Release v1.1.0 · learningmatter-mit/AtomisticSkills

What's Changed

Component	v1.0.0	v1.1.0	Added
Skills	119	121	+2
Workflows	9	9	—
MCP Tools	48	48	—
Tool Servers	19	19	—

Skill	Description	Author
`ml-bayesian-optimization`	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 candidates.	@bowen-bd
`drug-pocket-detection`	Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or ML (P2Rank). Returns ranked pockets with lining residues, geometric center, volume, and druggability score.	@mcox3406
`drug-mmpbsa-gbsa` (renamed from `drug-mmgbsa`)	Compute single-trajectory MM-GBSA and/or MM-PBSA binding free energy estimates from a protein-ligand MD trajectory. Two backends: fast OpenMM GBn2 and AmberTools MMPBSA.py supporting both GB and PB.	@mcox3406

MCP: Atomate2 job tracker enhanced with sub-job aggregation and error extraction; research standards refactored with intent classification
Skills: Off-equilibrium sampling now caches input parameters; ASE ExpCellFilter import compatibility fixed
Sorption: Fixed unphysical repulsion via spin/charge normalization; ODAC Widom spin handling corrected (PR #31)
GCMC: Fixed finite_or_none naming inconsistency and config path resolution
Drug discovery: PBC correction in drug-mmgbsa; fpocket added to drugdisc-agent
Config: Multi-agent support in configure_mcp.py
Docs & CI: Documentation website launched; GitHub Pages deployment; pre-commit hooks with ruff; README updated with video demo, arXiv badge, and citation