New Xavier Rozanska Computational Chemistry for Pollutant...
Transcript of New Xavier Rozanska Computational Chemistry for Pollutant...
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© Materials Design, SARL 2017
A Cleaner World with MedeA®
Xavier Rozanska
Computational Chemistry for Pollutant MitigationIFPEN, Rueil-Malmaison, March 13th, 2017
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OutlineMaterials Design
High-throughput in MedeA• Molecular and Fluids Properties
Integration in MedeA• Reactions in Fluid
• Metal organic framework
• Gas separation
Summary
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Introducing Materials Design, Inc.
Leader in atomic-scale materials modeling− MedeA® modeling software
− Materials properties service
− Research services
Founded in 1998 by a team of experts Success with 400+ global companies and institutions
Leader in atomic-scale materials modeling− MedeA® modeling software
− Materials properties service
− Research services
Founded in 1998 by a team of experts Success with 400+ global companies and institutions
© Materials Design, SARL 2017
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Integrated Modeling With MedeA®
Designed for productivity in engineering applications
Unique integration of quantum (ab initio) and classical forcefield methods
Foundation of multi-scalemodeling
High throughputscreening
Computationalefficiency (HPC)
Designed for productivity in engineering applications
Unique integration of quantum (ab initio) and classical forcefield methods
Foundation of multi-scalemodeling
High throughputscreening
Computationalefficiency (HPC)
© Materials Design, SARL 2017
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High-throughput:Molecular and Fluids Properties
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Research projects: High throughput property calculations for 100s of compounds
• Published results:
Big Data & Atomistic Simulations: Validation
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Simplification and standardization: Use of Flowcharts & Structure lists for Cp,id calculation
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High-throughput: Heat Capacity
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, , , ,Ideal gas
heat capacityResidual heat
capacity
From quantum chemistry (MOPAC) on isolated molecule
From MD or MC simulation of a condensed phase
Total heat capacity
, ,
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High-throughput: Normal Boiling Point Temperature (pure compounds)
Samples shown in the plot: ~100
AAE of the Tb calculated by GEMC simulation from the DIPPR* data, is 1.4%.
More than half of the compounds have an Absolute Deviation (AD) of the Tb below 1.0%.
MedeA®-GIBBS simulations
Exp. data: Wilding WV, Rowley RL, Oscarson JL. DIPPR project 801 evaluated process design data. Fluid Phase Equil. 1998;150–151:413–420
© Materials Design, SARL 2017
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Integration:Reactions in Fluid
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MedeA Integration: Communication between Tools and Methods
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VASP FFO LAMMPS
GIBBSElectrostaticpotential of
(porous) solids
ForcesEnergiesStresses
FF parameters
GaussianESP
charges
FF inter-operability
© Materials Design, SARL 2017
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Catalytic Ethylene FormylationReaction
Reaction conditions • CO/H2+C2H4 200-300 bar
• T=383-473 K
Catalyst• Tetracarbonyl cobalt
hydride
− HCo(CO)4
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O
CO/H2
Catalyst
Reaction mechanisms
© Materials Design, SARL 2017
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Molecular modeling analysis
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1
2
3
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CCSD(T)/CBS//B3LYP/aug-cc-pVTZ + anharmonic frequency calculations/B3LYP/aug-cc-pVTZ
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Molecular modeling analysis
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Reaction fluid thermophysical properties with MD and MC at 200 bar Density of supercritical H2/CO/C2H4 Residual Cp of supercritical H2/CO/C2H4
Diffusion of H2/CO/C2H4 at 473 K and 200 barMolc. log10(D)H2 ‐2.13CO ‐2.58C2H4 ‐2.66D is in cm2 s‐1
Diffusion of C2H4
at different T
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Integration:Characterization of Solids and their Interaction with Fluids
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Integration: Communication betweenTools and Methods
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VASP FFO LAMMPS
GIBBSElectrostaticpotential of
solids
ForcesEnergiesStresses
FF parameters
GaussianESP
charges
FF inter-operability
© Materials Design, SARL 2017
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MOF functionalization with amino-acids
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Applications:-enantioselective catalysis-adsorption/diffusion selectivity
Are the amino-acid groups fully free?(hopefully not!)If they are constrained: what is their conformation?
Considered functional groups:-GlycineProline and -Proline
T. Todorova, X. Rozanska, C. Gervais, A. Legrand, L. N. Ho, P. Berruyer, A. Lesage, L. Emsley, D. Farrusseng, J. Canivet, and C. Mellot-Draznieks, Chem. Eur. J. 2016, 22, 16531-16538
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Conformations in peptide-grafted MOF
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Developping missing FF parameters from VASP to MM
Validating the FF parameters with adsorption isotherm (MC) and density (NPT MD)
T. Todorova, X. Rozanska, C. Gervais, A. Legrand, L. N. Ho, P. Berruyer, A. Lesage, L. Emsley, D. Farrusseng, J. Canivet, and C. Mellot-Draznieks, Chem. Eur. J. 2016, 22, 16531-16538
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MD+ab initio+experimental data
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Proline GlycineProline
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Integration: Communication betweenTools and Methods
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VASP FFO LAMMPS
GIBBSElectrostaticpotential of
solids
ForcesEnergiesStresses
FF parameters
GaussianESP
charges
FF inter-operability
© Materials Design, SARL 2017
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Application: CO2/N2 separation by ALPO and modified ALPO (defects)
Aluminophosphatematerial: ZON
1: SiO22: AlPO43: Al30Si4P30O1284: NaAl32SiP31O128
The cells are optimized using VASP. The ab initio electrostatic potentials are computed with VASP. They are used in Grand Canonical Monte Carlo simulation (GIBBS).
Source: Rozanska et al. Oil Gas Sci. Tech. 2013, 68, 299-307
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Application: CO2/N2 separation by ALPO and modified ALPO
1: SiO22: AlPO43: Al30Si4P30O1284: NaAl32SiP31O128
Molecular CO2/N2 ratio from the Adsorption Isotherms of CO2 and N2 in ZON.
Source: Rozanska et al. Oil Gas Sci. Tech. 2013, 68, 299-307
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SummaryHigh-throughput & Integrated Atomistic Simulations
• Validation
• Simplification
• Standardization
• Integration and information exchange
• Multiscaling
Applications examples
• Properties & Big Data
• Catalysis and Reactions
• Characterization
• Separation
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Acknowledgements
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Caroline Mellot-Draznieks and Tanya Todorova (et al.), Collège de France –CNRS (MOF)
Materials Design’s colleagues (MedeA-Gibbs of organic fluids: Cp,res and Tb)
Dr Marianna Yiannourakou
Dr Philippe Ungerer
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Credits
© Materials Design, Inc. 2017 25
VASP: The VASP Group, Theoretical Physics Department, University of Vienna • Kresse, G., & Hafner, J. (1993). Ab initio molecular dynamics for liquid metals. Physical Review
B, 47(1), 558.• Kresse, G., & Furthmüller, J. (1996). Efficiency of ab-initio total energy calculations for metals
and semiconductors using a plane-wave basis set. Computational Materials Science, 6(1), 15-50.
LAMMPS: S. Plimpton, Fast Parallel Algorithms for Short-Range Molecular Dynamics, J Comp Phys, 117, 1-19 (1995), www.lammps.sandia.govGIBBS: License IFP-EN – LCP (CNRS – Université Paris Sud)• P Ungerer, C Beauvais, J Delhommelle, A Boutin, B Rousseau, AH Fuchs, The Journal of
Chemical Physics 112 (12), 5499-5510• E. Bourasseau, M. Haboudou, A. Boutin, A.H. Fuchs, P Ungerer, The Journal of chemical
physics 118 (7), 3020-3034• A.D. Mackie, .B Tavitian, A. Boutin, A.H. Fuchs, Molecular Simulation 19 (1), 1-15• M. Lagache, P. Ungerer, A. Boutin, A.H. Fuchs, Physical Chemistry Chemical Physics 3 (19),
4333-4339• E. Bourasseau, P. Ungerer, A. Boutin, A.H. Fuchs, Molecular Simulation 28 (4), 317-336• N. Ferrando, A. Boutin and V. Lachet, Journal of Physical Chemistry 114: 8680-8688, (2010).
MOPAC2012: James J. P. Stewart, Stewart Computational Chemistry, Colorado Springs, CO, USA, HTTP://OpenMOPAC.net (2012) MedeA®: MedeA: Materials Exploration and Design Analysis; Materials Design, Inc. 2017. www.materialsdesign.com
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Backup slides
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High-throughput: SimplificationMedeA-LAMMPS Flowchart• 4 modules ~ 4 parameters)
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LAMMPS input command file• 130 lines
© Materials Design, SARL 2017
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High-throughput: Standardization
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High-throughput: Standardization
29© Materials Design, SARL 2017