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@bowen-bd bowen-bd released this 28 Apr 19:50
· 287 commits to main since this release
v1.0.0
811faf2

AtomisticSkills is a composable framework for AI-driven atomistic materials research. Built on the hierarchical decomposition of complex scientific tasks into Workflows → Skills → Tools, it enables coding AI agents to autonomously conduct multi-stage materials, chemistry, and drug discovery research by combining modular, reusable capabilities.

The framework integrates state-of-the-art Machine Learning Interatomic Potentials (MLIPs), DFT calculations, generative AI, database APIs, and advanced simulation methods through the Model Context Protocol (MCP) tools and Skills, making advanced materials research accessible to AI copilots.

Included in this release

  • 9 Workflows: End-to-end research campaign templates that combine multiple skills to solve high-level scientific goals.
  • 119 Skills: Pre-configured tutorials and scripts covering an extensive range of research tasks in chemistry, materials science, and drug discovery.
  • 48 MCP Tools: Fundamental research primitives directly callable by AI agents, spanning molecular dynamics, DFT preparation, relaxation, and analysis.
  • 19 Tool Servers: Specialized backend integrations providing unified interfaces to MACE, MatGL, FAIRCHEM, Atomate2, MatterGen, SMOL, ADiT, DiffCSP, and more.

Key Features

  • Simulation Infrastructure: Multi-framework MLIP support with unified APIs for relaxation, MD, and fine-tuning. Includes DFT integration via Atomate2 and ORCA, and cluster expansion via SMOL.
  • Database APIs: Query structures, properties, bioactivity data, and literature from Materials Project, ChEMBL, PDB, PubChem, and ArXiv.
  • Property Evaluation: Extensive tools for computing thermodynamic stability, phase diagrams, phonons, QHA thermal expansion, elastic tensors, melting points, ionic diffusion, NEB barriers, surface energies, and more.
  • Experimental & Machine Learning Tools: Features for synthesis recommendation, XRD calculation, ADMET prediction, molecular docking, MLIP fine-tuning, benchmarking, and generative crystal design via MatterGen.

Contributors

A huge thank you to everyone who contributed skills to this release:

  • Bowen Deng (76 skills)
  • Matthew Cox (17 skills)
  • Miguel Steiner (3 skills)
  • Jesus Diaz Sanchez (3 skills)
  • Juno Nam (3 skills)
  • Yu Yao (3 skills)
  • Artur Lyssenko (3 skills)
  • Nofit Segal (2 skills)
  • Sathya Edamadaka (2 skills)
  • Sauradeep Majumdar (2 skills)
  • ChazzBM3 (1 skill)
  • Mingrou Xie (1 skill)
  • Ty Perez (1 skill)
  • Bohan Li (1 skill)
  • Jurģis Ruža (1 skill)
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