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.
