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mat-reaction-network

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Predict thermodynamically optimal solid-state inorganic synthesis pathways and tabulates basic reactions.

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Material Reaction Network Prediction

Goal

To predict the optimal sequence of thermodynamically favorable chemical reactions (pathways) needed to synthesize a target generic solid-state material from a set of starting precursors. This skill enumerates large, competitive reaction networks and solves for minimum-energy paths using the materialsproject/reaction-network code and Materials Project API thermodynamics data.

Instructions

1. Reaction Enumeration

Explore the landscape of competing reactions within a specific chemical system by explicitly generating balanced equations.

# Env: base-agent
python .agents/skills/mat-reaction-network/scripts/enumerate_reactions.py --chemsys Ba-Ti-O --enumerator-type basic_open --open-phases O2 --temperature 1000 --limit 10
  • --chemsys: The chemical system to restrict search to.
  • --enumerator-type: The algorithm used to propose reactions (basic, basic_open, minimize_gibbs, minimize_grand_potential).
  • --open-phases: (Specific to basic_open) allow materials to be freely consumed or produced from an infinite reservoir (like environmental O2).
  • --temperature: Synthesis temperature (Kelvin), affects Gibbs adjustments.
  • --limit: Maximum number of elementary reactions to print.

2. Pathfinding and Solving Syntheses

To resolve a complete list of step-by-step reactions that convert specific starting precursors into a target compound, use the pathway solver script.

# Env: base-agent
python .agents/skills/mat-reaction-network/scripts/find_pathways.py --target BaTiO3 --precursors BaO TiO2 --temperature 1000 --k-paths 5
  • --target: The desired final functional material.
  • --precursors: One or more starting materials (e.g., oxides or carbonates).
  • --byproducts: Optional allowed volatile byproducts (e.g., CO2, H2O) escaping into the atmosphere.
  • --k-paths: Number of different candidate elementary pathways to yield.

Examples

Finding pathways to synthesize Yttrium Manganite from carbonates and chlorides:

# Env: base-agent
python .agents/skills/mat-reaction-network/scripts/find_pathways.py \
    --target YMnO3 \
    --precursors YCl3 Mn2O3 Li2CO3 \
    --byproducts LiCl CO2 \
    --temperature 923 \
    --k-paths 5

Constraints

  • Environments: The scripts require the base-agent conda environment where reaction-network and mp-api are installed. Each execution MUST specify this environment.
  • Network Extent: Highly constrained chemical systems (e.g., >5 elements) without sensible stability filtering (--stability-tol) can generate massive reaction networks taking >10 minutes and >16GB memory to solve.
  • Open Phases: Synthesis in air or controlled atmospheres must be modeled appropriately by declaring oxygen/nitrogen as open phases.

References

  • McDermott, M. J., et al. "A graph-based approach to predicting solid-state synthesis pathways". Nature Communications (2021). DOI

Author: Bowen Deng Contact: GitHub @learningmatter-mit