| --- |
| 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+) |
|
|
