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Community recipe · Coding orchestration

Estimate ionic diffusivity and activation energy from material simulations

Source-backed example. Practical decision guidance is proposed and untested; this is not a full implementation guide.

The problem

Materials researchers need comparable temperature-dependent transport estimates without losing sight of simulation convergence and uncertainty.

Agent job

Coordinate multi-temperature molecular dynamics, assess convergence and fit an uncertainty-aware Arrhenius relationship.

Expected output

Diffusivity and activation-energy estimates with convergence checks and uncertainty information.

Where this framing comes from
  • Core problem: Editorial purpose framing
  • Agent job: Editorial purpose framing
  • Expected output: Editorial purpose framing
  • Relevant job roles: Editorial purpose framing
  • Benefit type: Editorial purpose framing
Editorial framing and outcome limits

Describes the task’s purpose and intended output; measured benefit, safe unsupervised operation and successful delivery are not established by this framing.

Editorial synthesis based on the linked source. This framing does not add evidence of successful delivery or results.

Case-specific decision guidance · untested

Case-specific decision guidance and first checks are original proposals. The linked source supports the attributed task; this added guidance has not been executed.

Read the attributed source evidence and original caveats below ↓

Why use this

  1. A materials researcher studying ion transport may need to compare trajectories from different temperatures while deciding whether each supports the intended estimate. A fitted relationship can look convincing even when convergence or unit handling remains unresolved.

  2. This job could organize the simulation and analysis plan around explicit adequacy questions and uncertainty reporting. Keeping incomplete trajectories and modelling assumptions visible helps an expert decide whether fitting is justified rather than forcing every run into a final number.

Why an agent / simpler alternative

Useful judgment

Reconcile trajectory metadata, identify missing convergence evidence and coordinate analysis choices for expert review.

Simpler alternative

Use a validated laboratory workflow when the material, model and analysis assumptions are already appropriate.

Unsuitable when

The task would present unreviewed estimates as measured transport properties or run costly simulations without authorization.

Proposed inputs

  1. Name

    Material/run manifest

    Owner

    Authorized researcher

    Minimum scope

    Structure, model identity, temperature labels and trajectory provenance

    Quality check

    Check units, versions and consistent run settings

  2. Name

    Analysis criteria

    Owner

    Domain lead

    Minimum scope

    Approved convergence, diffusive-regime and uncertainty requirements

    Quality check

    Specify exclusions without inventing adequacy thresholds

Access and setup — proposed or unknown

Proposed minimum
  1. Review synthetic trajectory manifests and proposed checks; no molecular dynamics, fitting, installations or compute allocation.

Compatibility unknowns
  1. Current models, libraries, trajectory support, compute resources and runtime compatibility are unknown.

External actions requiring approval
  1. Data access, simulation spending, model downloads, output overwrites and publishing scientific claims requires authorization.

Expected deliverable and review checks

Proposed artifact

Analysis-plan candidate with run inventory, adequacy questions, proposed fit inputs and uncertainty-reporting requirements.

Critical checks
  1. Require comparable units/settings; preserve excluded runs and reasons; separate planned convergence checks from measured evidence.

Missing output evidence

This overlay adds no new output inspection, execution or validation; consult the referenced canonical source outcome limits.

Failure signals and proposed responses

  1. Signal

    One trajectory uses undocumented time units

    Why it matters

    Comparisons and derived estimates may be invalid

    Proposed response

    Resolve metadata before any analysis.

  2. Signal

    A run lacks evidence for the intended regime

    Why it matters

    A numerical fit could overstate support

    Proposed response

    Hold it outside the fit pending expert assessment.

Effort and cost — unknown

Setup work
  1. Define model provenance and scientific acceptance criteria.

Ongoing review
  1. Experts inspect convergence and actual numerical evidence from future authorized runs.

Unknowns
  1. Model suitability, trajectory adequacy, compute demand and uncertainty estimation costs are unknown.

First check — proposed, unexecuted

Status

proposed_unexecuted

Synthetic fixture

Invent three temperature-labelled run manifests with one missing time unit and one unresolved adequacy flag; prepare an analysis-readiness table.

Acceptance checks
  1. Hold problematic runs; claim no diffusivity, activation energy or scientific validation; execute nothing.

Stop conditions
  1. Stop if provenance, adequacy criteria or compute authority is unresolved.

Limits

  1. A manifest review cannot validate simulated transport, convergence, fitted parameters or agreement with physical measurements.

Tasks: Build & debugTravel & transportData & analysis

Useful for: ResearcherEngineer

Editorial relevance from the title and source-supported actions; not evidence of the operator’s occupation.
AtomisticSkills with Google Antigravity / coding agentsIndependent testing: No

Editorial candidate. This is source-backed reporting or a documented workflow. BotCurrent has not independently tested this task. A proposed Canadian adaptation is not evidence of a Canadian deployment or compliance certification.

Original source

https://github.com/learningmatter-mit/AtomisticSkills/blob/main/skills/mat-diffusion-analysis/SKILL.md ↗

Attributed to: Bowen Deng / learningmatter-mit

Community recipe
Original action type
coding_agent_orchestration
What the source supports
The agent coordinates multi-temperature molecular dynamics, checks convergence, analyzes remaining trajectories and fits an uncertainty-aware Arrhenius relationship to estimate ion transport.
Source outcome limits
Agent-compatible recipe only. No transport simulation run inspected. Estimates require adequate trajectories and a linear diffusive regime; named-agent compatibility is not proof of execution.
Evidence attribution caveat
This community recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Source approval description
Not specified in the recipe.
Named tools
AtomisticSkills, MACE or MatGL MCP, ASE trajectories, pymatgen, Python analysis helpers
Model or version
Unknown
Publication date
Unknown
Source last checked
2026-10-14
Demonstrated in Canada
Unknown
Canadian availability
Unknown
Proposed Canadian adaptation
Assess local compute, model licensing and research-data handling. No Canadian deployment or agent entitlement verified.
Recommended approval boundaries
Approve compute budget and data/model licenses; scientist reviews equilibration, fit and extrapolation before use.