cornell computational chemistry seminar
DESCRIPTION
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?TRANSCRIPT
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Congratulations, you’re a computational chemist.Now what?Molecular Modeling in the Corporate World
A presentation for Roald Hoffmann’s groupCornell University 8 October 2013
George Fitzgerald, [email protected]
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How Widely Used is Modeling?
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Annual Occurrence of "DFT" in Journals
20% growth in annual citations
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• The chemicals industry is more than chemicals
Introduction
Chemicals
PharmaceuticalOil & Gas
Personal & Home Care
Semiconductor
Automotive &Fuel Cells
Aerospace & Defense
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Chemicals Industry R&D Process
ExplorationExploration Concept Qualification
Concept Qualification
Need Identified
Concept Formulated
Concept Validated
Business Commitment
Commercial Introduction
Resource Requirements ($$)
Technical Risk
DemonstrationPilot Test
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Chemicals Industry R&D Process
ExplorationExploration Concept Qualification
Concept Qualification
Resource Requirements ($$)
Technical Risk
DemonstrationPilot Test
• Fail over here where failure is inexpensive
• Use virtual screening to fail faster and cheaper
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• Quantum mechanics– Solution of the Schrödinger equation– Good results for structural,
electronic, optical properties– Necessary for systems with bond-
breaking, reactions and catalysis
• Molecular mechanics– Approximate atomic forces with ball-
spring model, charges, vdW forces– Good results for structures,
solubility, adhesion, diffusion
• Mesoscale– Groups of atoms represented by
beads– Micelle or vesicle formation– Emulsions, kinetics and properties
What Solutions are Available?
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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
Commercial vs. Software
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• 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
What do modelers do?
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• 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
Examples of Modeling Success
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• 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 =
What does it take to be successful?
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• 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
Example: Li-ion battery electrolytes
Halls & Tasaki, J. Power Sources 195 (2010) 1472
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• 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
Lithium Ion Batteries and SEI Film Formation
1 e- decomposition scheme
2 e- decomposition scheme
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• 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 of a system (η)
• Larger dipole moment leads to stronger dipole-cation interactions (μ)
• Lots of simple calculations instead of 1 monster calculation
Anode SEI Additive Descriptors
ELUMO, EAv
μv
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• 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
Anode SEI Additive Structure Library
R1
O
R2
O
R3
R4
O
X
X
Z
X
X
X
X
X
Z
XXX
XX
Z
X
X
X
Z
XX
X = F or H
X
XZ
XX
X
X z1
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• No one material satisfies all 3 objectives
• Multi-objective solutions represent a trade-off
• Pareto-optimal solutions show the ‘best’ tradeoffs
Anode SEI Additive Library Results
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• 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 seeHalls & Tasaki, “High-throughput quantum chemistry and virtual screening for lithium ion battery electrolyte additives,” J. Power Sources 195 (2010) 1472
Li-ion Battery Summary
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• 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
Length Scales: From Atoms to Airplanes
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Prediction of mechanical response of polymers in aerospace composites by Boeing
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• Used MD + Experiment to develop design rules• >1300 potential amine:epoxy binary
combinations considered• ~300 distinct mixtures simulated• ~85 formulations synthesized• ~12 formulations tested with Carbon fibers
Development Approach at Boeing
Steve Christensen says:“We are materials users rather than producers, Collaboration is key to a successful development”
Steve Christensen says:“We are materials users rather than producers, Collaboration is key to a successful development”
72% savings
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Extension from Molecular to Aircraft Scale
Material Properties from Simulation
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• 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
Mesoscale modeling
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• 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
Why we need mesoscale modeling?
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ATOMISTIC MESOSCALEUnits atoms Beads representing many atoms Dynamics F= ma Diffusion, hydrodynamicsLength nm 100’s of nm (or more)Time ns up to ms
Atomistic and mesoscale representations
~10 nm
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Block copolymer phase diagram
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• 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
Nafion mesostructure
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Nafion mesostructure
mean squared difference of concentration from average concentration, i.e. a measure of phase separation.
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Using Modeling to Optimize Biosensor Design
• 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
MicroneedleArray
MixingChannel
PlasmaFilter
SensorSpot
SqueezeBulb
1 cm
• For use in point-of-care diagnostics & monitoring• Human & animal trials• Clinical applications
• Incorporates a painless microneedle array for blood collection
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SERRS Biomolecular ComponentsAutocalibrated Displacement Assay
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Modeling Dye-Surface Interactions
Silver [111] Surface
Oxide Trilayer
H2O “Bridge”
Bulk Silver
GM19 Dye
Dye-Surface Interaction
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21 C
37 C
21 C Stable Sensor Surface 37 C Unstable Sensor Surface
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STABLE 21 C
37 C
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• 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!
e2v Summary
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• products• million direct employees• million indirect
employees• trillion in revenue
World Wide Chemical Industry Facts
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Annual Occurrence of "DFT" in Journals
Wouldn’t it be great if we couldget all of them to do modeling
• 70,000• 1• 50• $2.2
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modeling
technology
The Road Ahead