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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired

Yaolong Zhang¹, Ce Hu¹, Bin Jiang¹

¹Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China.

The Journal of Physical Chemistry Letters

|August 10, 2019

Summary

We introduce the embedded atom neural network (EANN), a machine learning model for accurate potential energy surfaces. EANN offers high efficiency and accuracy for molecular dynamics and spectroscopic simulations.

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

The empirical embedded atom method (EAM) is widely used for condensed phase simulations.
Developing accurate potential energy surfaces is crucial for molecular simulations.

Purpose of the Study:

To develop a simple, efficient, and accurate machine learning model for high-dimensional potential energy surfaces.
To introduce the embedded atom neural network (EANN) approach.

Main Methods:

Replaced the scalar embedded atom density in EAM with a Gaussian-type orbital based density vector.
Utilized neural networks to represent the relationship between the embedded density vector and atomic energy.

Main Results:

EANN demonstrated accuracy comparable to established machine learning models for molecular and periodic systems.
EANN requires significantly fewer parameters and configurations than other models.
The model implicitly captures three-body information, avoiding costly angular descriptors.

Conclusions:

EANN provides a highly accurate and efficient method for constructing potential energy surfaces.
EANN potentials can accelerate ab initio molecular dynamics and spectroscopic simulations in complex systems.