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
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.
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
- 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
- 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
Review synthetic trajectory manifests and proposed checks; no molecular dynamics, fitting, installations or compute allocation.
- Compatibility unknowns
Current models, libraries, trajectory support, compute resources and runtime compatibility are unknown.
- External actions requiring approval
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
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
- Signal
One trajectory uses undocumented time units
- Why it matters
Comparisons and derived estimates may be invalid
- Proposed response
Resolve metadata before any analysis.
- 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
Define model provenance and scientific acceptance criteria.
- Ongoing review
Experts inspect convergence and actual numerical evidence from future authorized runs.
- Unknowns
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
Hold problematic runs; claim no diffusivity, activation energy or scientific validation; execute nothing.
- Stop conditions
Stop if provenance, adequacy criteria or compute authority is unresolved.
Limits
A manifest review cannot validate simulated transport, convergence, fitted parameters or agreement with physical measurements.
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.