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An atomistic fingerprint algorithm for learning ab initio molecular force fields.

Yu-Hang Tang1, Dongkun Zhang1, George Em Karniadakis1

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|January 22, 2018
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Summary
This summary is machine-generated.

We introduce a novel density-encoded canonically aligned fingerprint for robust and efficient data-driven modeling. This method improves accuracy in predicting molecular properties, outperforming existing techniques, especially for sparse atomic environments.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning for molecular modeling

Background:

  • Molecular fingerprints are crucial for data-driven modeling of potential energy surfaces and interatomic forces.
  • Existing methods face challenges with sparse or symmetric atomic neighborhoods.

Purpose of the Study:

  • To present a robust and efficient density-encoded canonically aligned fingerprint algorithm.
  • To enable accurate fitting of per-atom scalar and vector quantities for molecular modeling.

Main Methods:

  • Constructing a continuous density field via superimposed smoothing kernels.
  • Achieving rotational invariance through local canonical frame alignment via kernel minisum optimization.
  • Measuring density field differences using volume integrals computed with optimal quadrature rules.

Main Results:

  • The proposed fingerprint demonstrates superiority over principal component analysis-based methods, particularly for sparse and symmetric atomic configurations.
  • Efficient computation of density field distances using discrete sampling.
  • Characterization of weight function performance for interatomic potential fitting.

Conclusions:

  • The density-encoded canonically aligned fingerprint offers a superior approach for molecular modeling.
  • The method is efficient and applicable to various benchmark problems in computational chemistry and materials science.