---
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+)