lab-cosmo Goto Github PK

.github

COSMO Github Page

archived_software-cookbook

ch4-manifold-training

chemiscope

An interactive structure/property explorer for materials and molecules

cluster-tutorials

cosmo-post-install

A set of post-installation shell scripts for Ubuntu

cosmo-tools

Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO

cosmoducation

Educational materials for teaching the fundamentals of representations at the atomic-scale.

equisolve

A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties and gradients.

equistore-examples

gch

Public repository for the generalized convex hull construction (GCH).

gle4md

Utilities to generate input parameters for Generalized Langevin Equation thermostats

glosim

A Python package to compute similarities between molecules and structures

glosim2

A rehaul of the glosim code, adding new features and removing many unused ones.

gromacs

i-pi

i-PI: a universal force engine

i-pi-dev_archive

Development version of i-PI

isv

An Interactive Sketch-Map Visualizer

kernel-tutorials

A set of utilities and pedagogic notebooks for the use of linear and kernel methods in atomistic modeling

lab-cosmo.github.io

Webpages for the code repositories of the Laboratory of Computational Science and Modelling @ EPFL

lammps

Internal COSMO developments of LAMMPS source

librascal

A scalable and versatile library to generate representations for atomic-scale learning

librascal-example-data

Data for running examples in librascal

marvel-college

Teaching materials for a high-school programming camp

metatensor

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

metatensor-docs

Documentation hosting for metatensor

metatensor-feedstock

A conda-smithy repository for metatensor.

metatensor-models

Atomistic models using metatensor

mops

Mathematical operations with GPU acceleration

ncenter-reps

A repository showcasing the construction of N-center features and Hamiltonian learning