Understanding catalysts from first-principles: tackling...
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Understanding catalysts from first-principles:tackling challenges in modeling catalysts
Bryan R. Goldsmith
Fritz Haber Institute of the Max Planck SocietyBerlin, Germany
CH3ReO3 + H2O2 + cyclohexenein aqueous acetonitrile
dispersed metal ionson amorphous SiO2
TM
Si
O
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Catalysis has a profound impact on everyday life
Value-added chemicals from biomassFertilizer
Synthesis of ammonia from its elementsFritz Haber, Nobel Lecture (1920)
Commodity chemical production Pharmaceutical industry
Y. Wang , S. De, N. Yan, Chem. Commun., 52, 6210 (2016)
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Modeling can provide deep insights into catalysisGold nanoparticle on TiO2 for CO oxidation
[1] J. Saavedra, H. Doan, C. Pursell, L. Grabow, B. Chandler, Science 345 (2014)
ExperimentsElementary step kinetics
In-situ reactivityStructure
Chemisorption
TheoryTransition States
IntermediatesStructureDynamics
[1]
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Computationalscreening
First-principles microkinetic modeling
Catalyst designCatalyst characterization
Computationalspectroscopy
First-principlesthermochemistry &kinetic parameters
Reaction mechanism
Mechanistic understanding
Active sites
The dream: computer-aided catalyst design
“Towards the computational design of solid catalysts” J. K. Nørskov et al., Nat. Chem. 1 (2009)
De
scripto
r
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Many challenges remain for modeling catalysts
Representative models
Thermodynamics and kinetics with chemical accuracy
Complexity of solution
Liquid/solid interfaces
[1] G. Rupprechter and C. Weiland, Nano Today 2 (2007)[2] B. N. Zope, D. D. Hibbitts, M. Neurock, R. J. Davis, Science 330 (2010)
[2] [1]
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length
time
f s p s n s μ s m s s hours years
Continuum Equations,Rate Equations
and Finite Element
Modeling
ElectronicStructureTheory
ab initioMolecularDynamics
ab initio
kinetic Monte Carlo
m
mm
μm
nm
density-
functional
theory
Multi-scale approach for modeling catalysts
Image courtesy of Karsten Reuter, TU Munich
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Outline. Three projects that help address the below challenges
Challenge 2: Understanding the importance of solvent during homogeneous catalysis
Mo/SiO2 active site
Challenge 1: Modeling heterogeneity in reactivity of amorphous catalysts
Challenge 3: Finding reliable descriptors of catalysts (and materials)
CH3ReO3
ReO O
CH
HH
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Mo/SiO2 active site
Challenge 1: Modeling heterogeneity in reactivity of amorphous catalysts
Outline. Three projects that help address the below challenges
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Amorphous catalysts are importantbut not well-understood due to their disorder
H. S. Taylor, Proc. R. Soc. A 108 (1925)C. Yoon and D. Cocke, J. Non-Crystalline Solids 79 (1986)B. Peters and S. L. Scott, J. Chem. Phys. 142 (2015)
Mo(VI) complex on SiO2
Mo
Si
O
Dispersed metal ionson amorphous supports
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Lack of well-defined structure
Diversity of catalyst site environments
Preparation dependent properties
Amorphous catalysts are importantbut not well-understood due to their disorder
H. S. Taylor, Proc. R. Soc. A 108 (1925)C. Yoon and D. Cocke, J. Non-Crystalline Solids 79 (1986)B. Peters and S. L. Scott, J. Chem. Phys. 142 (2015)
Challenges
Mo(VI) complex on SiO2
Mo
Si
O
Dispersed metal ionson amorphous supports
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Lack of well-defined structure
Diversity of catalyst site environments
Preparation dependent properties
Amorphous catalysts are importantbut not well-understood due to their disorder
H. S. Taylor, Proc. R. Soc. A 108 (1925)C. Yoon and D. Cocke, J. Non-Crystalline Solids 79 (1986)B. Peters and S. L. Scott, J. Chem. Phys. 142 (2015)
Challenges
Open questions
How do typical active and dead sites differ?
How to sample the distribution of sites?
How to build representative models?Mo(VI) complex on SiO2
Mo
Si
O
Dispersed metal ionson amorphous supports
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from structure to properties
Slab modelsCluster models
British Zeolite Association webpage http://www.bza.org/K. Reuter and M. Scheffler, Phys. Rev. B 65 (2001)P. E. Sinclair et al., J. Chem. Soc. Faraday Trans. 94 (1998)
Real system Model system
Cut, cap and constrainUnder periodic boundary conditions
Typical modeling protocols for crystalline catalysts
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Typical modeling protocols for amorphous catalysts
A. Fong et al., ACS Catal. 5 (2015); J. Handzlik, J. Phys. Chem. 111 (2007)
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Typical modeling protocols for amorphous catalysts
Approach 1. Assume one or two cluster models capture main features
A. Fong et al., ACS Catal. 5 (2015); J. Handzlik, J. Phys. Chem. 111 (2007)
Cr
Cr
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Typical modeling protocols for amorphous catalysts
Approach 1. Assume one or two cluster models capture main features
Approach 2. Use a crystalline slab as amorphous support surrogate
A. Fong et al., ACS Catal. 5 (2015); J. Handzlik, J. Phys. Chem. 111 (2007)
Cr
Cr
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Our approach. Combine small cluster models with a sequential quadratic programming algorithm
Goal: sample heterogeneity in reactivity of amorphous catalysts
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Our approach. Combine small cluster models with a sequential quadratic programming algorithm
Mo/SiO2 cluster model
Goal: sample heterogeneity in reactivity of amorphous catalysts
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Fluorine = constrained periphery atoms (xp)
Our approach. Combine small cluster models with a sequential quadratic programming algorithm
Mo/SiO2 cluster model
Goal: sample heterogeneity in reactivity of amorphous catalysts
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Where is the best place
to constrain the periphery atoms?
Fluorine = constrained periphery atoms (xp)
Our approach. Combine small cluster models with a sequential quadratic programming algorithm
Mo/SiO2 cluster model
Goal: sample heterogeneity in reactivity of amorphous catalysts
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Where is the best place
to constrain the periphery atoms?
Fluorine = constrained periphery atoms (xp)
Our approach. Combine small cluster models with a sequential quadratic programming algorithm
Mo/SiO2 cluster model
Goal: sample heterogeneity in reactivity of amorphous catalysts
Esite(xp) subject to E‡(xp) = E‡
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Where is the best place
to constrain the periphery atoms?
Fluorine = constrained periphery atoms (xp)
Our approach. Combine small cluster models with a sequential quadratic programming algorithm
Mo/SiO2 cluster model
Goal: sample heterogeneity in reactivity of amorphous catalysts
Esite(xp) subject to E‡(xp) = E‡
Position of periphery atoms
Activation energy some valueEnergy of catalyst site
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Esite(xp) subject to E‡(xp) = E‡
Sample the lowest energy catalyst sitefor each value of activation energy
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‘Dial’ the activation energy
Inverse design: property to structure
Sequential QuadraticProgramming
New catalyst site structure
Step in activation energy
Esite(xp) subject to E‡(xp) = E‡
Sample the lowest energy catalyst sitefor each value of activation energy
E‡ xp
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Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
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Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
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Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 27: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/27.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
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Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 29: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/29.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 30: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/30.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 31: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/31.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 32: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/32.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 33: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/33.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
![Page 34: Understanding catalysts from first-principles: tackling ...cheresearch.engin.umich.edu/goldsmith/...catalysts.pdf · Modeling can provide deep insights into catalysis Gold nanoparticle](https://reader034.fdocuments.us/reader034/viewer/2022043006/5f912badc405c149fa7a92db/html5/thumbnails/34.jpg)
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
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common
Activation energy,
Site
fo
rmat
ion
en
ergy
Example of algorithm on model energy landscape
New catalyst site structure
Step in activation energy
E‡ xp
Sample the lowest energy sitefor each value of E‡
B. R. Goldsmith et al., in Reaction Rate Constant Computations, Royal Society Chemistry (2013)
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Ethene metathesis by isolated Mo(VI)/SiO2
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Ethene metathesis by isolated Mo(VI)/SiO2
Metallacycle rotation
Trigonal bipyramidal Square pyramidalJ. Handzlik, J. Phys. Chem. 111 (2007)
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Ethene metathesis by isolated Mo(VI)/SiO2
Metallacycle rotation
Trigonal bipyramidal Square pyramidalJ. Handzlik, J. Phys. Chem. 111 (2007)
Examined structure-sensitivityusing 10 large models
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Examine structure-sensitivity toward metallacycle rotation
Six fluorine atoms = xp = constrained atoms for reactant, transition state, and product
E‡
B. R. Goldsmith, E. D. Sanderson, D. Bean, B. Peters, J. Chem. Phys. 138 (2013)
Metallacycle rotationTrigonal bipyramidal Square pyramidal
Our small cluster model for use with the algorithm
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dead sites
active × abundant= important
Rotation barrier, E‡ (kJ/mol)
Site
fo
rmat
ion
ener
gy (
kJ/m
ol)
Wide range in the metallacycle rotation barrier exists(E‡ = 28 to 72 kJ/mol)
E‡
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dead sites
active × abundant= important
Rotation barrier, E‡ (kJ/mol)
Site
fo
rmat
ion
ener
gy (
kJ/m
ol)
Wide range in the metallacycle rotation barrier exists(E‡ = 28 to 72 kJ/mol)
E‡
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10
sla
b m
od
els dead sites
active × abundant= important
Rotation barrier, E‡ (kJ/mol)
Site
fo
rmat
ion
ener
gy (
kJ/m
ol)
Wide range in the metallacycle rotation barrier exists(E‡ = 28 to 72 kJ/mol)
E‡
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“compressed”active
“stretched” inactive
Rotation barrier, E‡ (kJ/mol)
Si-S
i dis
tan
ce
(An
gstr
om
s)
Identify structure-activity descriptors of catalyst sites
E‡
rSi-Si
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E‡
Compare one simple cluster model to five large slab models
C. Ewing et al., Ind. Eng. Chem. Res. 55 (2016)
Five large slab models
One small cluster model
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E‡
Compare one simple cluster model to five large slab models
Small cluster model captures thetrend and ≈90% of the variance
C. Ewing et al., Ind. Eng. Chem. Res. 55 (2016)
Symbolsslab models
Black line our algorithm
Si-Si Distance (Angstroms)
Ro
tati
on
bar
rier
, E
‡(e
V)
Five large slab models
One small cluster model
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Outline. Three projects that help address the below challenges
Challenge 2: Understanding the importance of solvent during homogeneous catalysis
CH3ReO3
ReO O
CH
HH
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Homogeneous vs. amorphous catalysts
Homogeneous catalystsAmorphous catalytic solidsSingle active siteStructurally well-definedAmenable to spectroscopySolvent environment
Diversity of active sites Lack of long-range orderLess amenable to spectroscopy
A. Moses J. Am. Chem. Soc. 129 (2007); J. Pelletier and J. Basset, Acc. Chem. Res. 49 (2016)
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Homogeneous vs. amorphous catalysts
Homogeneous catalystsAmorphous catalytic solidsSingle active siteStructurally well-definedAmenable to spectroscopySolvent environment
Diversity of active sites Lack of long-range orderLess amenable to spectroscopy
CH3ReO3/SiO2-Al2O3
CH3ReO3
in CH3CN(aq)
A. Moses J. Am. Chem. Soc. 129 (2007); J. Pelletier and J. Basset, Acc. Chem. Res. 49 (2016)
Active alkene metathesis catalyst Not active for alkene metathesis
ReReRe
Al
Si
O O
C
HHH
O
C
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Homogeneous vs. amorphous catalysts
Homogeneous catalystsAmorphous catalytic solidsSingle active siteStructurally well-definedAmenable to spectroscopySolvent environment
Diversity of active sites Lack of long-range orderLess amenable to spectroscopy
CH3ReO3/SiO2-Al2O3
CH3ReO3
in CH3CN(aq)
A. Moses J. Am. Chem. Soc. 129 (2007); J. Pelletier and J. Basset, Acc. Chem. Res. 49 (2016)
Active alkene metathesis catalyst Very active for olefin epoxidation
ReReRe
Al
Si
O O
C
HHH
O
C
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“Greening” the Ethylene Oxide Process
C2H4 + 1
2O2 C2H4O (+ 2 CO2, 15 %) replace
Ag/a-Al2O3
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“Greening” the Ethylene Oxide Process
C2H4 + 1
2O2 C2H4O (+ 2 CO2, 15 %)
C2H4 + H2O2 C2H4O + H2O → C2H4(OH)2
replace
by
Ag/a-Al2O3
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“Greening” the Ethylene Oxide Process
M. Ghanta et al. Ind. Eng. Chem. Res., 52 (2013)
C2H4 + 1
2O2 C2H4O (+ 2 CO2, 15 %)
C2H4 + H2O2 C2H4O + H2O → C2H4(OH)2
replace
by
Ag/a-Al2O3
CH3ReO3 (MTO)
Homogeneous catalyst
C2H4O selectivity ≈ 100 %No H2O2 decompositionWorks with higher olefins (e.g., C3H6)
CH3ReO3
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J. H. Espenson, Chem. Comm. 479 (1999)W. A. Herrmann, R. W. Fischer, J. D. G. Correia, J. Mol. Catal. 94 (1994)
+
Methyltrioxorhenium activates H2O2
for oxygen atom transfer
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J. H. Espenson, Chem. Comm. 479 (1999)W. A. Herrmann, R. W. Fischer, J. D. G. Correia, J. Mol. Catal. 94 (1994)
+
Methyltrioxorhenium activates H2O2
for oxygen atom transfer
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ⱡ
J. H. Espenson, Chem. Comm. 479 (1999)W. A. Herrmann, R. W. Fischer, J. D. G. Correia, J. Mol. Catal. 94 (1994)
+
Methyltrioxorhenium activates H2O2
for oxygen atom transfer
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ⱡ
J. H. Espenson, Chem. Comm. 479 (1999)W. A. Herrmann, R. W. Fischer, J. D. G. Correia, J. Mol. Catal. 94 (1994)
+
Methyltrioxorhenium activates H2O2
for oxygen atom transfer
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ⱡⱡ
J. H. Espenson, Chem. Comm. 479 (1999)W. A. Herrmann, R. W. Fischer, J. D. G. Correia, J. Mol. Catal. 94 (1994)
+
Methyltrioxorhenium activates H2O2
for oxygen atom transfer
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ⱡⱡ
J. H. Espenson, Chem. Comm. 479 (1999)W. A. Herrmann, R. W. Fischer, J. D. G. Correia, J. Mol. Catal. 94 (1994)
+
Methyltrioxorhenium activates H2O2
for oxygen atom transfer
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The ‘clean’ spectra of MTO makes it amenable to kinetic studies
1H NMR spectra recorded at 3 minute intervals, 23.0 °C
T. Hwang*, B. R. Goldsmith*, B. Peters, S. L. Scott, Inorg. Chem. 52 (2013)
MTO ⇌ A ⇌ B
MTO
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Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
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Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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ThermodynamicsCalculated Experimental
Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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ThermodynamicsCalculated ExperimentalH1 > 0 H1 < 0
Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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ThermodynamicsCalculated ExperimentalH1 > 0 H1 < 0S1 < 0 S1 > 0
Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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ThermodynamicsCalculated ExperimentalH1 > 0 H1 < 0S1 < 0 S1 > 0G1 > 0 G1 < 0
Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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ThermodynamicsCalculated ExperimentalH1 > 0 H1 < 0S1 < 0 S1 > 0G1 > 0 G1 < 0G1 > G2 G1 < G2
Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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ThermodynamicsCalculated ExperimentalH1 > 0 H1 < 0S1 < 0 S1 > 0G1 > 0 G1 < 0G1 > G2 G1 < G2
Many discrepancies remain between experiment and theory
P. Gisdakis et al., Angew. Chem. Int. Ed. 37 (1998)J. M. Gonzales, et al. J. Am. Chem. Soc., 129 (2007)B. R. Goldsmith et al., J. Am. Chem. Soc. 137 (2015)
KineticsCalculated ExperimentalH1
‡ > 100 kJ mol-1 H1‡ = 24.5 kJ mol-1
MTO to A
Reaction parameters: H1, S1, G1
Activation parameters: H1‡,S1
‡, G1‡
A to B
H2, S2, G2
H2‡,S2
‡, G2‡
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+ H2O2 + H2OCH3ReO3
15 °C, CD3CN(aq)
Trace water can be important in many reactions
The water dependence of MTO has not been explained
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+ H2O2 + H2OCH3ReO3
15 °C, CD3CN(aq)
X = 1 – exp(-kobst)
Trace water can be important in many reactions
The water dependence of MTO has not been explained
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Goals1) Fully characterize the thermodynamics and kinetics 2) Elucidate the importance of water
B. R. Goldsmith, T. Hwang, S. Seritan, B. Peters, S. L. Scott, J. Am. Chem. Soc. 137 (2015)
Methods
Density-functional theory
Microkinetic modeling
Experimental kinetic measurements
cyclohexene cyclohexene
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Cyclohexene epoxidation step has only weak water acceleration
0.0
0.2
0.4
0.6
0.8
1.0
0
2
4
6
8
10
0 1 2 3 4 5
10
2 k
3a
pp /
M-1
s-1
10
2 k4
ap
p / M-1 s
-1
[H2O]
0 / M
Cyclohexene epoxidation by B
Cyclohexene epoxidation by A
Experiments
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Cyclohexene epoxidation step has only weak water acceleration
0.0
0.2
0.4
0.6
0.8
1.0
0
2
4
6
8
10
0 1 2 3 4 5
10
2 k
3a
pp /
M-1
s-1
10
2 k4
ap
p / M-1 s
-1
[H2O]
0 / M
Cyclohexene epoxidation by B
Cyclohexene epoxidation by A
Gⱡ = 118 kJ/mol Gⱡ = 115 kJ/mol
Gⱡ = 96 kJ/mol Gⱡ = 94 kJ/mol
epoxidation by A
epoxidation by B
Experiments
Theory
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Water dramatically accelerates the formation of species A and B, not the epoxidation step
At= A
¥+a(1-e
-kfastt) + be
-kslowt
CH3ReO3 + H2O2 + H2OUV-vis, 25.0 °C, CH3CN
fast formationof mostly A
slower formationof mostly B
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Free energy profiles for the formation of A and B
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free
en
ergy
, kJ
mo
l-1
reaction coordinate
-14
-12
CH3ReO3
+ 2 H2O2 A + H2O2
+ H2O
experimental
Free energy profiles for the formation of A and B
B + H2O
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free
en
ergy
, kJ
mo
l-1
reaction coordinate
-14
-12
7582
CH3ReO3
+ 2 H2O2 A + H2O2
+ H2O
experimental
Free energy profiles for the formation of A and B
B + H2O
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free
en
ergy
, kJ
mo
l-1
reaction coordinate
-14
-12
7582
CH3ReO3
+ 2 H2O2 A + H2O2
+ H2O
experimental 110121
reaction coordinate
-9-5CH3ReO3
+ 2 H2O2A + H2O2
+ H2O B + H2O
calculated
0H2O
0H2OFree energy profiles for the formation of A and B
B + H2O
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free
en
ergy
, kJ
mo
l-1
reaction coordinate
-14
-12
7582
CH3ReO3
+ 2 H2O2 A + H2O2
+ H2O
experimental 110121
reaction coordinate
-9-5CH3ReO3
+ 2 H2O2A + H2O2
+ H2O B + H2O
calculated
0H2O
0H2OFree energy profiles for the formation of A and B
B + H2O
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free
en
ergy
, kJ
mo
l-1
reaction coordinate
-14
-12
7582
CH3ReO3
+ 2 H2O2 A + H2O2
+ H2O
experimental 110121
reaction coordinate
-9-5CH3ReO3
+ 2 H2O2A + H2O2
+ H2O B + H2O
calculated
0H2O
0H2O
91
97
1H2O
1H2O
Free energy profiles for the formation of A and B
B + H2O
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free
en
ergy
, kJ
mo
l-1
reaction coordinate
-14
-12
7582
CH3ReO3
+ 2 H2O2 A + H2O2
+ H2O
experimental 110121
reaction coordinate
-9-5CH3ReO3
+ 2 H2O2A + H2O2
+ H2O B + H2O
calculated
0H2O
0H2O
91
97
1H2O
1H2O96
94
2H2O
2H2O
Free energy profiles for the formation of A and B
B + H2O
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The full catalytic cycle and the importance of water
B cycle
A cycle
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The full catalytic cycle and the importance of water
B cycle
A cycle
experimental kinetic profiles
computed kinetic profiles
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Outline. Three projects that help address the below challenges
Challenge 3: Finding reliable descriptors of catalysts (and materials)
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Computationalscreening
First-principles microkinetic modeling
Catalyst designCatalyst characterization
Computationalspectroscopy
First-principlesthermochemistry &kinetic parameters
Reaction mechanism
Mechanistic understanding
Active sites
Can we reliably extract descriptors from such catalysts?
De
scripto
r
Amorphous catalysts Homogeneous catalysts
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Identifying physically meaningful descriptors can help materials discovery
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Identifying physically meaningful descriptors can help materials discovery
Descriptor = function(atomic or material features)
Descriptor → Property
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Identifying physically meaningful descriptors can help materials discovery
Predict new materials
Increase understanding
Descriptor = function(atomic or material features)
Descriptor → Property
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Identifying physically meaningful descriptors can help materials discovery
J. K. Nørskov et al. Nat. Chem. 1, 37 (2009)
CO + 3H2 ⇌ CH4 + H2O
Predict new materials
Increase understanding
Descriptor = function(atomic or material features)
Descriptor → Property
On (111) facets
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Identifying physically meaningful descriptors can help materials discovery
CO + 3H2 ⇌ CH4 + H2O
Predict new materials
Increase understanding
Descriptor = function(atomic or material features)
Descriptor → Property
On (111) facets
The development of data-mining tools can facilitate the discovery of descriptors
J. K. Nørskov et al. Nat. Chem. 1, 37 (2009)
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material property 1 (y1)
mat
eria
l pro
per
ty 2
(y 2
)Subgroup discovery: find meaningful local descriptors of a target property in materials-science data
Review: M. Atzmueller, WIREs Data Min. Knowl. Discov. 5 (2015)
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material property 1 (y1)
mat
eria
l pro
per
ty 2
(y 2
)Subgroup discovery: find meaningful local descriptors of a target property in materials-science data
Review: M. Atzmueller, WIREs Data Min. Knowl. Discov. 5 (2015)
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material property 1 (y1)
mat
eria
l pro
per
ty 2
(y 2
) The periodic table has subgroups
Subgroup discovery: find meaningful local descriptors of a target property in materials-science data
Review: M. Atzmueller, WIREs Data Min. Knowl. Discov. 5 (2015)
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Find descriptors that predict crystal structuresfor the 82 octet AB-type materials
Rocksalt Zincblende
vsTarget property
sign(Erocksalt – Ezincblende)
B. R. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, L. Ghiringhelli, New J. Phys. 19, (2017)
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Find descriptors that predict crystal structuresfor the 82 octet AB-type materials
Rocksalt Zincblende
vsTarget property
sign(Erocksalt – Ezincblende)
B. R. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, L. Ghiringhelli, New J. Phys. 19, (2017)
Input candidate descriptors into subgroup discovery from DFT calculations• Radii of s, p, d orbitals of free atoms • Electron affinity• Ionization potential….and many others
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𝑟pA − 𝑟p
B
𝑟 sA
Subgroup discovery classifies 79 of the 82 compoundsusing a two-dimensional descriptor
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𝒓𝐩𝐀 − 𝒓𝐩
𝐁 ≤ 𝟏. 𝟏𝟔 Å
and 𝒓𝐬𝐀 ≤ 𝟏. 𝟐𝟕 Å
𝑟pA − 𝑟p
B
𝑟 sA
Subgroup discovery classifies 79 of the 82 compoundsusing a two-dimensional descriptor
zincblende subgroup
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𝒓𝐩𝐀 − 𝒓𝐩
𝐁 ≥ 𝟎. 𝟗𝟏 Å
and 𝒓𝐬𝐀 ≥ 𝟏. 𝟐𝟐 Å
𝒓𝐩𝐀 − 𝒓𝐩
𝐁 ≤ 𝟏. 𝟏𝟔 Å
and 𝒓𝐬𝐀 ≤ 𝟏. 𝟐𝟕 Å
𝑟pA − 𝑟p
B
𝑟 sA
Subgroup discovery classifies 79 of the 82 compoundsusing a two-dimensional descriptor
zincblende subgroup
Rocksalt
subgroup
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𝒓𝐩𝐀 − 𝒓𝐩
𝐁 ≥ 𝟎. 𝟗𝟏 Å
and 𝒓𝐬𝐀 ≥ 𝟏. 𝟐𝟐 Å
𝒓𝐩𝐀 − 𝒓𝐩
𝐁 ≤ 𝟏. 𝟏𝟔 Å
and 𝒓𝐬𝐀 ≤ 𝟏. 𝟐𝟕 Å
𝑟pA − 𝑟p
B
𝑟 sA
Subgroup discovery classifies 79 of the 82 compoundsusing a two-dimensional descriptor
zincblende subgroup
Rocksalt
subgroup
MgTe AgI
CuF
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Subgroup discovery applied to gas-phase gold clusters
Examine 24,400 gold cluster configurations of sizes Au5-Au14
Gold clusters have interesting chemical and electronic properties
Snapshots of stable gold cluster configurations
B. R. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, L. M. Ghiringhelli, New J. Phys. 19, (2017)M. Boley, B. R. Goldsmith, L. M. Ghiringhelli, J. Vreeken, arXiv:1701.07696, (2017)
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Rediscover simple insight about HOMO-LUMO gap
HO
MO
-LU
MO
en
ergy
gap
(e
V)
24,400 gold cluster configurations (Au5-Au14) in the gas phase 2,440 configurations per size
(relative) total energy of gold cluster (eV)
Target property HOMO-LUMO energy gap
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Three subgroups regarding HOMO-LUMO gap are found
N = # atoms in gold cluster
(relative) total energy of gold cluster (eV)HO
MO
-LU
MO
en
ergy
gap
(e
V)
𝐞𝐯𝐞𝐧 𝑵 𝐚𝐧𝐝 𝑵 > 𝟕
𝐨𝐝𝐝(𝑵)
𝑵 = 𝟔
Target property HOMO-LUMO energy gap
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Three subgroups regarding HOMO-LUMO gap are found
N = # atoms in gold cluster
(relative) total energy of gold cluster (eV)HO
MO
-LU
MO
en
ergy
gap
(e
V)
𝐞𝐯𝐞𝐧 𝑵 𝐚𝐧𝐝 𝑵 > 𝟕
𝐨𝐝𝐝(𝑵)
𝑵 = 𝟔
Metallic
Semiconducting
Target property HOMO-LUMO energy gap
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Three subgroups regarding HOMO-LUMO gap are found
N = # atoms in gold cluster
(relative) total energy of gold cluster (eV)HO
MO
-LU
MO
en
ergy
gap
(e
V)
𝐞𝐯𝐞𝐧 𝑵 𝐚𝐧𝐝 𝑵 > 𝟕
𝐨𝐝𝐝(𝑵)
𝑵 = 𝟔
Metallic
Semiconducting
Target property HOMO-LUMO energy gap
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Toward accelerating computer-aided catalyst design
Plans to apply machine learning to other systems
Finding descriptors of amorphous catalysts
Perovskite structure prediction and reactivity
Designing templates for organometallic catalysts
[1] C. Büchner et al., J. Non-Cryst. Solids 435 (2016)
[1]
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Acknowledgements
Baron Peters
Susannah L. Scott
Matthias Scheffler
Wei-Xue Li
Theory Department,The Fritz Haber InstituteLuca M. Ghiringhelli
Mario Boley
Taeho Hwang
and many others!
Chemical Engineering Department, UC Santa Barbara
Subgroup discovery tutorials and other analytics tools are online
https://www.nomad-coe.eu/