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08/25/2023
Physics-inspired Equivariant Descriptors of Non-bonded Interactions
Most of the existing machine-learning schemes applied to atomic-scale si...
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05/30/2023
Smooth, exact rotational symmetrization for deep learning on point clouds
Point clouds are versatile representations of 3D objects and have found ...
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03/07/2023
Wigner kernels: body-ordered equivariant machine learning without a basis
Machine-learning models based on a point-cloud representation of a physi...
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02/28/2023
Completeness of Atomic Structure Representations
Achieving a complete and symmetric description of a group of point parti...
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02/16/2023
Fast evaluation of real spherical harmonics and their derivatives in Cartesian coordinates
Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted b...
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09/21/2022
A data-driven interpretation of the stability of molecular crystals
Due to the subtle balance of intermolecular interactions that govern str...
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09/05/2022
A smooth basis for atomistic machine learning
Machine learning frameworks based on correlations of interatomic positio...
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06/28/2022
Electronic-structure properties from atom-centered predictions of the electron density
The electron density of a molecule or material has recently received maj...
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05/11/2022
Predicting hot electrons free energies from ground-state data
Machine-learning potentials are usually trained on the ground-state, Bor...
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02/03/2022
Unified theory of atom-centered representations and graph convolutional machine-learning schemes
Data-driven schemes that associate molecular and crystal structures with...
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01/18/2022
Incompleteness of graph convolutional neural networks for points clouds in three dimensions
Graph convolutional neural networks (GCNN) are very popular methods in m...
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09/24/2021
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Symmetry considerations are at the core of the major frameworks used to ...
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05/18/2021
Optimal radial basis for density-based atomic representations
The input of almost every machine learning algorithm targeting the prope...
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12/22/2020
Improving Sample and Feature Selection with Principal Covariates Regression
Selecting the most relevant features and samples out of a large set of c...
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09/06/2020
The role of feature space in atomistic learning
Efficient, physically-inspired descriptors of the structure and composit...
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08/27/2020
Multi-scale approach for the prediction of atomic scale properties
Electronic nearsightedness is one of the fundamental principles governin...
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06/21/2020
Learning the electronic density of states in condensed matter
The electronic density of states (DOS) quantifies the distribution of th...
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02/12/2020
