L1 ⚛ L1-300 ⊙ Testnet

Chemical Kinetics via Stiff ODEs

Computational Chemistry · Reaction network dynamics

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⬇ L1-300.md

# ⚛ L1 Principle — Chemical Kinetics via Stiff ODEs **ID:** `L1-300` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Computational Chemistry — *Reaction network dynamics* > **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** concentration time series > **📡 Carrier:** none · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41554114 --- ## 🧠 1. Introduction **Chemical Kinetics via Stiff ODEs** is a **nonlinear inverse problem** whose unknown lives in **concentration time series** space, within the **Reaction network dynamics** sub-domain of **Computational Chemistry**. Measurements consist of none via a **species concentration** sensing mechanism. The forward operator applies, in order: E · mass action operator; D · time · implicit operator; O · concentration profile operator. Observations are corrupted by additive Gaussian noise. Well-posed; inverse (rate constant fit) requires data across T for identifiability. ## ⚙ 2. Forward Model Physical chain: **x** → E · mass action → D · time · implicit → O · concentration profile → **y** (detector). ``` y = `O.concentration_profile` `D.time.implicit` `E.mass_action` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `E.mass_action` | E · mass action operator | | `D.time.implicit` | D · time · implicit operator | | `O.concentration_profile` | O · concentration profile operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Computational Chemistry | | Sub domain | Reaction network dynamics | | Carrier | none | | Problem class | nonlinear_inverse | | Solution space | concentration_time_series | | Noise model | gaussian | | Integration axis | time | | Difficulty delta | 3 | | L dag | 3 | ## 📡 4. Measurement Model Well-posed; inverse (rate constant fit) requires data across T for identifiability. | Metric | Value | |---|---| | Metric | concentration_L2_error | | Secondary | rate_constant_relative_error | ## 📏 5. Operating Range (Ω) **Center problem class:** `chemical_kinetics_ode` · **Forward operator:** `kinetics_ode_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | T k | — | 298 | | T end s | s | 100 | | N species | — | 10 | | N reactions | — | 20 | | Log k range | — | -4 – 4 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | T k | — | 273 – 2000 | | T end s | s | 0.001 – 1000000.0 | | N species | — | 2 – 1000 | | N reactions | — | 1 – 10000 | ## 🎯 6. Tolerance (ε) **Center tolerance:** c L2 <= 0.01 M | Metric | Range | |---|---| | Concentration l2 error | 0.005 – 0.3 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **concentration_L2_error**, with κ = `150` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x57f9b0b587ffecd8fbc97e817a3f48b9a3740a90cfe224c223eb4518ca7c24c3` - **Chain tx hash:** `0x69b90bf91345d1560aa3bc161fca1f2a2cbc92e1e254767fde84b4d6507e96dd` - **Chain block:** `41554114` --- ## File Mapping This bundle consists of: `L1-300.md`, `L1-300.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-300.md` | Source of truth — edit this | Human or LLM | | `L1-300.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-300`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-300.md).
Regenerate L1-300.json so every field matches the Markdown.
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L2-300-001 Chemical Kinetics via Stiff ODEs Mismatch-Only Spec

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📋 JSON metadata

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  "chain_block": 41554114,
  "chain_hash": "0x57f9b0b587ffecd8fbc97e817a3f48b9a3740a90cfe224c223eb4518ca7c24c3",
  "chain_tx_hash": "0x69b90bf91345d1560aa3bc161fca1f2a2cbc92e1e254767fde84b4d6507e96dd",
  "domain": "Computational Chemistry",
  "hardness_fn": {
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    "metric": "concentration_L2_error",
    "type": "epsilon_fn"
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    "regime": "Well-posed; inverse (rate constant fit) requires data across T for identifiability.",
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  },
  "physics_fingerprint": {
    "L_DAG": 3.0,
    "carrier": "none",
    "difficulty_delta": 3,
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    "integration_axis": "time",
    "noise_model": "gaussian",
    "primitives": [
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      "D.time.implicit",
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    ],
    "problem_class": "nonlinear_inverse",
    "sensing_mechanism": "species_concentration",
    "solution_space": "concentration_time_series",
    "sub_domain": "Reaction network dynamics",
    "title": "Chemical Kinetics via Stiff ODEs"
  },
  "size_tiers": {
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    ],
    "allowed_omega_dimensions": [
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      "log_k_range"
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    "allowed_problem_classes": [
      "chemical_kinetics_ode"
    ],
    "center_spec": {
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      "input_format": "measurement_only",
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        "N_species": 10,
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      "problem_class": "chemical_kinetics_ode"
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    "epsilon_bounds": {
      "concentration_L2_error": [
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        0.3
      ]
    },
    "omega_bounds": {
      "N_reactions": [
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        10000
      ],
      "N_species": [
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      ],
      "T_K": [
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        2000
      ],
      "t_end_s": [
        0.001,
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      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Reaction network dynamics",
  "title": "Chemical Kinetics via Stiff ODEs"
}

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