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SchNetPack: A Deep Learning Toolbox For Atomistic Systems.

K T Schütt1, P Kessel1, M Gastegger1

  • 1Machine Learning Group , Technische Universität Berlin , 10587 Berlin , Germany.

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|November 28, 2018
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Summary
This summary is machine-generated.

SchNetPack is a PyTorch-based toolbox for developing deep neural networks to predict molecular and material properties. It simplifies the creation and application of atomistic neural networks for quantum chemistry research.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Predicting quantum-chemical properties of molecules and materials is computationally intensive.
  • Developing and applying deep neural networks (DNNs) for these predictions requires specialized tools.
  • Existing frameworks may lack ease of use or specific functionalities for atomistic DNNs.

Purpose of the Study:

  • To introduce SchNetPack, a comprehensive toolbox for developing and applying DNNs for molecular and material properties.
  • To facilitate the implementation and evaluation of novel atomistic neural network models.
  • To make advanced quantum-chemical predictions accessible to a broader research community.

Main Methods:

  • SchNetPack utilizes the PyTorch deep learning framework.
  • It provides basic building blocks for atomistic neural networks, including implementations of atom-centered symmetry functions and the SchNet model.
  • The toolbox manages DNN training and offers parallelization across multiple GPUs for large datasets.

Main Results:

  • SchNetPack enables efficient training and application of DNNs on large-scale datasets.
  • It includes ready-to-use scripts for training models on molecular and material datasets.
  • An interface to the Atomic Simulation Environment (ASE) is provided for easy access to trained models.

Conclusions:

  • SchNetPack simplifies the development and application of DNNs for predicting potential energy surfaces and quantum-chemical properties.
  • The toolbox lowers the barrier to entry for researchers using DNNs in chemistry and materials science.
  • It supports efficient handling of large datasets and integration with existing simulation workflows.