Neural-Network-Models-for-chemistry

A collection of Neural Network Models for potential building

Density Functional Theory Method

• DeePKS, DeePHF
  DeePKS-kit is a program to generate accurate energy functionals for quantum chemistry systems, for both perturbative scheme (DeePHF) and self-consistent scheme (DeePKS).

• NeuralXC
  Implementation of a machine learned density functional.

• MOB-ML
  Machine Learning for Molecular Orbital Theory.

• DM21
  Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem.

• NN-GGA, NN-NRA, NN-meta-GGA, NN-LSDA
  Completing density functional theory by machine-learning hidden messages from molecules.

• FemiNet
  FermiNet is a neural network for learning highly accurate ground state wavefunctions of atoms and molecules using a variational Monte Carlo approach.

• DeePQMC
  DeepQMC implements variational quantum Monte Carlo for electrons in molecules, using deep neural networks written in PyTorch as trial wave functions.

• PauliNet
  PauliNet builds upon HF or CASSCF orbitals as a physically meaningful baseline and takes a neural network approach to the SJB wavefunction in order tocorrect this baseline towards a high-accuracy solution.

• DeePErwin
  DeepErwin is python package that implements and optimizes wave function models for numerical solutions to the multi-electron Schrödinger equation.

• Jax-DFT
  JAX-DFT implements one-dimensional density functional theory (DFT) in JAX. It uses powerful JAX primitives to enable JIT compilation, automatical differentiation, and high-performance computation on GPUs.

• sns-mp2
  Improving the accuracy of Moller-Plesset perturbation theory with neural networks

• DeepH-pack
  Deep neural networks for density functional theory Hamiltonian.

• kdft
  The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.

• ML-DFT
  ML-DFT: Machine learning for density functional approximations This repository contains the implementation for the kernel ridge regression based density functional approximation method described in the paper "Quantum chemical accuracy from density functional approximations via machine learning".

• D4FT(only arxiv, the github usl seems closed)
  this work proposed a deep learning approach to KS-DFT. First, in contrast to the conventional SCF loop, directly minimizing the total energy by reparameterizing the orthogonal constraint as a feed-forward computation. They prove that such an approach has the same expressivity as the SCF method yet reduces the computational complexity from O(N^4) to O(N^3)

• SchOrb Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Molecular Force Field Method

• DeePMD
  A package designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics.
• Torch-ANI
  TorchANI is a pytorch implementation of ANI model.
• mdgrad
  Pytorch differentiable molecular dynamics
• Schrodinger-ANI
  A neural network potential energy function for use in drug discovery, with chemical element support extended from 41% to 94% of druglike molecules based on ChEMBL.
• NerualForceFild
  The Neural Force Field (NFF) code is an API based on SchNet, DimeNet, PaiNN and DANN . It provides an interface to train and evaluate neural networks for force fields. It can also be used as a property predictor that uses both 3D geometries and 2D graph information.
• NNPOps
  The goal of this project is to promote the use of neural network potentials (NNPs) by providing highly optimized, open source implementations of bottleneck operations that appear in popular potentials.
• RuNNer
  A program package for constructing high-dimensional neural network potentials,4G-HDNNPs,3G-HDNNPs.
• aenet
  The Atomic Energy NETwork (ænet) package is a collection of tools for the construction and application of atomic interaction potentials based on artificial neural networks.
• sGDML
  Symmetric Gradient Domain Machine Learning
• GAP
  This package is part of QUantum mechanics and Interatomic Potentials
• QUIP
  The QUIP package is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to other software such as LAMMPS, CP2K and also the python framework ASE.
• NNP-MM
  NNP/MM embeds a Neural Network Potential into a conventional molecular mechanical (MM) model.
• GAMD
  Data and code for Graph neural network Accelerated Molecular Dynamics.
• PFP
  Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality.
• TeaNet
  universal neural network interatomic potential inspired by iterative electronic relaxations.
• n2p2
  This repository provides ready-to-use software for high-dimensional neural network potentials in computational physics and chemistry.
• Nequip
  NequIP is an open-source code for building E(3)-equivariant interatomic potentials.
• E3NN
  Euclidean neural networks,The aim of this library is to help the development of E(3) equivariant neural networks. It contains fundamental mathematical operations such as tensor products and spherical harmonics.
• SchNet
  SchNet is a deep learning architecture that allows for spatially and chemically resolved insights into quantum-mechanical observables of atomistic systems.
• SchNetPack
  SchNetPack aims to provide accessible atomistic neural networks that can be trained and applied out-of-the-box, while still being extensible to custom atomistic architectures.
• G-SchNet
  Implementation of G-SchNet - a generative model for 3d molecular structures.
• PhysNet
  PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges.
• DimeNet
  Directional Message Passing Neural Network.
• GemNet
  Universal Directional Graph Neural Networks for Molecules.
• DeePMoleNet
  DeepMoleNet is a deep learning package for molecular properties prediction.
• AirNet
  A new GNN-based deep molecular model by MindSpore.
• TorchMD-Net
  TorchMD-NET provides graph neural networks and equivariant transformer neural networks potentials for learning molecular potentials.
• AQML
  AQML is a mixed Python/Fortran/C++ package, intends to simulate quantum chemistry problems through the use of the fundamental building blocks of larger systems.
• TensorMol
  A pakcages of NN model chemistry, contains Behler-Parrinello with electrostatics, Many Body Expansion Bonds in Molecules NN, Atomwise, Forces, Inductive Charges.
• SpookyNet
  Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects.
• AIMNET
  This repository contains reference AIMNet implementation along with some examples and menchmarks.
• charge_transfer_nnp
  About Graph neural network potential with charge transfer with nequip model.
• AMP
  Amp: A modular approach to machine learning in atomistic simulations(https://github.com/ulissigroup/amptorch)
• SCFNN
  A self consistent field neural network (SCFNN) model.
• jax-md
  JAX MD is a functional and data driven library. Data is stored in arrays or tuples of arrays and functions transform data from one state to another.
• EANN
  Embedded Atomic Neural Network (EANN) is a physically-inspired neural network framework. The EANN package is implemented using the PyTorch framework used to train interatomic potentials, dipole moments, transition dipole moments and polarizabilities of various systems.
• espaloma
  Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm.
• MDsim
  Training and simulating MD with ML force fields
• ForceNet We demonstrate that force-centric GNN models without any explicit physical constraints are able to predict atomic forces more accurately than state-of-the-art energy centric GNN models, while being faster both in training and inference.
• DIG
  A library for graph deep learning research.
• scn
  Spherical Channels for Modeling Atomic Interactions
• spinconv
  Rotation Invariant Graph Neural Networks using Spin Convolutions.
• HIPPYNN
  a modular library for atomistic machine learning with pytorch.
• VisNet
  a scalable and accurate geometric deep learning potential for molecular dynamics simulation
• flare
  FLARE is an open-source Python package for creating fast and accurate interatomic potentials.)
• alignn
  The Atomistic Line Graph Neural Network (https://www.nature.com/articles/s41524-021-00650-1) introduces a new graph convolution layer that explicitly models both two and three body interactions in atomistic systems.
• So3krates
  Repository for training, testing and developing machine learned force fields using the So3krates model.
• spice-model-five-net
  Contains the five equivariant transformer models about the spice datasets(https://github.com/openmm/spice-dataset/releases/tag/1.1).
• sake
  Spatial Attention Kinetic Networks with E(n)-Equivariance
• eqgat
  Pytorch implementation for the manuscript Representation Learning on Biomolecular Structures using Equivariant Graph Attention
• phast PyTorch implementation for PhAST: Physics-Aware, Scalable and Task-specific GNNs for Accelerated Catalyst Design
• GNN-LF
  Graph Neural Network With Local Frame for Molecular Potential Energy Surface
• Cormorant
  We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems.
• LieConv
  Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
• torchmd-net/ET
  Neural network potentials based on graph neural networks and equivariant transformers
• GemNet
  GemNet: Universal Directional Graph Neural Networks for Molecules
• equiformer
  Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
• VisNet-LSRM
  Inspired by fragmentation-based methods, we propose the Long-Short-Range Message-Passing (LSR-MP) framework as a generalization of the existing equivariant graph neural networks (EGNNs) with the intent to incorporate long-range interactions efficiently and effectively.
• AP-net
  AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials

Semi-Empirical Method

• OrbNet
  OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy.

• AIQM1
  Artificial intelligence-enhanced quantum chemical method with broad applicability.

• BpopNN
  Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations.

• Delfta
  The DelFTa application is an easy-to-use, open-source toolbox for predicting quantum-mechanical properties of drug-like molecules. Using either ∆-learning (with a GFN2-xTB baseline) or direct-learning (without a baseline), the application accurately approximates DFT reference values (ωB97X-D/def2-SVP).

Coarse-Grained Method

• cgnet
  Coarse graining for molecular dynamics
• SchNet-CG
  We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.

Enhanced Sampling Method

• VES-NN
  Neural networks-based variationallyenhanced sampling

• Accelerated_sampling_with_autoencoder
  Accelerated sampling framework with autoencoder-based method

QM/MM Model

• NNP-MM
  NNP/MM embeds a Neural Network Potential into a conventional molecular mechanical (MM) model. We have implemented this using the Custom QM/MM features of NAMD 2.13, which interface NAMD with the TorchANI NNP python library developed by the Roitberg and Isayev groups.
• DeeP-HP
  support for neural networks potentials (ANI-2X, DeepMD).

Charge Model

• gimlet
  Graph Inference on Molecular Topology. A package for modelling, learning, and inference on molecular topological space written in Python and TensorFlow.

Post-HF Method

• LNO-CCSD(T)

• DLPNO-CCSD(T)

• PNO-CCSD(T)