RuNNer

  • Level of openness 1 ★☆☆
  • Document quality 2 ★★☆

FORTRAN-based software package developed by the Behler Group for implementing Behler-Parinello neural network potentials. Potentials can be constructed, evaluated, and used for molecular dynamics simulations using LAMMPS. The newest generation of neural network potentials that take into account long-range electrostatic interactions are implemented.

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MLIP

  • Level of openness 1 ★☆☆
  • Document quality 2 ★★☆

Software package that implements moment tensor potentials. Potentials can be trained and used for molecular dynamics calculations using LAMMPS. Active learning combined with molecular dynamics calculations is also available.

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TensorFlow

  • Level of openness 2 ★★☆
  • Document quality 3 ★★★

A numerical library for machine learning. Various functions on machine learning (including supervised learning and unsupervised learning) are implemented in this package. Complex network can be expressed in a simple form by using data flow graphs. Efficient CPU/GPGPU parallel computation is supported to realise efficient operation on large scale data.

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NequIP

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Open source software for building and using machine learning potentials based on E(3)-equivariant graph neural networks, which can be trained on output files of simulation codes that can be read by ASE. Molecular dynamics calculations with LAMMPS can be performed using the trained potentials.

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2DMAT

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

2DMAT is a framework for applying a search algorithm to a direct problem solver to find the optimal solution. In  version 1.0, for solving a direct problem, 2DMAT offers the wrapper of the solver for the total-reflection high-energy positron diffraction (TRHEPD) experiment. As algorithms, it offers the Nelder-Mead method, the grid search method, the Bayesian optimization method, and the replica exchange Monte Carlo method. Users can define original direct problem solvers or the search algorithms.

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Strawberry Fields

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Python library for the design, simulation, and optimization of continuous-variable quantum optical circuits. It has high-level functions for solving problems including graph and network optimization, machine learning, and chemistry, and can perform training and optimization of quantum programs using the TensorFlow backend.

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n2p2

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Software package that implements Behler-Parinello type neural network potential. The package provides tools for training and evaluating potentials based on given structure-energy data. It also provides an interface with LAMMPS for performing molecular dynamics calculations.

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SIMPLE-NN

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Software package to implement Behler-Parinello neural network potentials. Potentials can be trained from structure-energy/ interatomic forces/stress data, and molecular dynamics calculations using LAMMPS can also be performed using learned potentials. A prediction uncertainty measure can also be calculated simultaneously.

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DeePMD-kit

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Python/C++ based software package that employs deep learning techniques for construction of interatomic potentials. It implements the Deep Potential, which defines atomic environment descriptors with respect to a local reference frame. The output of many first-principles and molecular dynamics applications can be used as training data, and the trained potentials can be used for molecular dynamics calculations using LAMMPS and path integral molecular dynamics calculations using i-PI.

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QUIP

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

A collection of software tools for molecular dynamics calculations. Various interatomic potentials and tight binding models are implemented, and numerous external applications can be invoked. It also supports training and evaluation of GAP (Gaussian Approximation Potential), which is a form of machine learning potential.

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