An interface package to use Torch (the open-source numerical library for machine learning) from Python. Users can easily implement deep learning based on neural networks, and can use various state-of-the-art methods. This package supports GPGPU parallel computation, and realises high-speed operation. A front-end interface for C++ is also prepared.
QuCumber is an open-source Python package that implements neural-network quantum state reconstruction of many-body wavefunctions from measurement data such as magnetic spin projections, orbital occupation number. Given a training dataset of measurements, QuCumber discovers the most likely quantum state compatible with the measurements by finding the optimal set of parameters of a restricted Boltzmann machine (RBM).
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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.
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.
An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.
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.
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.
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.
An open-source numerical library for machine learning. Various functions related to deep learning are implemented. This package directly treats equations as such, and have useful routines such as matrix operation and auto partial derivative. Users can convert their codes into C language, and can compile it. High speed operation by GPGPU parallel calculation is supported. A number of tutorials are available.
An open-source numerical library for machine learning. Various functions related to deep learning based on neural networks are implemented. Users can implement complex network with flexible description, and can try various state-of-the-art methods. This package is used in a number of companies in the world. This package is written by the script language, lua.