AI & LLM Engineering · By lamm-mit

A skill your agent uses when predicting biomolecular structures (proteins, RNA, DNA, ligands) with the open-source Boltz diffusion model as an alternative to AlphaFold3.

lamm-mit/scienceclaw

Agentic materials discovery and DFT simulation framework using ASE, Quantum ESPRESSO, and Claude LLMs via LangGraph.

lamm-mit/scienceclaw

3D tetrahedral FEM modal analysis of a membrane STL. An agent skill from lamm-mit/scienceclaw.

lamm-mit/scienceclaw

Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns

lamm-mit/scienceclaw

Soft differentiable drop-in replacements for non-differentiable JAX functions (abs, relu, sort, argmax, comparison, logical operators, etc.) with adjustable softening strength.

lamm-mit/scienceclaw

Approximate deep learning model components with symbolic equations using PySR

lamm-mit/scienceclaw

Predict binding-related effects (ADMET) using TDC models from Hugging Face

lamm-mit/scienceclaw

Run structure relaxation and phonon calculations using Meta's UMA (Universal Materials Accelerator) via fairchem

lamm-mit/scienceclaw