docs/battery_methodology.md

Battery and Electrolyte Subset Methodology

This document explains the methodology behind the Battery and Electrolyte subsets. These subsets are chemically filtered from general DFT data — they are NOT purpose-built SSE discovery resources. For a complete assessment of what this dataset can and cannot do for SSE research, see SSE Readiness Assessment.

---

Battery Subset (82,925 entries)

Methodology

The Battery subset includes all entries whose families list contains at least one "battery-relevant" family. Relevance is determined by:

1. Literature prevalence — is this family studied as a battery material?
2. Mobile ion presence — does the composition contain Li, Na, Mg, Ca, Zn, or K?
3. Electrochemical activity — can this material function as an electrode or electrolyte?

Family Relevance

Family	Battery Relevance	Entries	Justification
layered_oxide	High	42,015	LiCoO₂, NMC, NCA — dominant cathode materials
sulfide_sse	Critical	16,359	LGPS, argyrodites — highest-conductivity solid electrolytes
halide_sse	Critical	18,803	Li₃YCl₆, Li₃InCl₆ — emerging high-voltage SSEs
polyanion	High	4,519	LiFePO₄, NASICON-type — commercial cathode materials
nasicon	Critical	560	Na₁₊ₓZr₂SiₓP₃₋ₓO₁₂ — solid electrolyte framework
garnet	Critical	23	Li₇La₃Zr₂O₁₂ (LLZO) — reference solid electrolyte
borohydride	Medium	646	LiBH₄ — lightweight SSE candidates
oxide	Medium	11,928	General oxide cathodes (LiMn₂O₄, etc.)
intermetallic	Low	0	Not included — no mobile ion carrier
unknown	Low	0	Not included — classification unavailable

Battery Subset Composition

layered_oxide: 42,015 (50.7%)
halide_sse:    18,803 (22.7%)
sulfide_sse:   16,359 (19.7%)
oxide:         11,928 (2.3%)
polyanion:      4,519 (5.4%)
borohydride:      646 (0.8%)
nasicon:          560 (0.7%)
garnet:            23 (0.03%)

Limitations

1. No experimental conductivity data — the Battery subset contains only DFT computed properties. Ionic conductivity, migration barriers, and cycling performance are not present. The Battery label denotes relevance, not measured performance.

2. Family imbalance — layered oxides dominate (50.7%). Garnets are severely undercounted (23 entries). Models trained on this subset may be biased toward layered oxide chemistries.

3. Proxy properties only — formation energy and band gap are proxies for battery performance, not direct measurements. A low formation energy does not guarantee good ionic conductivity.

---

Electrolyte Subset (41,665 entries)

Methodology

The Electrolyte subset is a stricter filter applied to the Battery subset:

1. Must be Strict Gold tier (quality ≥ 80, full provenance, no defects)
2. Must NOT be from OQMD (to avoid non-commercial license concerns)
3. Must be classified as one of: sulfide_sse, halide_sse, nasicon, garnet, or borohydride
4. Must have formation_energy_per_atom available

Rationale for Excluding Layered Oxides and General Oxides

Layered oxides and general oxides function primarily as cathode materials, not electrolytes. Solid electrolytes require:

• High ionic conductivity (Li⁺ mobility)
• Wide electrochemical stability window
• Low electronic conductivity

Layered oxides generally fail criterion 3 (they are electronic conductors). Their inclusion would dilute the electrolyte-specific signal.

Electrolyte Subset Composition

halide_sse:    18,797 (45.1%)
sulfide_sse:   15,701 (37.7%)
nasicon:          548 (1.3%)
borohydride:      596 (1.4%)
oxide:          6,023 (14.5%)  — primarily Li-containing oxides with SSE potential

Intended Use Cases

Use Case	Recommended Subset	Rationale
Electrolyte screening	Electrolyte	Strict Gold quality, SSE-focused chemistries
Electrode + electrolyte	Battery	Broader coverage including cathodes
General property prediction	General (full)	Maximum data diversity
Commercial applications	MP-only + JARVIS-only	No OQMD non-commercial restrictions

How the Subsets Were Constructed

import json

# Family → battery relevance mapping
BATTERY_FAMILIES = {
    "layered_oxide", "sulfide_sse", "halide_sse",
    "polyanion", "nasicon", "garnet", "borohydride", "oxide"
}

SSE_FAMILIES = {
    "sulfide_sse", "halide_sse", "nasicon", "garnet", "borohydride"
}

with open("entries_final_v3.json") as f:
    entries = json.load(f)

battery = [e for e in entries
           if any(f in BATTERY_FAMILIES for f in e.get("families", []))]

electrolyte = [e for e in battery
               if e.get("strict_gold", {}).get("is_strict_gold", False)
               and e.get("license") != "OQMD-noncommercial"
               and any(f in SSE_FAMILIES for f in e.get("families", []))]