31.5 -16.4

LTA 30 3 - 28.7 671.1 682.0 154.7 -14.5 188.0 43.9

LTA 42 1 - -66.0 -187.8 -189.0 -190.1 -108.4 -109.4 -112.5

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MOR 20 7 - -32.9 353.6 301.7 52.3 8.8 21.0 -35.4

MRE 3 1 - -55.9 17.0 8.1 -54.1 -76.5 -136.4 -72.4

MTW 39 1 - -129.9 329.3 448.8 40.4 181.4 158.6 -106.9

MTW 49 1 - -84.6 1065.7 1052.8 237.1 324.1 252.6 -70.0

MTW 16 2 - -120.9 -0.7 -0.4 -78.4 -95.5 -64.9 -109.4

NAT 39 1 - 11.6 1677.9 1668.0 308.4 353.7 366.3 116.6

NAT 39 4 - 11.8 1045.4 1040.7 188.3 484.0 505.9 215.5

NAT 4 1 -15.8 -64.3 627.0 666.8 113.1 22.5 30.7 -67.3

NAT 4 2 - -70.5 369.4 362.1 51.9 -55.9 -59.7 -77.8

NAT 4 4 - -61.7 271.7 310.1 -3.7 -55.7 -20.0 -82.3

OFF 18 2 - -93.5 430.4 421.1 -71.1 -99.7 -114.0 -115.5

OFF 39 2 - -91.0 531.2 518.4 34.0 -75.8 -91.1 -103.0

OFF 21 2 - -87.0 358.1 418.8 -50.7 -92.0 -104.5 -114.2

OFF 48 1 - -109.8 516.9 504.9 26.3 -116.0 -127.0 -143.6

OFF 11 2 - -67.6 200.2 191.2 3.7 -70.6 -82.5 -82.8

OFF 20 3 - -4.7 645.2 635.2 162.5 221.4 217.4 83.2

OWE 43 2 -94.3 -89.4 93.5 121.2 -60.4 -112.7 -120.4 -126.1

OWE 38 1 -68.6 -82.9 327.0 319.8 -45.1 -110.7 -111.2 -126.7

PHI 18 1 - -59.4 712.0 211.7 65.8 -47.3 -44.6 -69.9

PHI 39 1 - -68.9 1024.9 549.3 121.6 41.1 -25.5 -65.5

PHI 39 2 - -72.2 849.9 584.1 57.4 34.8 -31.3 -70.6

PHI 21 7 - -30.3 616.9 525.2 73.6 168.6 167.2 40.3

PHI 19 1 - -67.2 1161.0 650.6 34.9 -80.6 -92.3 -96.9

PHI 44 2 - -48.7 937.7 681.6 86.4 14.8 -1.8 -57.3

PHI 33 2 - -79.6 368.3 41.8 -57.3 -63.8 -94.9 -95.0

PON 1 3 - -53.9 11.7 11.5 -47.5 -58.1 -14.7 -65.4

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RHO 54 2 - -146.2 -189.0 -56.8 -251.2 -91.9 -157.1 -194.2

RRO 1 1 -28.8 -42.9 -36.8 -45.8 -62.1 -50.0 -56.1 -62.8

RRO 1 2 - -57.4 -32.8 -38.9 -66.8 -52.1 -58.8 -62.3

RRO 2 1 -38.2 -60.8 -11.4 -18.9 -66.4 -60.2 -71.4 -76.3

RRO 2 2 - -56.3 -33.8 -38.1 -71.7 -65.4 -73.3 -77.1

RTE 57 1 - -126.2 221.5 198.3 -2.5 -153.4 -153.9 -159.4

RTE 57 2 - -128.0 247.2 238.0 -45.4 -145.9 -170.7 -170.3

RTE 51 1 - -112.0 847.5 817.0 174.8 -94.0 -192.0 -142.2

RTE 51 2 - -118.0 733.7 658.0 84.6 -75.2 -59.5 -117.2

RTE 58 1 - -124.7 246.0 56.5 -37.1 -137.6 -162.8 -164.5

RTH 28 1 - -85.7 315.0 332.5 -167.8 -121.5 62.2 -119.9

RTH 28 2 - -86.6 14.9 15.5 -163.9 -174.4 -29.6 -123.0

RTH 18 1 - -67.9 243.0 257.3 -152.8 -50.4 4.4 -98.6

RTH 21 2 - -62.3 18.9 7.9 -163.1 -99.6 -48.3 -100.9

RTH 36 1 - -92.6 315.0 304.5 -131.6 -169.1 52.2 -123.6

RTH 36 2 - -92.7 312.6 300.0 -125.7 -91.8 -34.4 -124.2

RTH 31 1 - -77.5 79.4 88.8 -150.5 -58.7 4.5 -117.2

RTH 32 2 - -78.8 143.1 143.3 -155.3 -70.3 -20.7 -113.8

RTH 56 1 - -99.8 653.9 647.0 -143.3 -83.4 43.0 -134.8

RTH 55 2 - -95.2 118.5 227.4 -153.1 -99.2 -43.8 -135.8

RTH 20 2 - -57.3 97.0 89.9 -136.1 -74.8 -87.2 -87.3

RUT 14 4 - -93.1 227.6 211.6 -52.9 -74.2 -97.3 -122.9

SAS 18 1 - -63.5 -159.8 -143.0 -137.3 -80.0 -104.6 -105.0

SAS 59 1 - -34.7 1220.0 1659.8 184.7 -152.3 -111.2 -202.4

SAS 4 1 - -37.6 -125.2 -129.0 -133.4 -61.9 -82.7 -85.0

SAS 4 2 - -44.5 -126.8 -135.3 -137.2 -98.7 -87.8 -88.3

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SAV 36 4 - -96.9 84.7 120.8 -52.1 -104.9 -117.4 -122.1

SAV 27 5 - -57.0 133.5 273.1 -4.1 -100.4 -108.7 -116.0

SBN 1 1 -37.0 -51.7 81.2 69.3 -1.9 -48.1 -48.1 -63.7

SBN 1 3 - -58.6 72.7 63.3 -10.7 -47.9 -57.8 -60.5

SBN 17 1 -20.4 -41.0 619.8 606.8 95.3 17.4 37.9 -48.8

SBN 17 2 - -59.9 528.2 532.1 17.6 33.4 24.4 -40.6

SBN 20 1 -39.5 -15.7 492.7 493.7 138.6 55.8 50.2 6.0

SBN 20 2 - -27.7 469.0 466.7 101.3 66.0 56.3 14.5

SBN 20 3 - 35.2 456.4 445.1 81.9 126.4 118.3 59.1

SFE 45 1 -121.8 -145.8 -118.7 -66.1 -81.4 -87.6 -148.5 -127.7

SFN 35 1 - -176.6 -76.3 -98.2 -126.5 -99.8 -55.9 -117.9

SFN 35 2 - -126.3 -5.1 0.4 -112.6 -57.2 -21.4 -116.3

SFN 27 1 - -165.6 -54.6 -89.4 -105.6 109.6 92.2 -107.4

SFN 27 2 - -107.4 71.8 45.5 -109.4 -10.7 -26.6 -107.3

SFO 45 1 - -88.4 -140.2 -146.0 -154.5 -103.6 -104.2 -104.7

SOD 14 1 -87.5 -109.6 308.3 563.5 98.8 -50.6 -84.5 -90.0

SOD 39 1 -64.8 -94.6 697.8 687.4 361.3 34.0 248.9 -31.7

SOD 39 2 - -100.9 597.4 586.2 159.3 27.3 221.4 -53.1

SOD 19 1 -74.8 -118.6 689.2 679.1 172.3 -75.0 374.7 -114.9

SOD 50 1 -34.0 -57.4 1056.0 1045.4 476.5 125.2 114.9 61.7

SOD 36 1 87.4 90.9 1343.9 1330.4 474.3 322.7 316.8 134.7

SOD 3 1 -41.4 -100.4 -438.6 -118.9 45.5 -75.8 -88.0 -89.9

SOD 3 2 - -88.4 -1.3 -11.3 0.6 -75.8 -87.0 -88.8

SOD 48 1 257.2 191.3 2058.9 2879.7 584.3 872.4 1059.2 440.5

SOD 48 2 - 173.8 1581.8 1571.3 347.2 840.7 1002.8 479.1

SOD 27 1 73.0 24.0 1214.0 1332.3 457.7 136.3 326.2 136.7

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SOD 11 2 -60.8 -80.3 24.4 16.7 90.1 -62.6 -72.0 -75.5

SOD 22 1 -77.7 -84.3 269.3 260.8 280.9 -13.5 -26.6 -57.9

SOD 12 1 -50.1 -85.5 -141.5 -148.5 181.0 -74.5 -81.7 -88.5

SOD 12 2 - -80.9 52.8 46.3 79.6 -78.1 139.0 -95.1

SOD 37 1 -46.1 -96.3 116.2 107.3 217.8 -14.6 -20.0 -47.7

SOD 37 2 - -79.3 224.6 215.7 102.5 -5.6 -7.0 -39.6

SOD 4 1 -75.8 -117.4 -204.1 -21.9 140.5 -76.6 -41.8 -68.6

SOD 17 1 16.1 -34.0 930.2 922.7 373.7 200.6 201.5 23.7

SOD 60 1 63.3 5.2 2173.7 2222.9 566.3 639.8 628.7 333.5

SOD 60 2 - -14.0 1408.2 1397.4 282.6 638.2 628.0 341.7

SOD 33 1 -90.2 -124.5 -394.3 -403.9 82.5 -83.5 -91.7 -92.6

SOD 33 2 - -101.4 59.1 48.0 4.9 -84.4 -94.5 -95.3

SOD 20 1 -73.2 -107.3 193.4 361.0 161.3 -49.0 -63.2 -70.5

SOD 20 2 - -92.3 197.2 399.9 60.8 -66.1 143.1 -87.3

SSY 14 1 - -144.0 223.3 200.9 -120.8 2.6 159.4 -81.5

SSY 14 2 - -111.6 -1.4 -25.7 -108.1 -39.1 -72.2 -76.0

STI 27 8 - -79.4 226.9 242.0 -31.6 -98.4 -95.6 -118.6

THO 43 1 -65.0 -74.3 276.6 273.2 28.5 -30.7 -46.3 -82.1

THO 43 2 - -49.8 340.7 311.5 12.8 -52.5 -24.2 -65.5

THO 38 1 -3.7 -44.6 450.8 517.3 76.7 -62.2 77.9 -53.5

THO 8 2 -64.0 -62.6 302.4 300.9 24.9 -33.4 -19.7 -67.1

THO 4 1 -55.6 -74.7 140.2 111.4 -0.7 -70.8 -75.8 -89.1

TON 20 1 - -141.0 -85.8 30.2 -6.2 -77.5 -97.9 -84.8

VET 24 1 -94.3 -77.0 26.9 51.9 -37.9 -106.9 -119.2 -124.0

ZON 39 2 - -83.3 559.7 550.7 69.1 -36.2 -51.9 -80.9

ZON 33 1 - -84.9 -20.5 -8.5 -51.1 -84.4 -92.2 -92.7

ZON 33 4 - -83.7 -26.6 -36.8 -70.7 -91.9 -103.0 -103.1

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