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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Molecular dynamics simulations have been extensively used to predict thermalproperties, but simulating different phases with similar precision using aunified force field is often difficult, due to the lack of accurate andtransferrable interatomistic potential fields. As a result, this issue hasbecome a major barrier to predicting the phase change of materials and theirtransport properties with atomistic-level modeling techniques. Recently,machine learning based algorithms have emerged as promising tools to developaccurate potentials for molecular dynamics simulations. In this work, weapproach the problem of predicting the thermal conductivity of silicon indifferent phases by performing molecular dynamics simulations with a deepneural network potential. This neural network potential is trained withab-initio data of silicon in the crystalline, liquid and amorphous phases. Theaccuracy of our potential is first validated through reproducing the atomisticstructures during the phase transition, where other empirical potentialsusually fail. The thermal conductivity of different phases is then calculated,showing a good agreement with the experimental results and ab-initiocalculation results. Our work shows that a unified neural network-basedpotential can be a promising tool for studying phase change and thermaltransport of materials with high accuracy.

A Unified Deep Neural Network Potential Capable of Predicting Thermal Conductivity of Silicon in Different Phases