Predicting and Understanding Novel Electrode Materials from First Principles - Department of Energy

Predicting and Understanding Novel Electrode
 Materials from First Principles
 Principal Investigator: Kristin A. Persson

 Principal Investigator, Project ID BAT091
 Energy Technologies Area, Lawrence Berkeley National Laboratory
 Department of Materials Science and Engineering, UC Berkeley, Berkeley, California
 94720

 This presentation does not contain any proprietary, confidential, or otherwise restricted information

Overview
 Timeline Barriers
• October 1st 2019 - September 30st 2022. • Development of PHEV and EV batteries that
• Percent complete: 60% meet or exceed the DOE and USABC goals
 – Cost, Performance and Safety

 Budget Partners
• Funding for FY 20: $500K • Bryan McCloskey (LBNL)
• Funding for FY 21: $400K • Robert Kostecki (LBNL)
• Anticipated total funding: $1,620K • Guoying Chen (LBNL)
 • Gerbrand Ceder (LBNL)

 Support for this work from the Office of Vehicle Technologies, DOE-EERE, is gratefully acknowledged – Tien Duong

 1

Relevance
Impact:
Modeling research will aim to understand and
 suggest improvements for
• Li-ion conduction and oxygen transport in
 amorphous oxide coating materials
• SEI formation on Li metal anodes in Gen II
 - based electrolytes
• Li-ion conduction in non-aqueous, super-
 concentrated electrolytes

Objectives:
§ Identify ion conduction mechanisms in amorphous oxide coatings and use obtained design metrics to screen for optimal coating
 materials
§ Identify reaction pathways in SEI formation on Li metal anodes in Gen II liquid electrolytes from high-throughput first-principles
 simulations of the reaction cascade with associated machine-learning approaches.
§ Identify solvation environments, viscocity and conduction mechanisms in super-concentrated nonaqueous electrolytes, and
 propose changes to solvent/salt compositions to improve active ion conductivity

 2

Approaches and Techniques

• Electrochemical and chemical reactions are treated with first-principles, quantum mechanical methods (VASP, Gaussian). Crystal
 orbital Hamiltonian populations (COHP) are used to extract chemical interactions between atoms from band structure calculations.
• Amorphous solids are obtained through ab-initio molecular dynamics and a melt-quench process (see flow diagram: right)
• High-throughput first-principles calculations of molecular reactions, with associated machine-learning to accelerate
 fragmentation/recombination information.

 3

Milestones

Month/Year Milestones Status

December 2020 Preliminary insights into the SEI composition and Completed
 reaction pathways for baseline electrolytes. First
 approximative reaction scheme proposed

March 2020 Develop a valid model for amorphous structure as Completed
 compared to experimental radial distribution function
 data.

September 2021 Quantify the effect of co-solvents to at least one In progress
 superconcentrated electrolyte.

 4

Computational screening for Li-ion coatings with high stability, optimal
 Li conductivity as well as oxygen retention

Technical Accomplishments: Workflows

Amorphous solids are obtained
through ab-initio molecular dynamics
and a melt-quench process

 Python workflow: MPMorph

 ! in amorphous Li" O

 6/34
 Jianli Cheng et al., ACS Appl. Mater. Interfaces, 12, 31, 35748, (2020).

Technical Accomplishments: Li+, O2- Self-Diffusivity at Room Temperature

(a)
 ZnO
 • Li+ and O2- diffuse much faster in ZnO than in Al2O3

 Al2O3 • In general, a higher Li+ concentration trends with increasing Li+
 and O2- diffusivity

 • Li ion conductor, such as LiAlO2 and Li6ZnO4, might be a
 better coating candidate.
(b) ZnO • LiAlO2 and Li6ZnO4 are electrochemically instable

 Al2O3

 7/34
 Jianli Cheng et al., ACS Appl. Mater. Interfaces, 12, 31, 35748, (2020).

Technical Accomplishments: Flowchart of Computational Screening

 Initial screening Chemical stability & Li containing
 ACS Nano 2017, 11, 7019 − 7027
Fluorides Chlorides Oxychlorides Oxyfluorides ∆E123 ≥ −0.1 eV/atom
 ∆E123
 Metal Nonmetal Polyanionic Others LixNiO2
 oxides oxides oxides LixCoO2
 51,537 compounds LixFePO4
 Mole fraction LixMnO4
 Phase stability & band gap
 E/ DOS Solid Liquid
 E" ≤ 0.06 eV/atom E< ≥ 0.5 eV ∆E123 electrolyte electrolyte

 E
 VB CB

 SS
 E HF A % B& C' …
 25,688 compounds Mole fraction SSE
 Electrochemical stability
 913 472
 V −V:2 ≥ 4 V AD@ , neB compounds compounds
 Voltage Li containing
 Discharge
Charge

 compounds Li
 Window Coating
 Cathode Discharge reaction: 216 92
 A! B" C# … + δLi$ + δecompounds
 %
 → aA, bB, cC, … , δLi &'(
 compounds
 V178 ≤ 3 V Li@ , eB Common
 Charge reaction:
 compounds
 2,411 compounds A! B" C# … 72 compounds
 → δA)$ + nδe% + (a − δ)A, bB, cC, … &'(

Technical Accomplishments: Electrochemical Stability Window

• V()* ≤ 3 V & −V+, ≥ 4 V
• Fluorides have the largest electrochemical stability window, e.g., AlF3, LiAlF4.
• Polyanionic oxides have the largest number of candidates, e.g., LiPO3.
• 12 metal oxides, e.g., Al2O3; nonmetal oxides are eliminated, e.g., B2O3;
 9/34

Technical Accomplishments: Computational Screening
 Initial screening Chemical stability & Li containing
Fluorides Chlorides Oxychlorides Oxyfluorides ∆E123 ≥ −0.1 eV/atom
 ∆E123
 Metal Nonmetal Polyanionic Others LixNiO2
 oxides oxides oxides LixCoO2
 51,537 compounds LixFePO4
 Mole fraction LixMnO4
 Phase stability & band gap
 E/ DOS Solid Liquid
 E" ≤ 0.06 eV/atom E< ≥ 0.5 eV ∆E123 electrolyte electrolyte

 E
 VB CB

 SS
 E HF
 25,688 compounds Mole fraction SSE
 Electrochemical stability Li3PS4 LiPF6
 913 472
 V −V:2 ≥ 4 V AD@ , neB compounds compounds
 Voltage Li containing
 Discharge
Charge

 Window Coating compounds
 Cathode 216 92
 compounds compounds
 V178 ≤ 3 V Li@ ,eB Common
 compounds
 2,411 compounds 72 compounds
 10/34

Technical Accomplishments: Chemical Stability with Cathodes

• ∆E(,- ≥ −0.1 eV/atom
• Oxides coatings are less reactive with cathodes than fluorides and chlorides.
• All the electrochemical stable metal oxides have low reactivity with cathodes.
 11/34

Technical Accomplishments: Chemical Stability of Coating w.r.t. Electrolyte

 • Fluorides and chlorides have
 low chemical reactivity with
 LPS and HF.

 • Solid electrolyte: polyanionic
 oxides have the largest number
 of candidates.

 • Liquid electrolyte: most oxides
 compounds are screened out
 due to high reactivity with HF.

 12/34

Technical Accomplishments: Histogram of Number of Compounds

• Common coatings consist of fluorides and chlorides.
• Select representative compounds to evaluate ionic diffusivities.
 13/34

A machine-learning assisted reaction network for discovering SEI
 reactions and modeling SEI evolution

Technical Accomplishments and Progress:
 Producing a world-leading reaction network
 Quantum Mechanical HT
 infrastructure and Supercomputers

 > 20,000+ molecules and fragments
 > 200,000+ elementary reactions

 Transformation of
High-throughout computational infrastructure has resulted in world-leading available conventional reactions into
molecular and reaction network, covering over 200,000 elementary reactions. Graphical graphical representations,
representation is utilized to analyze the reaction pathways. where species and
 combinations of species are
 nodes, and reaction Gibbs
 free energies are transferred
 into cost functions allow us
 to analyze favorable
 thermodynamic pathways.

Technical Accomplishments: BonDNet Graph Neural Network

Even with supercomputers, it is too expensive to explicitly calculate all possible bond breaking and bond formation reactions in
this large network. Hence, we have trained a machine learning model to predict molecular bond formation energies, using local
bond topology and speciation as well as global charge of the molecular fragment. The results are very encouraging with over 90%
recall, precision and f1 scores.

 Previous best
 machine learning
 model: Limited to
 homolysis of
 neutral molecules

Novel reaction difference representation enables AI-
accelerated prediction of bond dissociation energy for
charged molecules BonDNet: Any bond dissociation reaction
 20% better performance
 Wen, M., Blau, S. M., Spotte-Smith, E. W. C., Dwaraknath, S., & Persson, K. A. Chemical Science 2021

Accomplishments: Reaction Network Paths to LEDC
 Automatically identified both
 0
 expected (purple, green) and
 novel (blue, red, gold)
 O
 Li +

 O O

 O
 0 pathways to lithium ethylene

 -1.0
 dicarbonate (LEDC), the
 Li+

 8
 -1.21
 O O

 majority organic component
 +1
 O
 Li+
 0 -0.42 Li+
 O 3

 of the SEI. These new paths
 O O ,
 Li+

 ~6000 species
 -1
 O

 -1.01
 O O Li+ -4.
 85

 -1.00
 show the diversity of
 1&4

 ~4,500,000 reactions
 -4.7
 O O 3
 1
 -1 1

 reactions that can occur,
 O
 Li+

 -4.44
 O O

 -1.72
 leading to stable and

 -1.6
 1
 -1
 Free energy [eV] -
 O

 metastable SEI products
 -1
 Li+
 O
 Li +
 O O- ,
 O O
 0
 O O
 O

 -0.23
 1: Add Li+ 2
 O ,
 O O

 0

 -1
 O

 .8
 9
 2: Add

• First ever electrochemical reaction network O O
 3

 LEDC 0

• 10x larger than any previously reported
 O
 3: Add Li+ Li +
 O O
 O O
 4: Remove Li+ Li +

 network O ,

 Blau, S. M., Patel, H. D., Spotte-Smith, E. W. C., Xie, X., Dwaraknath, S., & Persson, K. A. Chemical Science 2021 NREL | 17

Accomplishments: Reaction Network Paths to LEMC

Automatically identified optimal pathways to lithium ethylene monocarbonate (LEMC), another key organic
component of the SEI, and performed by-hand kinetic refinement

 All kinetically viable paths
 involve water or its fragments,
 indicating that water is
 essential to LEMC formation.

 Xie, X., Spotte-Smith, E. W. C., Patel, H. D., Blau, S. M., & Persson, K. A. In Preparation
 NREL | 18

Responses to Previous Year Reviewers’ Comments

 The program was not reviewed in 2020

Partners and Collaborations

We are excited that some of our coating formulations is considered by our partners at LBNL; Guoying Chen and
 Gerbrand Ceder, for high-voltage cathodes as well as solid state batteries.
The insights from the SEI reaction cascade on the Li metal anode will be compared to model materials and
 spectroscopic results by Robert Kostecki.
Collaborations on electrolyte formulations are ongoing with Bryan McCloskey, LBNL and Brett Lucht, U Rhode
 Island.

 20

Summary and Future Work
Coatings:
Fluorides have the largest electrochemical stability window, and low chemical reactivity with LPS and HF.
Solid electrolyte: polyanionic oxides have the largest number of candidates.
Liquid electrolyte: most oxides compounds are screened out due to high reactivity with HF.
 #$ %
Li+ and O2- diffusions are correlated: higher !" , higher !" . Higher Li content in similar chemical compound
gives both higher Li conductivity as well as O conductivity. Hence, careful tailoring of coating chemistry as
well as thickness, depending on the application, is recommended. Specific recommendations and compounds
are forthcoming.
SEI prediction:
We continue to develop our data-driven, machine-learning assisted infrastructure for SEI prediction. We have
successfully automatically retrieved previously known reaction pathways to LEDC, including a few new variations.
We have applied the same methodology to LEMC formation, and find that it is critically dependent on water.
Hence, we expect that while LEMC is favorable to form, it does not replace LEDC in the early SEI
formation. Future work is concentrating on incorporating the breakdown of the salt and LiF-generating species,
and how those reactions are influenced by the organic breakdown products.