Unified theory of atom-centered representations and graph convolutional
machine-learning schemes
- URL: http://arxiv.org/abs/2202.01566v1
- Date: Thu, 3 Feb 2022 12:56:22 GMT
- Title: Unified theory of atom-centered representations and graph convolutional
machine-learning schemes
- Authors: Jigyasa Nigam, Guillaume Fraux, Michele Ceriotti
- Abstract summary: atom-centered density correlations (ACDC) are used as a basis for a body-ordered, symmetry-adapted expansion of the targets.
We generalize ACDC to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data-driven schemes that associate molecular and crystal structures with
their microscopic properties share the need for a concise, effective
description of the arrangement of their atomic constituents. Many types of
models rely on descriptions of atom-centered environments, that are associated
with an atomic property or with an atomic contribution to an extensive
macroscopic quantity. Frameworks in this class can be understood in terms of
atom-centered density correlations (ACDC), that are used as a basis for a
body-ordered, symmetry-adapted expansion of the targets. Several other schemes,
that gather information on the relationship between neighboring atoms using
graph-convolutional (or message-passing) ideas, cannot be directly mapped to
correlations centered around a single atom. We generalize the ACDC framework to
include multi-centered information, generating representations that provide a
complete linear basis to regress symmetric functions of atomic coordinates, and
form the basis to systematize our understanding of both atom-centered and
graph-convolutional machine-learning schemes.
Related papers
- Cartesian atomic cluster expansion for machine learning interatomic potentials [0.0]
Machine learning interatomic potentials are revolutionizing atomistic modelling in material science and chemistry.
We propose a Cartesian-coordinates-based atomic density expansion that exhibits good accuracy, stability, and generalizability.
We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys.
arXiv Detail & Related papers (2024-02-12T08:17:23Z) - Binding Dynamics in Rotating Features [72.80071820194273]
We propose an alternative "cosine binding" mechanism, which explicitly computes the alignment between features and adjusts weights accordingly.
This allows us to draw direct connections to self-attention and biological neural processes, and to shed light on the fundamental dynamics for object-centric representations to emerge in Rotating Features.
arXiv Detail & Related papers (2024-02-08T12:31:08Z) - Atomic and Subgraph-aware Bilateral Aggregation for Molecular
Representation Learning [57.670845619155195]
We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA)
ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information.
Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
arXiv Detail & Related papers (2023-05-22T00:56:00Z) - Completeness of Atomic Structure Representations [0.0]
We present a novel approach to construct descriptors of emphfinite correlations based on the relative arrangement of particle triplets.
Our strategy is demonstrated on a class of atomic arrangements that are specifically built to defy a broad class of conventional symmetric descriptors.
arXiv Detail & Related papers (2023-02-28T17:11:42Z) - Heterogeneous reconstruction of deformable atomic models in Cryo-EM [30.864688165021054]
We describe a heterogeneous reconstruction method based on an atomistic representation whose deformation is reduced to a handful of collective motions.
We show for each distribution that our approach is able to recapitulate the intermediate atomic models with atomic-level accuracy.
arXiv Detail & Related papers (2022-09-29T22:35:35Z) - Correlated steady states and Raman lasing in continuously pumped and
probed atomic ensembles [68.8204255655161]
We consider an ensemble of Alkali atoms that are continuously optically pumped and probed.
Due to the collective scattering of photons at large optical depth, the steady state of atoms does not correspond to an uncorrelated tensor-product state.
We find and characterize regimes of Raman lasing, akin to the model of a superradiant laser.
arXiv Detail & Related papers (2022-05-10T06:54:54Z) - Equivariant representations for molecular Hamiltonians and N-center
atomic-scale properties [0.0]
We discuss a family of structural descriptors that generalize the very successful atom-centered density correlation features to the N-centers case.
We show in particular how this construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian written in an atom-centered orbital basis.
arXiv Detail & Related papers (2021-09-24T17:19:57Z) - Optimal radial basis for density-based atomic representations [58.720142291102135]
We discuss how to build an adaptive, optimal numerical basis that is chosen to represent most efficiently the structural diversity of the dataset at hand.
For each training dataset, this optimal basis is unique, and can be computed at no additional cost with respect to the primitive basis.
We demonstrate that this construction yields representations that are accurate and computationally efficient.
arXiv Detail & Related papers (2021-05-18T17:57:08Z) - The role of feature space in atomistic learning [62.997667081978825]
Physically-inspired descriptors play a key role in the application of machine-learning techniques to atomistic simulations.
We introduce a framework to compare different sets of descriptors, and different ways of transforming them by means of metrics and kernels.
We compare representations built in terms of n-body correlations of the atom density, quantitatively assessing the information loss associated with the use of low-order features.
arXiv Detail & Related papers (2020-09-06T14:12:09Z) - Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials.
The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other.
The results show that our model can get a promising prediction accuracy with cross-validation.
arXiv Detail & Related papers (2020-05-27T13:32:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.