E(n)-Equivariant Cartesian Tensor Passing Potential

Abstract

Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is limited to scalars, vectors, and tensors. In this work, we proposed High-order Tensor Passing Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. Compared to high order tensor models based on spherical vectors, this network is simpler and can achieve comparable accuracy with much fewer parameters. The tests in several datasets demonstrate HotPP is a promising new approach that warrants further investigation.

27 pages, 5 figures. (Email: [email protected]); Keywords: machine-learning force field; neural network; molecular dynamics simulations; ab initio calculations; materials design

References in corpus (14)

• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Machine-learning based interatomic potential for amorphous carbon
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
• Physics-inspired structural representations for molecules and materials
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• The Phase Diagram of a Deep Potential Water Model
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• An Accurate and Transferable Machine Learning Potential for Carbon
• Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
• The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
• Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
• Hyperspatial optimisation of structures
• Modeling liquid water by climbing up Jacob's ladder in density functional theory facilitated by using deep neural network potentials

Cited by in corpus (6)

• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
• Advances in modeling complex materials: The rise of neuroevolution potentials
• Navigating the Evolution of Two-dimensional Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches
• Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys
• On-the-fly machine learning-augmented constrained AIMD to design new routes from glassy carbon to quenchable amorphous diamond with low pressure and temperature
• CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations