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Electronic structure at coarse-grained resolutions from supervised machine learning.

Nicholas E Jackson1,2, Alec S Bowen2, Lucas W Antony2

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|March 28, 2019
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Summary
This summary is machine-generated.

This study introduces artificial neural network electronic coarse graining (ANN-ECG) to directly map molecular electronic structure to coarse-grained models. This machine learning method accelerates simulations by eliminating computationally intensive backmapping steps.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Understanding electronic structure in soft materials requires combining classical and quantum simulations.
  • Sampling conformational space is computationally demanding.
  • Coarse-grained (CG) models accelerate simulations but require backmapping for quantum calculations.

Purpose of the Study:

  • To develop a machine learning approach for directly calculating conformationally dependent electronic structure in CG models.
  • To accelerate computational studies of soft materials by eliminating backmapping.
  • To ensure consistency between CG spatial resolution and electronic structure calculations.

Main Methods:

  • Developed artificial neural network electronic coarse graining (ANN-ECG).
  • Mapped conformationally dependent electronic structure directly to CG pseudo-atom configurations.
  • Averaged over decimated degrees of freedom to accelerate simulations.

Main Results:

  • ANN-ECG eliminates the need for computationally intensive backmapping and repeated quantum-chemical calculations.
  • The approach provides accurate electronic structure information consistent with CG spatial resolution.
  • ANN-ECG can identify computationally optimal CG resolutions.

Conclusions:

  • ANN-ECG offers a significant acceleration for computational studies requiring electronic structure in soft materials.
  • This method bridges the gap between CG simulations and quantum-chemical accuracy.
  • The approach is versatile for optimizing CG model resolution for specific applications.