English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We propose a simple, but efficient and accurate machine learning (ML) modelfor developing high-dimensional potential energy surface. This so-calledembedded atom neural network (EANN) approach is inspired by the well-knownempirical embedded atom method (EAM) model used in condensed phase. It simplyreplaces the scalar embedded atom density in EAM with a Gaussian-type orbitalbased density vector, and represents the complex relationship between theembedded density vector and atomic energy by neural networks. We demonstratethat the EANN approach is equally accurate as several established ML models inrepresenting both big molecular and extended periodic systems, yet with muchfewer parameters and configurations. It is highly efficient as it implicitlycontains the three-body information without an explicit sum of the conventionalcostly angular descriptors. With high accuracy and efficiency, EANN potentialscan vastly accelerate molecular dynamics and spectroscopic simulations incomplex systems at ab initio level.

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