This is a little presentation I gave to Roald Hoffmann's group at Cornell. What are the industrial applications of computational chemistry? How to people work differently in academia vs. industry? What are the sorts of things students should think about if they plan to work in the corporate world?

1. Congratulations, you’re a
computational chemist.
Now what?
Molecular Modeling in the Corporate World
A presentation for Roald Hoffmann’s group
Cornell University
8 October 2013
George Fitzgerald, Ph.D.
gfitzgerald@accelrys.com

2. How Widely Used is Modeling?
Annual Occurrence of "DFT" in Journals
16000
14000
12000
20% growth in annual citations
10000
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3. Introduction
• The chemicals industry is more than chemicals
Chemicals
Oil & Gas
Pharmaceutical
Personal & Home Care
Semiconductor
Automotive &
Fuel Cells
Aerospace &
Defense

4. Chemicals Industry R&D Process
Need
Identified
Exploration
Concept
Formulated
Concept
Validated
Concept
Qualification
Business
Commitment
Pilot Test
Commercial
Introduction
Demonstration
Resource Requirements ($$)
Technical Risk

5. Chemicals Industry R&D Process
Exploration
Concept
Qualification
Pilot Test
Demonstration
Resource Requirements ($$)
Technical Risk
• Fail over here where
failure is inexpensive
• Use virtual screening to
fail faster and cheaper

6. What Solutions are Available?
• Quantum mechanics
– Solution of the Schrödinger equation
– Good results for
structural, electronic, optical
properties
– Necessary for systems with bondbreaking, reactions and catalysis
• Molecular mechanics
– Approximate atomic forces with ballspring model, charges, vdW forces
– Good results for
structures, solubility, adhesion, diffusi
on
• Mesoscale
– Groups of atoms represented by
beads
– Micelle or vesicle formation
– Emulsions, kinetics and properties

7. Commercial vs. Software
Corporations (usually) like it
• Their Motivation:
solve problems fast
• Easy learning curve
• Tech support
• Validated results
Universities (usually) don’t
• Their Motivation:
do new science
• Expensive
• No source code
• Not latest & greatest

8. What do modelers do?
• Most corporate modeling groups are service
organizations
• Work with experimental teams
– New product development
– Product improvement
– Trouble shooting
• Timeframe
– Short term: days
– Long term: ~ 6 months

9. Examples of Modeling Success
• P&G likes to be way ahead of its competition. They
designed an “innovative” packaging for the Folgers coffee.
– Problem was there was gas build up inside the plastic can
causing explosion.
• Boeing needs a foolproof way of bonding metal to
composite.
– Problem to solve is to improve joints and eliminate failures.
• PPG is looking for ways to speed up time-to-market next
generation photochromic lens
– They need to do this to grow the mature market while
maintaining competitive lead
• Alcoa needs better and low cost designs of Al
– With stiff and fast growing competition from plastic and
composite makers, Alcoa needs ways to compete effectively
by making new and lower cost aluminum designs, faster
prototype and testing cycles.
• BP needs to accelerate the development of “Designer “
catalysts to reduce cost and increase profitability

10. What does it take to be successful?
• Know how to model
– Translate problem to atomic scale
– Determine what questions modeling can answer
• Know your tools
– HF, DFT, CCSDT, MM2, UFF, DFTB,…
– Which method is right for this problem?
– Which software package, commercial or freeware?
• Know how to talk to experimentalists
– LUMO energies, autocorrelation functions = 
– Phase diagrams, reaction rates, solubility = 

11. Example: Li-ion battery electrolytes
• Electrolyte consists of Li salts in aprotic solvent +
additives
• Additives increase the dielectric strength
• Enhance electrode stability by facilitating the
formation of the solid/electrolyte interface (SEI) layer
Halls & Tasaki, J. Power Sources 195 (2010) 1472

12. Lithium Ion Batteries and SEI Film Formation
1 e- decomposition
scheme
2 e- decomposition
scheme
• Initiation step leading to SEI formation is electron transfer
resulting in decomposition reaction producing the SEI layer at
the graphite-electrolyte interface
• Important requirements for electrolyte additives selected to
facilitate good SEI formation are:
– higher reduction potential than the base solvent
– maximal reactivity for a given chemical design space
– large dipole moment for interaction with Li

13. Anode SEI Additive Descriptors
• Increased reduction potential
correlates with a lower LUMO
energy value or a higher vertical
electron affinity (EAv)
• Measure of stability or reactivity
is the chemical hardness ofv a
system (η)
• Larger dipole moment leads to
stronger dipole-cation
interactions (μ)
• Lots of simple calculations
instead of 1 monster calculation
ELUMO, EAv
μ

14. Anode SEI Additive Structure Library
X
X
Z
X
O
O
X
X
R4
X
Z
O
X
X
X
X
X
X
X
X
X
R3
R2
Z
X
X
X
Z
X
X
Z
R1
X z1
X
X
X = F or H
• Experiment tells us that derivatives of ethylene carbonate
(EC) are good candidates
• Modeling experience tells us that semiempirical (PM3) is
adequate for these properties
• Commercial software makes it easy to create a
combinatorial library of 7381 structures and run the
calculations

15. Anode SEI Additive Library Results
• No one material satisfies
all 3 objectives
• Multi-objective solutions
represent a trade-off
• Pareto-optimal solutions
show the ‘best’ tradeoffs

16. Li-ion Battery Summary
• The generation of virtual structure libraries can be used to
explore materials design space
• Advanced materials modeling workflows can be captured in
pipelined protocols enabling the analysis of virtual materials
libraries
• Combination of molecular modeling and data analysis can
identify leads efficiently
• Acknowledgements
– Ken Tasaki (Mitsubishi Chemicals Inc.)
– Mathew Halls (Accelrys)
– Computational resources: HP
• For details see
Halls & Tasaki, “High-throughput quantum chemistry and
virtual screening for lithium ion battery electrolyte additives,”
J. Power Sources 195 (2010) 1472

17. Length Scales: From Atoms to Airplanes
• Molecular-level approach
– Begin with a system and its properties
– Develop a model based on a few critical parameters
– Extrapolate model to new systems until you find suitable
material
• Build up from molecular level to bulk incrementally

18. Prediction of mechanical response of polymers in aerospace
composites by Boeing

19. Development Approach at Boeing
• Used MD + Experiment to develop design rules
• >1300 potential amine:epoxy binary
combinations considered
• ~300 distinct mixtures simulated
72% savings
• ~85 formulations synthesized
• ~12 formulations tested with Carbon fibers
Steve Christensen says:
“We are materials users rather than producers,
Collaboration is key to a successful development”

20. Extension from Molecular to Aircraft Scale
Material Properties from Simulation

21. Mesoscale modeling
• Mesoscale structures, such as the morphology of a
polymer blend, evolve slowly, and so are
essentially ‘frozen in’ by the materials processing
• Many materials depend for their action on the
precise form of the mesoscale units, e.g. the
micellar structure determines the success of
emulsion polymerization or the effectiveness of a
detergent
• Mesoscale structures play a crucial role in
determining material properties

22. Why we need mesoscale modeling?
• We need “mesoscopic” length and time scales but
– Atomistic simulation is too detailed to be usefully
applied
– Continuum modeling is too coarse
• We use mesoscale modeling, which assumes
– The phenomena at atomistic scale are at equilibrium
– Have a relaxation time much shorter than the time scale
of interest

23. Atomistic and mesoscale representations
~10 nm
Units
Dynamics
Length
Time
ATOMISTIC
atoms
F= ma
nm
ns
MESOSCALE
Beads representing many atoms
Diffusion, hydrodynamics
100’s of nm (or more)
up to ms

24. Block copolymer phase diagram

25. Nafion mesostructure
• Ionomer proton exchange
membranes (PEM) membrane for
fuel cell applications
• Debate over mesoscale structure
and mechanism of ion transport
• Can we
– Identify the transport mechanism
– Predict phase stability as a function of
environment (e.g., H2O, Temp)
– Ultimately create new membranes with
better stability

26. Nafion mesostructure
mean squared difference
of concentration from
average
concentration, i.e. a
measure of phase
separation.

27. Using Modeling to Optimize Biosensor Design
Sensor
Spot
Squeeze
Bulb
Plasma
Filter
1 cm
Microneedle
Array
Mixing
Channel
• For use in point-of-care diagnostics &
monitoring
• Human & animal trials
• Clinical applications
• Incorporates a painless microneedle array
for blood collection
•
Surface Enhanced Resonance Raman
Spectroscopy
– We get a strong signal only from
molecules at the surface
– The laser is tuned to maximise photon
absorption
– The Raman ‘dyes’ are chosen to give
spectral separation

28. SERRS Biomolecular Components
Autocalibrated Displacement Assay

29. Modeling Dye-Surface Interactions
Silver [111]
Surface
GM19 Dye
Oxide Trilayer
H2O “Bridge”
Bulk Silver
Dye-Surface Interaction

30. 21 C Stable Sensor Surface
37 C
21 C
37 C Unstable Sensor Surface

31. 37 C
STABLE
21 C

32. e2v Summary
• e2v substantially improved the performance of the
biosensor by molecular modeling
– Surface binding (Forcite, DMol3, CASTEP): add Cl– Optical properties (DMol3, CASTEP): 458 nm laser
• Multidisciplinary program
– Quantum physics, computational chemistry, physical
chemistry, organic chemistry, electrochemistry, microfluidic
engineering, optical spectroscopy, numerical
modelling, machine learning, statistical
analysis, robotics, electronics and a biologist to actually use
it all.
• Despite this, it actually works!

33. World Wide Chemical Industry Facts
70,000 products
1
million direct employees
50
million indirect employees
$2.2 trillion in revenue
Annual Occurrence of "DFT" in Journals
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Wouldn’t it be great if we could
get all of them to do modeling
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34. The Road Ahead
technology
modeling