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, Ph.D.gfitzgerald@accelrys.com

How Widely Used is Modeling?

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Annual Occurrence of "DFT" in Journals

20% growth in annual citations

• The chemicals industry is more than chemicals

Introduction

Chemicals

PharmaceuticalOil & Gas

Personal & Home Care

Semiconductor

Automotive &Fuel Cells

Aerospace & Defense

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

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

• 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?

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

• 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?

• 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

• 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?

• 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

• 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

• 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

• 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

• 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

• 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

• 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

Prediction of mechanical response of polymers in aerospace composites by Boeing

• 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

Extension from Molecular to Aircraft Scale

Material Properties from Simulation

• 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

• 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?

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

Block copolymer phase diagram

• 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

Nafion mesostructure

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

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

SERRS Biomolecular ComponentsAutocalibrated Displacement Assay

Modeling Dye-Surface Interactions

Silver [111] Surface

Oxide Trilayer

H2O “Bridge”

Bulk Silver

GM19 Dye

Dye-Surface Interaction

21 C

37 C

21 C Stable Sensor Surface 37 C Unstable Sensor Surface

STABLE 21 C

37 C

• 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

• products• million direct employees• million indirect

employees• trillion in revenue

World Wide Chemical Industry Facts

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Wouldn’t it be great if we couldget all of them to do modeling

• 70,000• 1• 50• $2.2

modeling

technology

The Road Ahead