Guest Editorial: Special Topic on Software for Atomistic Machine Learning

Abstract

A survey of the contributions to the Journal of Chemical Physics' Special Topic on Software for Atomistic Machine Learning.

References in corpus (13)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• DeePMD-kit v2: A software package for Deep Potential models
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• Updates to the DScribe Library: New Descriptors and Derivatives
• SchNetPack 2.0: A neural network toolbox for atomistic machine learning
• A unified framework for machine learning collective variables for enhanced sampling simulations: $\text{mlcolvar}$
• Developments and Further Applications of Ephemeral Data Derived Potentials
• PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations
• ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training
• DeepQMC: an open-source software suite for variational optimization of deep-learning molecular wave functions
• A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces and nanoparticles
• Stress and heat flux via automatic differentiation
• Generating candidates in global optimization algorithms using complementary energy landscapes

Cited by in corpus (2)

• chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
• Machine-Learned Potentials for Solvation Modeling