---
title: SPARK
emoji: ⚡
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.29.0"
app_file: app.py
pinned: false
license: mit
short_description: SPARK — Bayesian inference for CV & TPD analysis
---
> # SPARK has moved
> This Space has moved to **[https://huggingface.co/spaces/bing-yan/spark](https://huggingface.co/spaces/bing-yan/spark)**.
> The model, code, and inference behaviour are identical — only the
> URL and branding (TRACE -> SPARK) have changed.
> This `/trace` URL remains live as a redirect for existing bookmarks.



# SPARK — Simulation-based Posterior Amortization for Reaction Kinetics

Amortized Bayesian inference for electrochemistry and catalysis.
Upload cyclic voltammetry (CV) or temperature-programmed desorption (TPD) data to automatically:

1. **Identify the reaction mechanism** from a candidate list (9 for CV, 11 for TPD)
2. **Infer kinetic parameters** with full Bayesian posterior uncertainty

Inference takes ~50 ms on CPU. Accepts CSV files or plot images.

The deployed checkpoints are the **noise-augmented headline models** (CV: `v14_9mech`,
TPD: `tpd_11mech_v2`) that retain 92.4 % (CV) and 95.6 % (TPD) classification accuracy
on realistic noisy inputs.

## Supported Mechanisms

**Electrochemistry (CV, 9 mechanisms):** Nernst, Butler–Volmer, Marcus–Hush–Chidsey,
Adsorption, EC, Langmuir–Hinshelwood, EE, EC′, CE.

**Catalysis (TPD, 11 mechanisms):** First-order, Second-order, Zeroth-order,
FirstOrderCovDep, DiffLimited, Precursor, Dissociative, ActivatedAdsorption,
LH Surface, Mars–van Krevelen, TwoSite.

## Citation

```
[Citation to be added upon publication]
```