fenggo

I-ReaxFF

I-ReaxFF: stand for Intelligent-Reactive Force Field

FindMole

Find molecular structrues or fragments in the energetical matrial trajectories, mainly suport the lammps software

chainer-chemistry

Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry

lantern

🔴Lantern Latest Download https://github.com/getlantern/lantern/releases/tag/latest 🔴蓝灯最新版本下载 https://github.com/getlantern/forum/issues/833 🔴

ReaxFF-nn_for_lammps

ReaxFF-nn mathine learning potentail for usage with lammps

TensorMol

Tensorflow + Molecules = TensorMol

alphafold_pytorch

An implementation of the DeepMind's AlphaFold based on PyTorch for research

AlphaZero_Gomoku

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

autograd

Efficiently computes derivatives of numpy code.

betago

BetaGo: AlphaGo for the masses, live on GitHub.

chemview

The new generation molecular viewer for IPython notebook

dpgen

The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field

emdk

EMDK: stand for Effective Material Design toolKit

flare

An open-source Python package for creating fast and accurate interatomic potentials.

HOMO-LUMO

Program to calculate the direct and indirect gap from VASP results

lammps

Public development project of the LAMMPS MD software package

MechanicalFF

minigo

An open-source implementation of the AlphaGoZero algorithm

OpenFermion

OpenFermion: The Electronic Structure Package For Quantum Computers.

OpenFermion-Psi4

OpenFermion plugin to interface with the electronic structure package Psi4.

psi4

Open-Source Quantum Chemistry – an electronic structure package in C++ driven by Python

ReaxFF-nn

Impliment of the ReaxFF-nn mathine learning potential in the GULP package

RocAlphaGo

An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016), details of which can be found on their website https://deepmind.com/publications.html.

schnetpack

SchNetPack - Deep Neural Networks for Atomistic Systems

tangent

Source-to-Source Debuggable Derivatives in Pure Python