Automatic Reaction Mechanism Generation with Group Additive KineticsRichard H. West

Joshua W. Allen

William H. Green

Massachusetts Institute of Technology

Combustion chemistry is complex

1250 715

0.1

10

Initial temperature (K)

Igni

tion

del

ay (m

s)

900

1

2

Modern kinetic models are large

1 80

1000

C atoms in largest alkane

Sp

ecie

s in

kin

etic

mo

del

3

Detailed kinetic modeling is complex

For each chemical reaction

we need:

•forward rate coefficient

•equilibrium constantk f = A exp

�−EaRT

�

∆G = ∆H − T∆S

k f

kr= Keq = exp

�−∆G

RT

�

A + B � C + D

r = k f [A][B]

4

Detailed kinetic modeling is complex

Estimating all the reactions is tedious and error prone.

What do we do?What do we do?

5

Detailed kinetic modeling is complex

Estimating all the reactions is tedious and error prone.

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SimulationRaghavan

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Recent advances allow complicated chemical reacting systems...

quantum calculations can provide data for unknown species and reactions...

...and software can automatically build kinetic models based on chemistry rules

many important systems have very complicated chemistry

it is common to make simple chemistry models...

and industrial reactors are at such different conditions...

...and fit the parameters to laboratory reactor data

...that the models may be invalid

I work on a modern approach to this problem, that you can use today!

but these models offer little insight into the underlying chemistry

These first-principles models offer more insight than simple linear models...

...to be modelled from first principles!

...and are better for extrapolating to new temperatures and pressures

the methods are now fast and accurate enough to interest the wider chemical engineering community

you can do better than that!

12/03/2009 12:17PM_rp_svg2.html

Page 1 of 1file:///Users/rwest/XCodeProjects/RMG/software/python/_rp_svg2.html

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500 Tp (K)

1!(N/

N 0)

SimulationRaghavan

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Page 1 of 1file:///Users/rwest/XCodeProjects/RMG/software/python/_rp_svg2.html

Scale = 0.1

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Recent advances allow complicated chemical reacting systems...

quantum calculations can provide data for unknown species and reactions...

...and software can automatically build kinetic models based on chemistry rules

many important systems have very complicated chemistry

it is common to make simple chemistry models...

and industrial reactors are at such different conditions...

...and fit the parameters to laboratory reactor data

...that the models may be invalid

I work on a modern approach to this problem, that you can use today!

but these models offer little insight into the underlying chemistry

These first-principles models offer more insight than simple linear models...

...to be modelled from first principles!

...and are better for extrapolating to new temperatures and pressures

the methods are now fast and accurate enough to interest the wider chemical engineering community

you can do better than that!

5

Detailed kinetic modeling is complex

Estimating all the reactions is tedious and error prone.

12/03/2009 12:17PM_rp_svg2.html

Page 1 of 1file:///Users/rwest/XCodeProjects/RMG/software/python/_rp_svg2.html

Scale = 0.1

-

!0.2

0

0.2

0.4

0.6

0.8

1

500 Tp (K)

1!(N/

N 0)

SimulationRaghavan

1000 1500 2000

12/03/2009 12:17PM_rp_svg2.html

Page 1 of 1file:///Users/rwest/XCodeProjects/RMG/software/python/_rp_svg2.html

Scale = 0.1

-

Recent advances allow complicated chemical reacting systems...

quantum calculations can provide data for unknown species and reactions...

...and software can automatically build kinetic models based on chemistry rules

many important systems have very complicated chemistry

it is common to make simple chemistry models...

and industrial reactors are at such different conditions...

...and fit the parameters to laboratory reactor data

...that the models may be invalid

I work on a modern approach to this problem, that you can use today!

but these models offer little insight into the underlying chemistry

These first-principles models offer more insight than simple linear models...

...to be modelled from first principles!

...and are better for extrapolating to new temperatures and pressures

the methods are now fast and accurate enough to interest the wider chemical engineering community

you can do better than that!

12/03/2009 12:17PM_rp_svg2.html

Page 1 of 1file:///Users/rwest/XCodeProjects/RMG/software/python/_rp_svg2.html

Scale = 0.1

-

!0.2

0

0.2

0.4

0.6

0.8

1

500 Tp (K)

1!(N/

N 0)

SimulationRaghavan

1000 1500 2000

12/03/2009 12:17PM_rp_svg2.html

Page 1 of 1file:///Users/rwest/XCodeProjects/RMG/software/python/_rp_svg2.html

Scale = 0.1

-

Recent advances allow complicated chemical reacting systems...

quantum calculations can provide data for unknown species and reactions...

...and software can automatically build kinetic models based on chemistry rules

many important systems have very complicated chemistry

it is common to make simple chemistry models...

and industrial reactors are at such different conditions...

...and fit the parameters to laboratory reactor data

...that the models may be invalid

I work on a modern approach to this problem, that you can use today!

but these models offer little insight into the underlying chemistry

These first-principles models offer more insight than simple linear models...

...to be modelled from first principles!

...and are better for extrapolating to new temperatures and pressures

the methods are now fast and accurate enough to interest the wider chemical engineering community

you can do better than that!

5

Detailed kinetic modeling is complex

Estimating all the reactions is tedious and error prone.

Teach the chemistry to a computer!

⇌facebook.com/rmg.mitrmg . sou rce f o rge . ne t

Reaction Mechanism Generator

•free and open source software

Teach the chemistry to a computer!

⇌facebook.com/rmg.mitrmg . sou rce f o rge . ne t

Reaction Mechanism Generator

•free and open source software

5

Automatic reaction mechanism generationneeds methods to:

1. Represent molecules (and identify duplicates)

2. Create reactions (and then new species)

3. Estimate thermo and kinetic parameters (quickly!)

CH3 +

6

Molecules are represented as graphs

CH3CH2. C C*

H

H

H H

H

=

7

Thermochemistry is estimated by Benson group contributions

C-(C)(H)3

C-(C)2(H)2

Cb-(H)

C-(C)(Cb)(O)(H)

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Reaction families propose all possible reactions with given chemical species

•Template for recognizing reactive sites

•Recipe for changing the bonding at the site

•Rules for estimating the rate, based on local chemical structure

bond breaking and hydrogen abstraction

intramolecularH-abstraction

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Reaction families propose all possible reactions with given chemical species

•Template for recognizing reactive sites

•Recipe for changing the bonding at the site

•Rules for estimating the rate, based on local chemical structure

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Octane autoxidation has many pathways

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•Some pathways go further than others.

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Need reasonable rate estimates,even of unlikely reactions

•Faster pathways are explored

•Slower pathways are ignored

•Exploration continues until tolerance satisfied.

AB

CD

E

FG

H

AB

CD

E

F

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Rate estimates are based on the local structure of the reacting sites.

•Hydrogen abstraction: XH + Y. → X. + YH

•Rate depends on X and Y.

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OH

O

Rate estimation rules are organized in a tree

•Most general structure at top

•More specific structures are children

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Part of the tree for X

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Part of the tree for Y

Ideal tree: lots of data

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Typical tree: sparse data

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RMG averages obscure the source of data

•The pair (O_pri, Ct_rad) is not in the database.

•It is estimated by averaging pairs that are:• H_Abstraction estimate: (Average of: (Average of: (Average of: (O_pri O2b) && Average of: (O/H/NonDeC O2b) && O_pri

H_rad && Average of: (O/H/NonDeC H_rad && O/H/OneDe H_rad) && Average of: (O_pri C_methyl && Average of: (O_pri C_rad/H2/Cs)) && Average of: (O/H/NonDeC C_methyl && Average of: (O/H/NonDeC C_rad/H2/Cs) && Average of: (O/H/NonDeC C_rad/H/NonDeC) && Average of: (Average of: (O/H/NonDeC C_rad/Cs3)) && Average of: (Average of: (H2O2 C4H9O/c12345 && H2O2 C4H9O/c134(2)5 && H2O2 C4H9O/c134(2)5 && H2O2 C4H9O/c14(2,3)5) && Average of: (H2O2 C3H5/c132)) && Average of: (Average of: (H2O2 C4H9O/c12345 && H2O2 C4H9O/c12345 && H2O2 C4H9O/c134(2)5) && Average of: (Average of: (H2O2 C4H9O/c12345))) && Average of: (Average of: (Average of: (H2O2 C4H9O/c134(2)5))) && O/H/OneDe C_methyl) && Average of: (O_pri Cd_pri_rad) && Average of: (O/H/NonDeC Cd_pri_rad && Average of: (H2O2 C4H7/c1342) && Average of: (H2O2 Cd_rad/NonDeC)) && Average of: (O/H/NonDeC Ct_rad) && Average of: (O_pri CO_pri_rad) && Average of: (O_pri O_pri_rad && Average of: (O_pri O_rad/NonDeC)) && Average of: (O/H/NonDeC O_pri_rad && Average of: (H2O2 O_rad/NonDeO && H2O2 O_rad/OneDe)))))

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O_priCt_rad

New approach: group additive log(k)

•O_pri is in the database and contributes -2.35 to log(k@1000K)

•Ct_rad is in the databaseand contributes +2.53 to log(k@1000K)

•Add these to a base rate, to get rate estimate.

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O_priCt_rad

Group Additive Kinetics through the years

•Reference reaction + thermodynamic corrections

•Willems and Froment (1988)

•Reference reaction + generalized corrective factors

•Truong (2000)

•Estimate thermodynamics of transition state

•Sumathi et al. (2001)

•Direct estimation of Arrhenius parameters

•Saeys et al (2004-)22

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How to generate kinetics group additivity values

Hierarchy  offunc.onal  groups

Database  of  reac.ons

Assign  groups  for  each  reac.on

Solve  op.miza.onproblem

Validate  withtest  set Group  addi.vity

values

Check  tree  forwell-­‐formedness

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The ideal training set…

...would use real reactant and product species

...would only have one k(T) for each reaction

...would only have well-known k(T) values

...would be large

The ideal training set does not exist.

•PrIMe (primekinetics.org)•Transcription errors•No temperature ranges

•NIST (kinetics.nist.gov)•duplicates•estimates•no API

•Current RMG rules (rmg.mit.edu)•functional groups not molecules•current choice

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⇌

Group values trained using old RMG rules,then tested against PrIMe database.

•Take PrIMe Database

•Filter only Hydrogen Abstraction reactions

•Correct obvious errors (eg. Avogadro number)

•Try to predict with RMG

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warehouse.primekine.cs.org

3118  C/H/O  reac.ons

348  C/H/O  hydrogen  abstrac.on  reac.ons

13654  reac.ons

1075  C/H/O  template  reac.ons

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Good agreement when we havean exact match of a rate rule

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Good agreement when we havean exact match of a rate rule

PrIMe  Ea  off  by9.6  kcal/mol

PrIMe  Ea  off  by6.1  kcal/mol

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Much larger uncertainty when we useaveraged rate rules

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Much larger uncertainty when we useaveraged rate rules

Complex  “average-­‐of”  es.mate

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Slightly smaller uncertainty when we usekinetics group additivity

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Slightly smaller uncertainty when we usekinetics group additivity

Trained  on1  rule

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We can use the group valuesto design a better tree

±0.0  (233)log  kXH(1000  K)  [cm3/mol*s] Number  of  entries  trained  against

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We can use the group valuesto design a better tree

+0.18  (120) -­‐0.55  (25) -­‐2.31  (5) +0.79  (22) -­‐0.05  (34)-­‐0.59  (19)

+0.05  (23) +1.83  (2) +2.09  (1) +1.42  (3)-­‐0.12  (17)

+0.16  (47) +0.18  (28) +0.56  (29)-­‐0.45  (16)

±0.0  (233)log  kXH(1000  K)  [cm3/mol*s] Number  of  entries  trained  against

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We can use the group valuesto design a better tree

±0.0  (233)log  kY.(1000  K)  [cm3/mol*s] Number  of  entries  trained  against

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We can use the group valuesto design a better tree

±0.0  (233)

-­‐0.09  (218) +1.68  (13)

+0.54  (23) -­‐0.69  (34) -­‐0.96  (23) -­‐1.11  (17)-­‐7.82  (12) +3.52  (1)

-­‐0.56  (97) +1.02  (26) +2.62  (7)+2.21  (23)-­‐7.01  (13) -­‐1.13  (12) +0.77  (37)

log  kY.(1000  K)  [cm3/mol*s] Number  of  entries  trained  against

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Benefits of group additive approach

•Easier to explain and justify than averaging method

•Possible to include uncertainty estimates

•Trained against real reactions

•Easy to modify trees and update rules

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Next steps

•Collect reliable, clean, database of reaction rates.

•Formalize the estimation of uncertainties

•Extend to other reaction families

•cyclic transition states?

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Acknowledgements

Prof William H. Green

Joshua W. Allen

Connie Gao

Dr. Michael Harper

Amrit Jalan

Gregory Magoon

Shamel Merchant

⇌rmg.mi t .edurmg.s f .ne t

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Contributions

Developed framework for fitting kinetics group additivity parameters

Group additivity kinetics estimation shows promise for hydrogen abstraction reactions

Key challenge: Getting lots of data