Skill that audits and rewrites content to remove AI writing patterns. Use it with your favorite agents including Claude Code, OpenClaw, and Hermes.

Dual graph transformer for molecular property prediction

Quantum Operator Learning Library (Quoll)

tutorials for installing, configuring, and using atomate2

Lightweight diffusion transformer for crystalline materials discovery

GPU-accelerated diffuse scattering from MD trajectories: S(q, ω), 3D-ΔPDF, and phonon dispersion

python Parameter EStimation TOolbox

Terminal based crystal structure viewer

Research Data Management for Chemists Workshop Material

To implement the phonon SED (spectral energy desity) method in 2010, phonon lifetime can be calculated.

Extracts and formats text annotations from a PDF file

Ball-and-stick crystal structure viewer for Python. Publication-quality vector output (SVG, PDF) and trajectory animations (GIF, MP4) via matplotlib, with ASE and pymatgen interoperability.

Self-describing sparse tensor data format for atomistic machine learning and beyond

Chemical intuition for surface science in a package.

Intelligent Scientific Agent for Materials Design

CBE 504: Chemical Kinetics and Reaction Engineering

A RL framework for Crystal Structure Generation using GRPO

Kosmos: An AI Scientist for Autonomous Discovery - An implementation and adaptation to be driven by Claude Code or API - Based on the Kosmos AI Paper - https://arxiv.org/abs/2511.02824

Skala exchange-correlation functional

Space group equivariant crystal diffusion [NeurIPS 2025]

Training Neural Network potentials through customizable routines in JAX.

Public releases of the Clinamen2 optimization framework

Python software that implements the formulation for evaluating the effects of electron-phonon coupling on the optical properties of defects. Interfaces with the mace machine learning interatomic po…

A unified package for post-processing optical properties of point defects from first principles calculation.

MACE-Field - Fast and accurate machine learning interatomic potentials under an external electric field for predicting dielectric properties.

Graph neural network prediction of electronic Hamiltonians in atomic orbital representation with many body messages

Nondiagonal supercells for phonopy

Interactive browser visualizations for materials science: crystal structures/molecules, trajectories, convex hulls, phase diagrams, Fermi surfaces, bands+DOS, Brillouin zones, etc.