Compressing local atomic neighbourhood descriptors
arXiv:2112.13055 · doi:10.1038/s41524-022-00847-y
Abstract
Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements e.g. the length of body-ordered descriptors, such as the Smooth Overlap of Atomic Positions (SOAP) power spectrum (3-body) and the Atomic Cluster Expansion (ACE) (multiple body-orders), scales as where is the body-order and is the number of radial basis functions used in the density expansion. We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum. Firstly, we show that the power spectrum is amenable to lossless compression with respect to both and , so that the descriptor length can be reduced from to . Secondly, we introduce a generalized SOAP kernel, where compression is achieved through the use of the total, element agnostic density, in combination with radial projection. The ideas used in the generalized kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the Atom Centered Symmetry Functions (ACSF). Finally, both compression approaches are shown to offer comparable performance to the original descriptor across a variety of numerical tests.
arXiv comment16 pages, 10 figures
References in corpus (28)
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- On representing chemical environments
- SchNet - a deep learning architecture for molecules and materials
- E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
- Perspective on density functional theory
- Moment Tensor Potentials: a class of systematically improvable interatomic potentials
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials
- DScribe: Library of Descriptors for Machine Learning in Materials Science
- Comparing molecules and solids across structural and alchemical space
- Machine Learning Unifies the Modelling of Materials and Molecules
- Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
- Physics-inspired structural representations for molecules and materials
- Machine Learning Energies of 2 M Elpasolite (ABCD) Crystals
- Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
- Alchemical and structural distribution based representation for improved QML
- WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
- De novo exploration and self-guided learning of potential-energy surfaces
- On the Completeness of Atomic Structure Representations
- Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
- Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements
- Recursive evaluation and iterative contraction of -body equivariant features
- Ranking the information content of distance measures
- An information theoretic approach to statistical dependence: copula information
- Efficient implementation of atom-density representations
- An assessment of the structural resolution of various fingerprints commonly used in machine learning
- Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials
- Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments
- Optimal radial basis for density-based atomic representations