turing-chemputer

Turing Chemputer Skill: Programmable Chemical Synthesis

Status: ✅ Production Ready Trit: 0 (ERGODIC - coordinator) Color: #26D826 (Green) Principle: Chemistry as computation Frame: XDL programs executed on modular hardware

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Overview

Turing Chemputer coordinates chemical synthesis as program execution. Using XDL (Chemical Description Language), any synthesis protocol becomes an executable program on modular robotic hardware.

1. XDL: XML-based chemical programming language
2. Chempiler: Compile XDL to hardware instructions
3. Modular hardware: Reactors, filters, separators as primitives
4. Turing completeness: Loops, conditionals, recursion

Core Framework

<!-- XDL: Chemical Description Language -->
<Synthesis>
  <Hardware>
    <Reactor id="reactor1" volume="100 mL"/>
    <Filter id="filter1"/>
    <Separator id="sep1"/>
  </Hardware>
  
  <Procedure>
    <Add reagent="A" vessel="reactor1" amount="10 mmol"/>
    <Add reagent="B" vessel="reactor1" amount="12 mmol"/>
    <HeatChill vessel="reactor1" temp="80 °C" time="2 h"/>
    <Filter from="reactor1" to="filter1"/>
  </Procedure>
</Synthesis>

def compile_xdl(xdl: str) -> HardwareInstructions:
    """Chempiler: XDL → executable hardware program."""
    tree = parse_xdl(xdl)
    graph = build_synthesis_graph(tree)
    return optimize_and_schedule(graph)

Key Concepts

1. XDL Programming

class XDLProgram:
    def __init__(self):
        self.steps = []
    
    def add(self, reagent: str, vessel: str, amount: str):
        self.steps.append(Add(reagent, vessel, amount))
    
    def heat(self, vessel: str, temp: str, time: str):
        self.steps.append(HeatChill(vessel, temp, time))
    
    def filter(self, from_vessel: str, to_vessel: str):
        self.steps.append(Filter(from_vessel, to_vessel))
    
    def loop(self, times: int, body: list):
        """Turing-complete: iteration."""
        self.steps.append(Loop(times, body))
    
    def conditional(self, sensor: str, threshold: float, then: list, else_: list):
        """Turing-complete: branching."""
        self.steps.append(Conditional(sensor, threshold, then, else_))

2. Hardware Abstraction

class Chemputer:
    def __init__(self, hardware_graph: nx.DiGraph):
        self.graph = hardware_graph
        self.state = ChemicalState()
    
    def execute(self, program: XDLProgram):
        """Execute XDL on hardware."""
        for step in program.steps:
            self.validate_hardware(step)
            self.execute_step(step)
            self.update_state(step)
    
    def validate_hardware(self, step):
        """Check hardware connectivity and capacity."""
        if not self.graph.has_path(step.source, step.target):
            raise HardwareError("No fluidic path")

3. Synthesis Graph Optimization

def optimize_synthesis(xdl: XDLProgram) -> XDLProgram:
    """Optimize for time, yield, and hardware utilization."""
    graph = to_dag(xdl)
    
    # Parallelize independent operations
    parallel = find_parallel_steps(graph)
    
    # Minimize transfers
    optimized = minimize_transfers(graph)
    
    # Schedule for hardware
    return schedule(optimized, hardware_constraints)

Commands

# Compile XDL to hardware
just chemputer-compile synthesis.xdl

# Validate hardware graph
just chemputer-validate hardware.json

# Simulate synthesis
just chemputer-simulate synthesis.xdl --dry-run

# Execute on hardware
just chemputer-execute synthesis.xdl --hardware lab1

Integration with GF(3) Triads

assembly-index (-1) ⊗ turing-chemputer (0) ⊗ crn-topology (+1) = 0 ✓  [Molecular Complexity]
kolmogorov-compression (-1) ⊗ turing-chemputer (0) ⊗ dna-origami (+1) = 0 ✓  [Self-Assembly]
persistent-homology (-1) ⊗ turing-chemputer (0) ⊗ crn-topology (+1) = 0 ✓  [Topological CRN]

Julia Scientific Package Integration

From julia-scientific skill - molecular representation evolution connects chemputer synthesis to learnable chemistry:

Gen	Representation	Julia Package	Chemputer Role
1	SMILES	MolecularGraph.jl	Target specification
5	GNN (MPNN/GAT)	GraphNeuralNetworks.jl	Retrosynthesis prediction
6	3D coordinates	Chemfiles.jl	Hardware geometry

# Retrosynthesis via GNN → XDL generation
using MolecularGraph, GraphNeuralNetworks

function retro_to_xdl(target_smiles::String, model::GNNModel)
    mol = smilestomol(target_smiles)
    fg = featurize(mol)

    # GNN predicts synthetic route
    precursors = model.predict_precursors(fg)
    reactions = model.predict_reactions(precursors, mol)

    # Generate XDL from predicted route
    generate_xdl(reactions)
end

Related Skills

• assembly-index (-1): Validate molecular complexity
• crn-topology (+1): Generate reaction networks
• acsets (0): Algebraic hardware graph representation
• julia-scientific (0): Full Julia package mapping (137 skills)

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Skill Name: turing-chemputer Type: Chemical Synthesis Coordinator Trit: 0 (ERGODIC) Color: #26D826 (Green)

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Cheminformatics

• rdkit [○] via bicomodule
  • Chemical computation

Bibliography References

• general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.