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license: cc-by-nc-4.0
task_categories:
- tabular-regression
- tabular-classification
- time-series-forecasting
tags:
- materials-science
- battery
- lithium-ion
- energy-storage
- electrochemistry
- synthetic-data
- degradation-modeling
- cycle-life
- solid-state-battery
- manufacturing
pretty_name: MAT-001 — Synthetic Battery Materials Dataset (Sample)
size_categories:
- 10K<n<100K
---
# MAT-001 — Synthetic Battery Materials Dataset (Sample)
**XpertSystems.ai Synthetic Scientific Discovery Platform · SKU: MAT001-SAMPLE · Version 1.0.0**
This is a **free preview** of the full **MAT-001 — Synthetic Battery
Materials Dataset** product. It contains roughly **~1% of the full dataset**
at identical schema, chemistry family distribution, and physics-aware
calibration, so you can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|-------------------------------|---------------|---------------|----------------------------------------------|
| `battery_materials_master.csv`| ~2,500 | ~250,000 | Cathode/anode/electrolyte compositions |
| `charge_discharge_cycles.csv` | ~60,000 | ~25,000,000 | Per-cycle telemetry (capacity, voltage, IR) |
| `degradation_profiles.csv` | ~20,000 | ~3,750,000 | Capacity-fade trajectories |
| `thermal_stability_events.csv`| ~500 | ~50,000 | Thermal runaway / failure events |
| `manufacturing_variations.csv`| ~5,000 | ~250,000 | Production lot variations |
| `battery_pack_summary.csv` | ~500 | ~25,000 | Per-pack aggregate KPIs |
## Dataset Summary
MAT-001 simulates the full battery materials discovery lifecycle with
physics-aware synthetic generation across **10 commercial and emerging
battery chemistries**, with:
- **10 battery family archetypes**: nmc811, nmc622, nca, lfp, solid_state,
sodium_ion, lithium_sulfur, silicon_anode, graphene_enhanced,
defense_high_temp — each with empirically-anchored cathode/anode/
electrolyte/separator/crystal structure profiles
- **Arrhenius degradation modeling** with activation energy 0.42 eV and
reference temperature 25 °C
- **Per-chemistry capacity fade multipliers**: solid_state 0.72×,
silicon_anode 1.35×, high-temperature 1.55×, fast-charge 1.28×
- **Dendrite formation modeling** with chemistry-dependent base rates
- **12 dopant types** (Al, Mg, Zr, Ti, W, Mo, Nb, B, F, dual-dopants)
- **6 binder systems**: PVDF, CMC-SBR, PAA, alginate, PTFE, high-temp polyimide
- **Manufacturing yield variability** with calibrated 93% baseline
- **Thermal runaway events** with chemistry-specific onset temperatures
(LFP 265 °C → defense_high_temp 310 °C → lithium_sulfur 175 °C)
- **Anomaly injection** at 1.5% rate for outlier modeling
## Calibrated Benchmark Targets
The full product is calibrated to **10 benchmark validation tests** drawn
from authoritative materials science and battery research sources (DOE
Battery Targets, NMC/LFP production benchmarks, solid-state battery
roadmap literature, EV cell cycle-life literature, UL9540A thermal safety
literature, electrochemical cycling literature, battery degradation
studies, fast-charge degradation literature, manufacturing quality
benchmarks).
Sample benchmark results:
| Test | Target | Observed | Source | Verdict |
|------|--------|----------|--------|---------|
| energy_density_nmc811_wh_kg | 275.0 | 277.7 | DOE battery targets; high-nickel NMC | ✓ PASS |
| energy_density_lfp_wh_kg | 165.0 | 167.1 | DOE battery targets; LFP production | ✓ PASS |
| energy_density_solid_state_wh_kg | 365.0 | 370.9 | solid-state battery roadmap literatu | ✓ PASS |
| cycle_life_80pct_retention | 1000.0 | 1555.6 | EV cell cycle-life literature | ✓ PASS |
| coulombic_efficiency | 0.9950 | 0.9955 | electrochemical cycling literature | ✓ PASS |
| impedance_growth_1000_cycles | 0.2800 | 0.2733 | battery degradation studies | ✓ PASS |
| fast_charge_capacity_loss_500_cycles | 0.1200 | 0.1071 | fast-charge degradation literature | ✓ PASS |
| thermal_runaway_temperature_c | 185.0 | 213.3 | UL9540A and thermal safety literatur | ✓ PASS |
| manufacturing_yield_rate | 0.9300 | 0.8994 | battery manufacturing quality benchm | ✓ PASS |
| diffusion_coefficient_log10 | -11.0000 | -10.9654 | materials diffusion coefficient lite | ✓ PASS |
*Every benchmark in the sample lands within the same calibrated tolerance
as the full product. Physics-aware generation means observed values are
deterministic functions of the calibrated parameters, not stochastic
artifacts that require large samples to converge.*
## Schema Highlights
### `battery_materials_master.csv` (primary file)
| Column | Type | Description |
|---------------------------------|---------|----------------------------------------------|
| material_id | string | Unique material identifier |
| chemistry_family | string | 1 of 10 family archetypes |
| cathode_composition | string | Cathode chemistry (e.g. LiNi0.8Mn0.1Co0.1O2) |
| anode_material | string | Anode (graphite, silicon_graphite, lithium_metal, etc.) |
| electrolyte | string | Electrolyte system |
| separator | string | Separator material |
| crystal_structure | string | Crystal structure family |
| dopant | string | Dopant species (or 'none') |
| binder | string | Binder system |
| nominal_voltage_v | float | Nominal cell voltage |
| energy_density_wh_kg | float | Gravimetric energy density |
| power_density_w_kg | float | Power density |
| thermal_runaway_temp_c | float | Thermal runaway onset temperature |
| expected_cycle_life | float | Expected cycles to 80% capacity |
| diff_log10 | float | Log10 lithium diffusion coefficient |
| conductivity | float | Ionic conductivity (S/cm) |
| manufacturability_score | float | Manufacturability index (0–1) |
### `charge_discharge_cycles.csv` (per-cycle telemetry)
| Column | Type | Description |
|---------------------------------|---------|----------------------------------------------|
| material_id | string | FK to `battery_materials_master.csv` |
| cycle_number | int | Cycle index (1-N) |
| capacity_retention | float | Capacity as fraction of initial |
| coulombic_efficiency | float | CE for this cycle |
| voltage_hysteresis_mv | float | Charge/discharge voltage gap |
| internal_resistance_mohm | float | Cell internal resistance |
| temperature_c | float | Operating temperature |
| c_rate | float | Charge/discharge rate |
### `degradation_profiles.csv` (capacity fade trajectories)
| Column | Type | Description |
|---------------------------------|---------|----------------------------------------------|
| material_id | string | FK |
| timestep | int | Degradation timepoint |
| capacity_fade_pct | float | Cumulative capacity loss |
| impedance_growth_pct | float | Impedance growth |
| dendrite_formation_score | float | Dendrite formation risk (0–1) |
| sei_layer_growth_nm | float | SEI layer thickness growth |
### `thermal_stability_events.csv` (runaway events)
| Column | Type | Description |
|---------------------------------|---------|----------------------------------------------|
| material_id | string | FK |
| onset_temp_c | float | Runaway onset temperature |
| peak_temp_c | float | Peak temperature during event |
| heat_release_rate_w | float | Heat release rate |
| failure_mode | string | Failure mode classification |
See `manufacturing_variations.csv` and `battery_pack_summary.csv` for the
production-lot variation and per-pack aggregate schemas respectively.
## Suggested Use Cases
- Training **battery chemistry classifiers** — 10-class family identification
- **Cycle life prediction** — regress to 80% retention from early-cycle data
- **Energy density forecasting** from composition features
- **Thermal runaway detection** — predict onset from operating signals
- **Dopant effect modeling** — quantify impact of doping on cycle life
- **Solid-state battery design optimization** — explore composition space
- **Manufacturing yield prediction** from process variation features
- **Capacity fade trajectory modeling** — sequence prediction tasks
- **Battery management system (BMS) algorithm training**
- **Materials informatics / inverse design** — find compositions hitting
target energy density + cycle life
- **Battery digital twin training data**
- **EV pack-level state-of-health (SoH) estimation**
## Loading the Data
```python
import pandas as pd
materials = pd.read_csv("battery_materials_master.csv")
cycles = pd.read_csv("charge_discharge_cycles.csv")
degrad = pd.read_csv("degradation_profiles.csv")
thermal = pd.read_csv("thermal_stability_events.csv")
mfg = pd.read_csv("manufacturing_variations.csv")
packs = pd.read_csv("battery_pack_summary.csv")
# Join cycle telemetry with material chemistry
enriched = cycles.merge(materials, on="material_id", how="left")
# Regression target: energy density from composition features
y_energy = materials["energy_density_wh_kg"]
# Classification target: chemistry family from observable telemetry
y_family = materials["chemistry_family"]
# Sequence prediction target: capacity retention curve per material
seq_data = cycles.groupby("material_id")["capacity_retention"].apply(list)
```
## License
This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial
research and evaluation). The **full production dataset** is licensed
commercially — contact XpertSystems.ai for licensing terms.
## Full Product
The full MAT-001 dataset includes **~29 million rows** across all six files,
with calibrated benchmark validation against 10 metrics drawn from
authoritative materials science and battery research sources.
📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**
## Citation
```bibtex
@dataset{xpertsystems_mat001_sample_2026,
title = {MAT-001: Synthetic Battery Materials Dataset (Sample)},
author = {XpertSystems.ai},
year = {2026},
url = {https://huggingface.co/datasets/xpertsystems/mat001-sample}
}
```
## Generation Details
- Generator version : 1.0.0
- Random seed : 42
- Generated : 2026-05-16 15:47:32 UTC
- Physics model : Arrhenius degradation, 0.42 eV activation energy
- Overall benchmark : 100.0 / 100 (grade A+)
|