High-throughput Quantum Chemistry and Virtual Screening

for Lithium Ion Battery Electrolytes

George Fitzgeralda

Mathew D. Hallsa

Ken Tasakib

a Accelrys, Incb Mitsubishi Chemical, Inc

© 2008 Accelrys, Inc. 2

Introduction

• Computational approach to designing new materials is well-established– Polymers– Catalysts– Active Pharmaceutical Ingredients– Semiconductors– Nanotubes

• Computational approaches:– Save time by screening compounds rapidly in silico and forwarding only the best

leads for experimental screening– Deliver information that you can’t get from experiment– Provide fundamental insight at the atomic level

• Automation provides– A means to screen more leads faster– A method to make sense of your results

• This presentation demonstrates this approach for lithium ion battery electrolytes

© 2008 Accelrys, Inc. 3

Acknowledgements

• Support from Accelrys and Mitsubishi Chemical is gratefully acknowledged

• Computational resources were provided by Hewlett-Packard

© 2008 Accelrys, Inc. 4

Lithium Ion Batteries and SEI Film Formation

• The electrolyte typically consists of one or more lithium salts dissolved inan aprotic solvent with at least one additional functional additive

• Additives are included in electrolyte formulations to increase thedielectric strength and enhance electrode stability by facilitating theformation of the solid/electrolyte interface (SEI) layer

© 2008 Accelrys, Inc. 5

Lithium Ion Batteries and SEI Film Formation

• Initiation step leading to anode SEI formation is electron transfer to theSEI forming species resulting in a concerted or multi-step decompositionreaction producing the passivating SEI layer at the graphite-electrolyteinterface

• Important requirements for electrolyte additives selected to facilitategood 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

1 e- decomposition scheme

2 e- decomposition scheme

© 2008 Accelrys, Inc. 6

In Silico Materials Analysis and Optimization

• Requires user intervention at each step– allows for user error setting parameters– time between compute steps for user action

• Manually extract properties from output and compute derived properties• Manually make comparisons with data for other systems

Lead systemProperty prediction

Structural optimization or dynamics simulations

Develop structural model

Select alloy, ceramic, dielectric material, etc

Lead systemProperty prediction

Structural optimization or dynamics simulations

Develop structural model

Select alloy, ceramic, dielectric material, etc

Change constituent atoms, substitute additive, etc

This is a labor-intensive process !

© 2008 Accelrys, Inc. 7

Virtual Screening

• Virtual screening is the cornerstone of in silico drug discovery

• Allows researchers to effectively screen drug design space to identify most promising structures

– reduces the size of a chemical library to be screened experimentally: O(106) to O(10)

• Improves the likelihood of finding interesting structures

– systematic screening– screen possible design space before

synthesized• Saves time and money

– computational evaluation is faster and much less expensive than experimental testing

High-throughput virtual screening will revolutionize the discovery and optimization of materials systems

© 2008 Accelrys, Inc. 8

Chemical Motif

Design

Virtual Library

Enumeration

Automated QC

Calculation

Virtual Materials Library /

Database

Identification of optimum

leads

Experimental screeningAnalysis

Materials Discovery and Optimization using Virtual Screening

© 2008 Accelrys, Inc. 9

Anode SEI Additive Structure Library

• Cyclic carbonates, related to ethylene carbonate (EC), are often used asanode SEI additives for use with graphite anodes

• To explore the effect of alkylation or fluorination on EC-based additiveproperties an R-Group based enumeration scheme was used to generate aEC-based additive structure library (7381 stereochemically uniquestructures)

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

© 2008 Accelrys, Inc. 10

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 of a system (η)

• Larger dipole moment leads to stronger dipole-cation inteactions (μ)

• Work by Chung et al, has shown that the PM3 semiempirical Hamiltonian is effective in computing properties related to electrolyte components performance

ELUMO, EAv

μG.-G. Chung, H.-J. Kim, S.-I. Yu, S.-H. Jan, J.-W. Choi and M.-H. Kim, J. Electrochem. Soc. 147, 4291 (2000).

© 2008 Accelrys, Inc. 11

LUMO & Electron Affinity

• Lower LUMO energy reflects the ease ofelectron transfer at the anode surface whichis the activation step leading to reductivedecomposition

• Relative LUMO energy plots with respect tothe EC LUMO shows that LUMO variabilityacross the library is 3.7 eV, with the lowerlimit in the ca -3.4 eV range

• A better indicator of high reduction potentialis a larger electron affinity. Additives areselected with higher reduction potential thanbase solvent

• Relative EA plots with respect to the EC EAshows that EA variability across the library is4.14 eV, with the upper limit in the ca +4.1 eVrange

LUMO

Electron Affinity

© 2008 Accelrys, Inc. 12

Dipole Moment & Hardness

• Larger dipole moment leads to strongernonbonded interactions with the Li-cation

• Relative dipole moment plots with respect tothe EC dipole shows that dipole variabilityacross the library is 7.5 Debye, with the upperlimit in the ca 2.81 D range

• Lower chemical hardness indicates increasedreactivity and lower stability which isdesirable for SEI film formation

• Relative hardness plots with respect to theEC hardness shows that hardness variabilityacross the library is 1.7 eV, with the lowerlimit in the ca -1.6 eV range

Dipole Moment

Chemical Hardness

© 2008 Accelrys, Inc. 13

Anode SEI Additive Results

• Optimal materials must satisfy a number of objectives• Multi-objective solutions represent a trade-off between objectives• One approach is to adopt the “Pareto-optimal” solution

– Set of solutions such that is not possible to improve one property without making any other property worse

– This case: • Minimize the chemical hardness• Maximize the dipole moment and electron affinity

© 2008 Accelrys, Inc. 14

3D View of Pareto Surface

© 2008 Accelrys, Inc. 15

Anode SEI Additive Pareto Optimal Candidate

• Screening the EC-based additive library gives structure 1573 as a typical Pareto-optimal solution

↑(EA and μ) and ↓η

© 2008 Accelrys, Inc. 16

Method Validation

• Butyl sultone (BS) has been reported to be a highly effective graphite anode SEI forming additive inelectrolyte formulations involving propylene carbonate (PC) as a co-solvent

• The use of BS as an electrolyte additive overcame performance issues seen with the application of PC asa co-solvent, such as the loss of discharge capacity and decrease of cycling stability

• BS is predicted to be more easily reduced than PC, having an electron affinity 1.27 eV larger than thatcomputed for PC. The chemical hardness of BS is 0.60 eV lower than that of PC, suggesting it would bemore reactive, facilitating SEI formation

• Fluoroethylene carbonate (FEC) has also been used experimentally as an SEI additive in electrolytes forlithium ion batteries

• Using an ethylene carbonate (EC) containing solvent, the addition of FEC at 10% and 30% levels, shiftedthe onset of SEI formation to higher potentials by +0.25 eV and +0.38 eV, compared to 0.4 eV for the basesolvent vs. Li/Li+

• FEC is predicted to has a higher electron affinity (+0.42 eV) and a lower chemical hardness (−0.13 eV)than EC, suggesting superior SEI forming behaviour

© 2008 Accelrys, Inc. 17

Summary

• The use of high-throughput quantum chemistry to analyze and screen a materials structure library representing a well defined chemical design space has been applied to fluoro- and alkyl derivatized ethylene carbonate (EC)

• The effect of fluorination leads to a maximum electron affinity across the library of 4.13 eV, compared to alkylation leading to a maximum value of only 0.43 eV, relative to EC

• The results presented here introduce a new and powerful approach for exploring the property limits of structural motifs for lithium battery electrolyte additives. High-throughput computational screening has the potential to dramatically reduce the time and effort for evaluating new synthetic directions for anode SEI formation additives

• This work has appeared in print as Journal of Power Sources 195 (2010) 1472–1478.

© 2008 Accelrys, Inc. 18

Related Work

• A similar approach has been applied to other materials:– OLEDS– Fuel cell catalysts (ECS Transactions, 25 (2009) 1335-1344)– Polyolefin catalysts (poster, 21st North American Catalyst Society meeting)

• Methods are being developed to improve the optimization process for searching these very large libraries

– QSARs– Neural Networks– Evolutionary Algorithms