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
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
@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+)