L1 ⚛ L1-303 ⊙ Testnet

Eyring Transition State Theory

Computational Chemistry · Reaction rate prediction from QM

⬇ Download bundle 🚀 Submit to mainnet — earn 100 PWM + 5% royalty

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-303.md

# ⚛ L1 Principle — Eyring Transition State Theory **ID:** `L1-303` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Computational Chemistry — *Reaction rate prediction from QM* > **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** TS barrier frequency > **📡 Carrier:** none · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41554114 --- ## 🧠 1. Introduction **Eyring Transition State Theory** is a **nonlinear inverse problem** whose unknown lives in **TS barrier frequency** space, within the **Reaction rate prediction from QM** sub-domain of **Computational Chemistry**. Measurements consist of none via a **rate vs T measurement** sensing mechanism. The forward operator applies, in order: E · quantum chemistry operator; E · TS structure operator; computes eigen-pairs of a linear operator; O · rate k T operator. Observations are corrupted by additive Gaussian noise. Well-posed from QM; inversion for barrier + frequencies requires wide T-range. ## ⚙ 2. Forward Model Physical chain: **x** → E · quantum chemistry → E · TS structure → O · rate k T → **y** (detector). ``` y = `O.rate_k_T` `E.TS_structure` `E.quantum_chemistry` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `E.quantum_chemistry` | E · quantum chemistry operator | | `E.TS_structure` | E · ts structure operator | | `O.rate_k_T` | O · rate k t operator | **🛠 Solver components** _(used inside the solver, not in the forward equation)_: | Primitive | What it does | |---|---| | `E.eigensolve` | Computes eigen-pairs of a linear operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Computational Chemistry | | Sub domain | Reaction rate prediction from QM | | Carrier | none | | Problem class | nonlinear_inverse | | Solution space | TS_barrier_frequency | | Noise model | gaussian | | Integration axis | temperature | | Difficulty delta | 5 | | L dag | 3.4 | ## 📡 4. Measurement Model Well-posed from QM; inversion for barrier + frequencies requires wide T-range. | Metric | Value | |---|---| | Metric | log_k_error | | Secondary | Ea_error_kcal_mol | ## 📏 5. Operating Range (Ω) **Center problem class:** `transition_state_theory` · **Forward operator:** `tst_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | N atoms | — | 10 | | T k range | — | 200 – 500 | | Tunneling | — | SCT | | N temperatures | — | 15 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N atoms | — | 3 – 1000 | | Tunneling | — | none, Wigner, Eckart, SCT | | T k range max | — | 500 – 3000 | | N temperatures | — | 3 – 200 | ## 🎯 6. Tolerance (ε) **Center tolerance:** log10(k) <= 0.1 | Metric | Range | |---|---| | Log k error | 0.05 – 2.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **log_k_error**, with κ = `300` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x6a06873a0fc9356d7b210f03fedf74cb20434eb31c8ecc1f4433622ce797e545` - **Chain tx hash:** `0xafab6944916d097c500bbd23356047a99f3f29db5d380c7ed800103d8cd5169b` - **Chain block:** `41554114` --- ## File Mapping This bundle consists of: `L1-303.md`, `L1-303.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-303.md` | Source of truth — edit this | Human or LLM | | `L1-303.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 `L1-303`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

📝 Polish this artifact in your LLM

Download the files on the right.
Paste the Markdown into ChatGPT / Claude / Gemini and edit freely.
Send your edited Markdown back with the prompt below.
Save the regenerated JSON, then submit via /submit.

Prompt to give your LLM

Read the attached Markdown (L1-303.md).
Regenerate L1-303.json so every field matches the Markdown.
Preserve the schema documented in the File Mapping footer at the bottom of the MD.
Output each file in its own fenced code block tagged with the filename.

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Spec 1 child ▾

L2-303-001 Eyring Transition State Theory Mismatch-Only Spec

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-303",
  "chain_block": 41554114,
  "chain_hash": "0x6a06873a0fc9356d7b210f03fedf74cb20434eb31c8ecc1f4433622ce797e545",
  "chain_tx_hash": "0xafab6944916d097c500bbd23356047a99f3f29db5d380c7ed800103d8cd5169b",
  "domain": "Computational Chemistry",
  "hardness_fn": {
    "delta": 5,
    "kappa": 300,
    "metric": "log_k_error",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "log_k_error",
    "regime": "Well-posed from QM; inversion for barrier + frequencies requires wide T-range.",
    "secondary": "Ea_error_kcal_mol"
  },
  "physics_fingerprint": {
    "L_DAG": 3.4,
    "carrier": "none",
    "difficulty_delta": 5,
    "domain": "Computational Chemistry",
    "integration_axis": "temperature",
    "noise_model": "gaussian",
    "primitives": [
      "E.quantum_chemistry",
      "E.TS_structure",
      "E.eigensolve",
      "O.rate_k_T"
    ],
    "problem_class": "nonlinear_inverse",
    "sensing_mechanism": "rate_vs_T_measurement",
    "solution_space": "TS_barrier_frequency",
    "sub_domain": "Reaction rate prediction from QM",
    "title": "Eyring Transition State Theory"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "tst_forward"
    ],
    "allowed_omega_dimensions": [
      "N_atoms",
      "N_temperatures",
      "T_K_range",
      "tunneling"
    ],
    "allowed_problem_classes": [
      "transition_state_theory"
    ],
    "center_spec": {
      "epsilon_fn_center": "log10(k) \u003c= 0.1",
      "forward_operator": "tst_forward",
      "input_format": "measurement_only",
      "omega": {
        "N_atoms": 10,
        "N_temperatures": 15,
        "T_K_range": [
          200,
          500
        ],
        "tunneling": "SCT"
      },
      "problem_class": "transition_state_theory"
    },
    "epsilon_bounds": {
      "log_k_error": [
        0.05,
        2.0
      ]
    },
    "omega_bounds": {
      "N_atoms": [
        3,
        1000
      ],
      "N_temperatures": [
        3,
        200
      ],
      "T_K_range_max": [
        500,
        3000
      ],
      "tunneling": [
        "none",
        "Wigner",
        "Eckart",
        "SCT"
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Reaction rate prediction from QM",
  "title": "Eyring Transition State Theory"
}

🎁

Earn PWM by promoting this to mainnet

• 100 PWM one-time grant on mainnet promotion
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Submit to mainnet →

How: Download the bundle above → polish the Markdown in ChatGPT / Claude / Gemini → submit your version → founder reviews → mainnet.