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plin1112's Projects

abinit icon abinit

The official github mirror of the Abinit repository. We welcome bug fixes and improvements. Note that most of the active developments are hosted on our https://gitlab.abinit.org/ server. Before embarking on making significant changes, please contact the Abinit group.

aimnet icon aimnet

Atoms In Molecules Neural Network Potential

aix360 icon aix360

Interpretability and explainability of data and machine learning models

allen-tildesley icon allen-tildesley

Sample code from Allen & Tildesley's Computer Simulation of Liquids, 1st Ed. See https://github.com/Allen-Tildesley/examples for modernized and updated examples from the 2nd edition.

ani1_dataset icon ani1_dataset

A data set of 20 million calculated off-equilibrium conformations for organic molecules

ase_ani icon ase_ani

ANI-1 neural net potential with python interface (ASE)

ased3 icon ased3

Python-native(ish) implementation of Grimme's DFT-D3(BJ) correction for use in Atomic Simulation Environment (ASE)

auto3d_pkg icon auto3d_pkg

Auto3D generates low-energy conformers from SMILES/SDF

auto_martini icon auto_martini

Automatic MARTINI parametrization of small organic molecules

autografs icon autografs

Python library for generation of MOFs, COFs, Zeolites...

automaticweightedloss icon automaticweightedloss

Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, Auxiliary Tasks in Multi-task Learning

autosurf-v1.3 icon autosurf-v1.3

AUTOSURF is a powerful software designed to robustly compute electronic energies and incorporate them into a global potential energy surface (PES). The code completely automates all of the steps and procedures that go into fitting various classes of PESs, being well-suited for treating systems with highly-anisotropic interactions.

btw-ff icon btw-ff

A forcefield to describe the structures and properties of metal-organic frameworks

cclib icon cclib

Parsers and algorithms for computational chemistry logfiles

cheatsheets-ai icon cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

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