e-science technologies in the simulation of complex materials l. blanshard, r. tyer, k. kleese s. a....
TRANSCRIPT
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e-Science Technologies in the Simulation of Complex Materials
L. Blanshard, R. Tyer, K. Kleese
S. A. French, D. S. Coombes, C. R. A. CatlowB. Butchart, W. Emmerich – CSH. Nowell, S. L. Price – Chem
eMaterials
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Combinatorial Computational Catalysis
Polymorphismprediction of prediction of polymorphspolymorphs – – a drug substance may exist a drug substance may exist as two or more crystalline as two or more crystalline phases in which the phases in which the molecules are packed molecules are packed differently. differently.
Acid Sites in Zeolites
explore which sites are involved in explore which sites are involved in catalysiscatalysis – – used in used in diverse diverse industries including petroleum, industries including petroleum, chemical, polymers, chemical, polymers, agrochemicals, and environmental. agrochemicals, and environmental.
N
CH3
NO2
NO2
H
CH3
OH
H
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Combinatorial Computational Catalysis
Acid Sites in Zeolites
explore which sites are involved in explore which sites are involved in catalysiscatalysis – – used in used in diverse diverse industries including petroleum, industries including petroleum, chemical, polymers, chemical, polymers, agrochemicals, and environmental. agrochemicals, and environmental.
Polymorphismprediction of prediction of polymorphspolymorphs – – a drug substance may exist a drug substance may exist as two or more crystalline as two or more crystalline phases in which the phases in which the molecules are packed molecules are packed differently. differently.
N
CH3
NO2
NO2
H
CH3
OH
H
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• simulations take too long to run• data are distributed across many sites and systems• no catalogue system• output in legacy text files, different for each program • few tools to access, manage and transfer data• workflow management is manual• licensing within distributed environment
e-Science Issues to Address
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Acid Sites in Zeolites
•Determine the extra framework cation position within the zeolite framework.
•Explore which proton sites are involved in catalysis and then characterise the active sites.
•To produce a database with structural models and associated vibrational modes for Si/Al ratios.
•Improve understanding of the role of the Si/Al ratio in zeolite chemistry.
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Chabazite: 1T site, 12 Si centres per unit cell, 8 membered ring channels (3.8Å * 3.8Å).
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Si/Al – 11 = 4
Si/Al – 5 = 160
Si/Al – 3 = 5760
Si/Al – 2 = 184,320
The number of calculations quickly becomes an issue when realistic Si/Al ratios are considered.
A Si/Al ratio of 2 would require 184,320 calculations at ~100 second each.
= 5120.0 hours = 213 days of cpu time.
The Problem
When substitution of a second Al is considered there are now 4 * (10 * 4) possible structures as symmetry has been broken.
Note this is for a very simple zeolite with 36 ions per unit cell, materials of interest have 296.
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A combined MC and EM approach has been developed to model zeolitic materials with low and medium Si/Al ratios. Firstly Al is inserted into a siliceous unit cell and then charge compensate with cations.
MC/EM
-122
91.3
8-1
2290
.88
-122
90.3
8
Initial structures
Latti
ce e
nerg
y (e
V)
-122
95.3
2-1
2295
.22
-122
95.1
2-1
2295
.02
Final structures
Latti
ce e
nerg
y (e
V)
-122
91.3
8-1
2290
.88
-122
90.3
8
Initial structures
Latti
ce e
nerg
y (e
V)
-122
95.3
2-1
2295
.22
-122
95.1
2-1
2295
.02
Final structures
Latti
ce e
nerg
y (e
V)
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Name OpSys Arch State Activity LoadAv Mem ActvtyTime
[email protected] IRIX65 SGI Owner Idle 1.192 128 3+03:01:[email protected] IRIX65 SGI Unclaimed Idle 0.000 507 0+00:15:09ising2.ri.ac. LINUX INTEL Unclaimed Idle 0.200 501 [?????]vm1-16@strutt1-4 OSF1 ALPHA Owner Idle 1.113 1024 0+0:26:46xp2.ri.ac.uk OSF1 ALPHA Owner Idle 1.113 256 49+12:26:46xp3.ri.ac.uk OSF1 ALPHA Unclaimed Idle 0.000 256 0+00:55:00d8.ri.ac.uk WINNT40 INTEL Unclaimed Idle 0.000 255 0+02:09:45ATLANTIC WINNT51 INTEL Unclaimed Idle 0.008 256 0+01:02:30BABBLE.ri.ac. WINNT51 INTEL Unclaimed Idle 0.252 512 0+00:22:57D500.ri.ac.uk WINNT51 INTEL Owner Idle 0.533 254 0+05:26:06PCDAVIDC.ri.a WINNT51 INTEL Unclaimed Idle 0.000 504 0+03:51:26e-sam.ri.ac.u WINNT51 INTEL Unclaimed Idle 0.001 512 0+03:16:39pcalexey.ri.a WINNT51 INTEL Unclaimed Idle 0.002 256 0+00:35:53
Machines Owner Claimed Unclaimed Matched Preempting
ALPHA/OSF1 18 1 0 1 0 0 INTEL/LINUX 1 0 0 1 0 0 INTEL/WINNT40 1 0 0 1 0 0 INTEL/WINNT51 14 1 0 5 0 0 SGI/IRIX65 22 15 0 7 0 0
Total 56 17 0 15 0 0
RI Condor Pool
We have set up and tested a Condor pool at the RI, which has 50+ heterogeneous nodes from desktop PC’s, machines controlling instruments to main servers of the DFRL.
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Name OpSys Arch State Activity LoadAv Mem ActvtyTime
[email protected] IRIX65 SGI Owner Idle 1.192 128 3+03:01:[email protected] IRIX65 SGI Unclaimed Idle 0.000 507 0+00:15:09ising2.ri.ac. LINUX INTEL Unclaimed Idle 0.200 501 [?????]vm1-16@strutt1-4 OSF1 ALPHA Owner Idle 1.113 1024 0+0:26:46xp2.ri.ac.uk OSF1 ALPHA Owner Idle 1.113 256 49+12:26:46xp3.ri.ac.uk OSF1 ALPHA Unclaimed Idle 0.000 256 0+00:55:00d8.ri.ac.uk WINNT40 INTEL Unclaimed Idle 0.000 255 0+02:09:45ATLANTIC WINNT51 INTEL Unclaimed Idle 0.008 256 0+01:02:30BABBLE.ri.ac. WINNT51 INTEL Unclaimed Idle 0.252 512 0+00:22:57D500.ri.ac.uk WINNT51 INTEL Owner Idle 0.533 254 0+05:26:06PCDAVIDC.ri.a WINNT51 INTEL Unclaimed Idle 0.000 504 0+03:51:26e-sam.ri.ac.u WINNT51 INTEL Unclaimed Idle 0.001 512 0+03:16:39pcalexey.ri.a WINNT51 INTEL Unclaimed Idle 0.002 256 0+00:35:53
Machines Owner Claimed Unclaimed Matched Preempting
ALPHA/OSF1 18 1 0 1 0 0 INTEL/LINUX 1 0 0 1 0 0 INTEL/WINNT40 1 0 0 1 0 0 INTEL/WINNT51 14 1 0 5 0 0 SGI/IRIX65 22 15 0 7 0 0
Total 56 17 0 15 0 0
RI Condor Pool
But where is PC-CRAC???
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-12090
-12070
-12050
ConfigurationsT
E (
eV
)
full
100
50
20
10
5
single
Level of Optimisation
50eV
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-12090
-12070
-12050
-12030
-12010
-11990
-11970
-11950
-11930
-11910
-11890
-11870
-11850
TE
(eV
)
full
100
50
20
10
5
single
Level of Optimisation
240eV
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MOR
Mordenite – • 1 dimensional channel system• simulation cell contains two unit cells• 296 atoms, with 96 Si centres (referred
to as T sites).• Substituting 8 T sites with 8 Na cations
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GulpWinXP
Gulp Files
Workflow
MC_subs
Perlscript
MS Excel
SRB
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GulpWinXP
Gulp Files
Workflow II
MC_subs
Perlscript
MS Excel
SRB
Si-zeo structureInteratomic potsInput file
Batch of labelled Gulp files
C++
f90
Scommands
Subset of data in formatted file
Script autobatch sub
Script for cleaning dirs
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Extensive use of Condor pools (UCL ~950 nodes in teaching pools). ~150 cpu-years of previously unused compute resource have been utilised in this study. Close collaboration with the NERC e-minerals project has allowed access to this resource.
150,000 calculations have been performed each with varying numbers of particles per simulation box, which means a total of ~75,000,000 particles have been included in our simulations of Mordenite to date.
Condor Stats
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Jobs submitted in 1,000 job batches – issue of stability. Shadows – not my game but a pain when Condor Master dies due to too many jobs hitting the queue (guilty feeling as Master was not solely running pool but also being used for science by pool administrator. Maximum number of jobs in queue.
Condor Specifics
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Handling of data and analysis becomes RDS.However, keeping the pool full of jobs is also a tedious step when jobs are short, which is the ideal for the UCL pool (re: turning off pool once a day) – drip feeding.
Condor Specifics
Thought in application design is key – many on UCL pool are TOTALLY unsuitable for UCL Condor Pool.
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MOR
Mordenite – • 1 dimensional channel system• simulation cell contains two unit cells• 296 atoms, with 96 Si centres (referred
to as T sites).• Substituting 8 T sites with 8 Na cations
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0-12085
-12083
-12081
-12079
-12077
-12075
-12073
-12071
-12069
-12067
-12065
ConfigurationsT
ota
l E
ner
gy
(eV
)
5350
5370
5390
5410
5430
5450
5470
5490
5510
5530
5550
Cel
l V
ol.
full_TE
full_Vol
5 per. Mov. Avg. (full_TE)
5 per. Mov. Avg. (full_Vol)
It can be seen that there are two distinct regions, -12079eV to -12076eV and -12075eV to -12073eV, but there is no obvious correlation between total energy and cell volume.
100
100 Configurations
20eV
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-12090
-12085
-12080
-12075
-12070
-12065
configurationsT
E
5350
5400
5450
5500
5550
VO
L
TE
VOL
200 per. Mov. Avg. (TE)
200 per. Mov. Avg. (VOL)
However, when 10,000 structures are considered it is clear that the most stable structures correspond to cation placements that do not cause the cell to expand. This requires that the cations sit in the large channel.
0 10000
10000 Configurations
25eV
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Energy_eV-12085 -12080 -12075 -12070 -12065 -12060 -12055 -12050 -12...
5350
5400
5450
5500
5550
5600
10000 Configurations
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Comparison of Regions
-12079.5eV -12075.04eV
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Interrogation ofdatabase
Energy and gnorm within
desired range?
NO
YES
No further analysis
Path to file (held indatabase) selected to
text file
Text file used as inputfor script calling analysis program
for each path.
Analysis
mysql, allows input from a text file, C/C++ program or mysql command line and GUI
Properties: Total energy, cell volume, lattice parameters, T-O distances, T-O-T bond angles, cation-framework oxygen distances, coordination of user specified species etc.
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GulpWinXP
Gulp Files
Workflow III
MC_subs
SRB
db
mysql
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Property Good Bad
Lattice Energy(eV)
< -12070 > -12068
Al-Na average distance (Å)
> 3.6 < 3.4
cell volume(Å3)
< 5420 > 5475
average cation – Oxygen (Å)
> 2.75 < 2.65
Building an Ensemble
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Validation
Comparison with experiment is very promising showing a large difference in the quality of the fit between ‘good’ set and ‘bad’.
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Is the job queued?
Start
Loop through all jobs in database
YES
NO
Queue jobs and update database
Search directory treefor output files
Found files?
Find energyupdate databasechange filename
YES
All jobsqueued?
NO
Set status basedon energy table
Any jobs'IDLE'
NO
YES
EndYESNO
Monitor
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Jobs
Analysis
Model/ConfigurationGenerator
Distributed Computing
Portal
SteeringImprove generation / model
strategy
User Input: Structural modelSi/Al, cation types, [H2O] etc.
User Input: Diffraction data, chemical analysis, building units, Si/Al, cation types, [H2O] etc.
Analysis(geometry, energy, fit)
D. Lewis, R. Coates, S. FrenchUCL Chem / RI
Drip Feeding and Interactive Steering using Relational Databases
db
db
db
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Workflow IV
SSH CML CML
CML
CML
Workflow service needs to be exposed to outside world as a web service
Since we require new WSDL interfaces for each application it is a perfect opportunity to employ a standard representation for chemical structures.
XML standard in Chemistry is CML (Chemical Markup Language)
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We are now doing science that was not possible before the advancements made within e-Science.
Key Achievement
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FER
Ferrite – • 2 dimensional channel system• simulation cell contains 115 atoms.• substituting at 4 T sites with 4 Na cations
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Only 75 out of 100 configurations optimise
-4400
-4398
-4396
-4394
-4392
-4390
-4388
-4386
1 11 21 31 41 51 61 71
TE
in
eV
1950
1970
1990
2010
2030
2050
2070
2090
2110
Configurations
Vo
l
TE eV
Vol
5 per. Mov. Avg. (Vol)
5 per. Mov. Avg. ( TE eV)
14eV
100 Configurations
Again there are steps in Total Energy and again this time no correlation with volume for the low number of configurations.
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-4400
-4398
-4396
-4394
-4392
-4390
-4388
-4386
1 1001 2001 3001 4001 5001 6001 7001 8001
TE
in
eV
1950
1970
1990
2010
2030
2050
2070
2090
2110
2130
2150
Configurations
Vo
l
TE
Vol
200 per. Mov. Avg. (TE)
200 per. Mov. Avg. (Vol)
15eV
10000 Configurations
However, this time when 10,000 structures are considered there are no clear steps in the volume. The volume still increases with decreasing stability but this is due to cell expansion caused by Al to Al interactions.
Only 7500 out of 10000 optimise
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Comparison of Regions
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Comparison of Regions
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MFI
ZSM5 – • 3 dimensional channel system• simulation cell contains 292 atoms• substituting at 4 sites with 4 Na cations
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-12215
-12214
-12213
-12212
-12211
-12210
-12209
-12208
-12207
-12206
-12205
1 1001 2001 3001 4001
TE
in
eV
5250
5270
5290
5310
5330
5350
5370
5390
Configurations
Cel
l V
olu
me
TE
Vol
200 per. Mov. Avg. (Vol)
200 per. Mov. Avg. (TE)
10eV
10000 Configurations
There is a step in Total Energy but this time only one and from then the trend is smooth.
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When confirmed the lowest energy positions of Al the cation is exchanged for a proton and again energy minimised.
This method will allow us to construct realistic models of low and medium Si/Al zeolites. Such structures can be used for further simulations and aid the interpretation of experimental data.
What Next
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BaTiO3
Solid Solutions
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Solid Solutions
BaSrTiO3
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Solid Solutions
SrTiO3
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• upload files as part of workflow to SRB• generate metadata• upload extracted data from files• more extensive use of CML
Ongoing and Future Work
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We are now doing science that was not possible before the advancements made within e-Science.
Key Achievement
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1. First use of CML schema for defining Web Service port types.2. Calculation of 50,000 configurations of zeolite Mordenite (24,000,000 particles) to gain insight into structure when a realistic ratio of Al substitution is included in model.3. Successfully exposed Fortran codes as OGSI Web Services - prototype application deployed on 80 nodes. The prototype computational polymorph application is being ported to a larger production machine.4. First use of BPEL standard for orchestrating web services in a Grid application.5. Open Source BPEL implementation in development enabling late binding and dynamic deployment of large computational processes.6. Integration of OGSI and BPEL with Sun Grid Engine.7. Development of Graphic User Interface for polymorph application - connects to relational database via EJB interface.8. Infrastructure for metadata and data management9. SRB and dataportal are already being used to hold datasets and being used for transferring the data between different scientists and computer applications.10. Implementation of Condor pool at Ri.
Achievements To Date
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Polymorph Prediction
Different crystal structures of a molecule are called polymorphs.
Polymorphs may have considerably different properties(e.g. bioavailability, solubility, morphology)
Polymorph prediction is of great importance to the pharmaceutical industry where the discovery of a new polymorph during production or storage of a drug may be disastrous
Drug molecules are often flexible and this makes the polymorph prediction process more challenging…
N
CH3
NO2
NO2
H
CH3
OH
H
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MOLPAK Generation of ~6000 densely packed crystal
structures using rigid molecular probe
DMAREL Lattice energy optimisation
For flexible molecules: conformational optimisation
n feasible rigid molecular probes representing energetically plausible conformers
Data : Unit cell volume, density, lattice energy
Restricted number of structures selected crystal structures and properties stored in
Database
Morphology
n times
n = number of conformers
Polymorph Prediction Workflow
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Store data files from simulations in the Store data files from simulations in the Storage Resource BrokerStorage Resource Broker
Storage Resource Broker
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We are now doing science that was not possible before the advancements made within e-Science.
Key Achievement