Stuart E. Murdock1, Thomas F. Hughes2, H. Shaun Kwak3, Alex Goldberg4, David J. Giesen2, Yixiang Cao2, Jeffrey Sanders2, Jacob Gavartin5, G. K. Phani Dathar6 and Mathew D. Halls4

1 Schrödinger Inc., Portland, OR, 97204, United States; 2 Schrödinger Inc., New York, NY 10036, United States; 3 Schrödinger Inc., Cambridge, MA 02142, United States; 4 Schrödinger Inc., San Diego, CA 92121, United States; 5 Schrödinger Inc., Camberley GU16 7ER, United Kingdom; 6 Schrödinger Inc., Bangalore, India 560 086

E-mail: stuart.murdock@schrodinger.com

A02-0167: Discovery of New Anode SEI Forming Additives Using an in silico Evolutionary Approach

 

Abstract To improve the performance of lithium ion batteries, functional additives are included in electrolyte formulations. So as we can reduce damage to the electrolyte when a cell is charged sacrificial anode SEI forming additives are used requiring high reduction potential and high reactivity. Previously, high throughput quantum chemical screening of structure libraries has been demonstrated for electrolyte component discovery but this can be a time consuming and highly curated procedure. An alternative approach involves the automated evolution of a set of input structures toward target property characteristics. This requires less user management and enables knowledge creation rather than knowledge implementation. In this work, a quantum chemistry based genetic optimization framework is applied to battery electrolyte components for the first time, seeking to improve better anode forming SEI characteristics.

Overview and Method The genetic optimization discovery tool in the Materials Science Suite from Schrödinger Inc. (1,2) automatically performs structure mutation, quantum chemical scoring and selection/promotion which evolves the chemical population so that their properties will tend toward the optimization targets. To identify improved anode SEI additives, the goal of this work was to simultaneously minimize the oxidation potential and to maximize the reduction potential, which would correspond to reducing the chemical hardness (η) and maximizing the reduction potential leading to better anode forming SEI characteristics. The oxidation/reduction potentials were estimated from the highest occupied molecular orbitals (HOMO) and lowest unoccupied molecular orbitals (LUMO) from calculations carried out using density functional theory (DFT) with the B3LYP hybrid functional along with the 6-311G** triple-ζ polarized basis set in the Jaguar DFT program (3,4). Separate GA optimizations were carried out on ethylene carbonate (EC), performing only individual isoelectronic mutations, only fragment mutations, and then allowing bond cross-over, isoelectronic and fragment mutations; using 10 individuals per generation which is shown in Tables I, II and III. Also, a GA optimization was carried out in 4 parts using bond cross-over, isoelectronic and fragment mutations on a library of 125 anode SEI formers obtained from the patent and scientific literature shown in table IV. For the EC optimization, the jobs were run over 10 processors which was run for a maximum of 10 generations. 120 processors were used for the library of 125 SEI additive structures which was run for a maximum of 20 generations. A scoring penalty was applied to constrain the number of atoms in the newly evolved structures to between 5 and 40 atoms

REFERENCES

(1) Schrödinger  Materials  Science  Suite  2015-­‐2,  Schrödinger,  LLC,  New  York,  NY,  2015   (2) C.S.  Perone,  ACM  SIGEVOluGon  4(1),  12  (2009) (3) Jaguar,  version  8.8,  Schrödinger,  LLC,  New  York,  NY,  2015  

(4) A.  D.  Bochevarov,  E.  Harder,  T.  F  Hughes,  J.R.  Greenwood,  D.A.  Braden,  D.M.  Philipp,  D.  Rinaldo,  M.D.  Halls,  J.  Zhang  and  R.A.  Friesner,  Int.  J.  Quantum  Chem.  113(18),  2110  (2013)   (5) R.G.  Pearson,  J.  Chem.  Sci.  117(5),  369    (2005)  

Conclusions The results presented here introduce a powerful and efficient approach for using the predictive power of modern quantum chemistry to discover lithium battery electrolyte additives. Compared to exhaustive high-throughput virtual screening, using a GA discovery approach dramatically reduces the number of simulations needed to identify chemical systems having the desired property profile, and samples chemical space not covered by deterministic library generation. An exemplary anode SEI candidate was identified, which represents a promising lead for future investigation. We propose the GA approach as a new and powerful addition to the toolbox for computational discovery and optimization in the development of additives for lithium ion battery electrolytes.

In Table IV, the final minimum oxidation and maximum reduction potentials for each group after 20 generations is presented, with the initial values in parentheses. All SEI additive groups have had the range of the critical properties significantly improved from the initial library values. An exemplary structure with a highest score is shown in Figure 1. The properties of this structure are compared to the properties of the known anode SEI additives, butyl sultone (BS), propylene carbonate (PC) and fluoroethylene carbonate (FEC) calculated at the B3LYP/6-311G** level of theory in Table V. Using an ethylene carbonate containing solvent, the addition of FEC at 10% and 30% levels, shifted the onset of SEI formation to higher potentials by +0.25 eV and +0.38 eV, compared to 0.4 eV for the base solvent vs. Li/Li+. Comparing the B3LYP/6-311G** predicted properties for BS, PC and FEC with those for the structure in Figure 1 (Table V), suggests that the proposed additive would show good performance as an anode SEI additive, promising to be more reactive and more easily reduced than the other additives; with a higher effective (by >2.74 eV) and a much smaller η (by >2.85 eV).

Results and Discussion One general measure of chemical reactivity is the chemical hardness (η), defined as: where IP and EA correspond to the ionization potential and electron affinity, respectively. Molecules with a large IP and low EA are less chemically active, i.e., they are chemically hard; therefore a small η would be a favorable feature of an anode SEI additive candidate. The SEI optimization property targets are met by targeting the GA to structures that simultaneously maximize the reduction potential and minimize the oxidation potential. Ethylene carbonate is a widely used co-solvent in Li-ion battery electrolytes. The genetic optimization approach was used to evolve EC toward higher reduction potential and reactivity. Three different GA optimizations were carried out and then a larger optimization of four groups, for ease of monitoring and performance. In Table IV, the final minimum oxidation and maximum reduction potentials for each group after 20 generations is presented, with the initial values in parentheses. After illustrating the effect of the genetic optimization approach for evolving EC into a solution with properties better suited for application as an anode SEI forming additive, a larger optimization of 125 structures was constructed from the anode SEI additives reported in the literature.

TABLE I. Minimum Oxidation potentials ( ) and maximum reduction potential

( ) for each generation in the GA optimization (↑ & ↓ ) of ethylene carbonate (EC) using fragment mutation

Generation (eV) (eV)

0 1

3.12 2.17

-3.73 -1.70

2 1.73 -1.61 3 4 5 6 7 8 9

1.63 1.29 1.30 1.70 1.51 1.66 1.42

-1.81 -1.63 -1.57 -1.70 -1.76 -1.74 -1.71

TABLE II. Minimum Oxidation potentials ( ) and maximum reduction potential

( ) for each generation in the GA optimization (↑ & ↓ ) of ethylene carbonate (EC) using isoelectronic mutation

Generation (eV) (eV)

0 1

3.12 1.57

-3.73 -2.02

2 1.98 -2.02 3 4 5 6 7 8 9

10

1.49 1.49 1.18 1.30 1.38 1.57 1.51 1.61

-2.02 -2.02 -2.02 -2.69 -2.69 -2.42 -2.44 -2.02

TABLE III. Minimum Oxidation potentials ( ) and maximum reduction potential

( ) for each generation in the GA optimization (↑ & ↓ ) of ethylene carbonate (EC) using fragment, isoelectronic and bond cross-over mutation

Generation (eV) (eV)

0 1

3.12 2.72

-3.73 -1.67

2 2.14 -2.13 3 4 5 6 7 8 9

1.70 1.85 1.73 1.17 1.15 1.29 0.99

-2.79 -2.91 -2.94 -2.63 -3.27 -2.87 -1.82

TABLE IV. Minimum Oxidation potentials ( ) and maximum reduction potential

( ) for each generation in the GA optimization (↑ & ↓ ) of anode SEI former library groups using fragment, isoelectronic and bond cross-over mutation. Initial values are in parentheses.

Group (eV) (eV)

1 2

1.56 (2.08) 1.24 (1.59)

-0.07 (-0.43) 0.11 (-0.25)

3 0.67 (1.85) 0.60 (0.06) 4 1.39 (1.37) 0.11 (-0.84)

TABLE V. B3LYP/6-311G** calculated effective oxidation potentials

( ), effective reduction potential ( ) and chemical hardness (η) for butyl sultone (BS), propylene carbonate (PC),

fluoroethylene carbonate (FEC) and the GA derived SEI former candidate

Generation (eV) (eV)

η

(eV) GA SEI Candidate 1.71 -0.73 1.51 Butyl sultone (BS) 3.31 -3.57 4.48

Propylene carbonate (PC) 3.08 -3.69 4.36 Fluoroethylene carbonate

(FEC) 3.43 -3.47 4.51

Figure 1. Molecular structure for exemplary SEI additive from the GA optimization calculations (highest scores simultaneously ↑ Vred & ↓ Vox).