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Agent Workflows · By learningmatter-mit

6 skills found.
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1

Set up, check or troubleshoot how AtomisticSkills runs on this machine -- creating its Python environments, connecting its MCP servers, choosing uv or a container runtime, and configuring API keys.

learningmatter-mit/AtomisticSkills176—~1.2kAutomated safety check: NotesMITyesterday
2

Query Materials Project database for crystal structures, computed properties, elastic/magnetic data, and structurally similar materials using the MP API.

learningmatter-mit/AtomisticSkills176—~3kAutomated safety check: PassMITyesterday
3

Simulate long-time kinetics using rejection-free kinetic Monte Carlo (KMC) with event catalog construction, rate assignment via TST/Arrhenius, detailed-balance validation, superbasin handling, and…

learningmatter-mit/AtomisticSkills176—~3.8kAutomated safety check: PassMITyesterday
4

Iteratively optimize expensive black-box objectives — such as materials properties, experimental yields, or simulation outputs — by learning from past evaluations to select the most promising next…

learningmatter-mit/AtomisticSkills176—~2.3kAutomated safety check: PassMITyesterday
5

train a Cluster Expansion (CE) for lattice-based Monte Carlo simulation of disordered materials.

learningmatter-mit/AtomisticSkills176—~2.1kAutomated safety check: PassMITyesterday
6

Hierarchically decompose high-level scientific workflows (from literature or user-proposed) into executable sequences of existing SKILLs and MCP tools for the research plan.

learningmatter-mit/AtomisticSkills176—~936Automated safety check: PassMITyesterday