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Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions.

Siddarth K Achar1,2, Leonardo Bernasconi3, J Karl Johnson2

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We developed DeepCDP, a deep learning method to predict electron densities for chemical systems. This approach offers accurate, computationally efficient predictions for materials chemistry applications, overcoming limitations of traditional quantum mechanical methods.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Accurate electron densities are vital for understanding chemical reactions, ion transport, and charge transfer in materials.
  • Traditional quantum mechanical (QM) methods like density functional theory (DFT) struggle with scalability for large systems and long timescales.
  • Limitations in QM methods hinder the study of complex dynamical chemical systems.

Purpose of the Study:

  • To develop a novel deep neural network machine learning formalism, DeepCDP, for predicting charge densities.
  • To overcome the computational limitations of traditional QM methods for dynamical chemical systems.
  • To enable accurate and efficient prediction of electron densities using only atomic positions.

Main Methods:

  • Developed Deep Charge Density Prediction (DeepCDP), a deep neural network formalism.
  • Utilized weighted smooth overlap of atomic positions to fingerprint environments on a grid-point basis.
  • Mapped atomic fingerprints to electron density data generated from QM simulations for training.
  • Trained models on diverse systems including bulk materials (Cu, LiF, Si), molecular systems (water), and 2D materials (graphane).

Main Results:

  • DeepCDP achieved high prediction accuracy with R2 values > 0.99 and MSE ~ 10-5e2 Å-6 for most trained systems.
  • Demonstrated linear scaling with system size and high parallelizability, significantly reducing computational cost.
  • Accurately predicted excess charge in protonated systems and tracked proton locations.
  • Showcased model transferability to predict electron densities for unseen systems with trained atomic species.

Conclusions:

  • DeepCDP provides a computationally efficient and accurate alternative to traditional QM methods for electron density prediction.
  • The method enables the study of large-scale charge transport and chemical reactions in materials.
  • DeepCDP's transferability expands its applicability across various chemical systems.
  • This approach facilitates the investigation of complex dynamical processes in materials chemistry.