High-voltage cathodes like LiNiₓMnᵧO_z (LNMO) offer fast lithium transport, thermal stability, and a cobalt-free path forward but electrolyte instability above 4.3 V limits performance. Traditional additive discovery is slow, resource-intensive, and often trial-and-error.

In our latest white paper, developed with Morrow Batteries, we present a predictive computational–experimental workflow to accelerate electrolyte additive screening.

Predictive Additive Screening

Using redox-potential-guided DFT calculations, we can:

• Rank additive candidates based on oxidation/reduction potentials
  
  

• Predict SEI and CEI formation before costly experiments
  
  

• Reduce the pool from 32 → 6 → 1 promising candidates
  
  

We then applied reaction-pathway analysis to understand bond-breaking, HF scavenging, and polymer formation, providing mechanistic insight into additive behavior.

Figure 1: Reduction and oxidation potentials of the studied additive candidates calculated against the reference Li0/Li+ voltage. The names of additives have been omitted for confidentiality.

Experimental Validation

Selected additives were tested in 1.2 Ah LNMO pouch cells:

• One additive significantly improved high-rate cycling
  
  

• Faster, more targeted development cycles for high-voltage electrolytes

  
  

  
  

A Scalable Framework

This workflow shows that computational screening + targeted experiments can dramatically reduce development cycles while uncovering deeper mechanistic insight. The approach is applicable to LNMO and other demanding high-voltage cathodes.