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

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:

# 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)

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.