xpertsystems/mat001-sample

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+)