Resources

This page includes some useful code or data that we have used.

Jump to: molecular dynamics, Machine learning

Molecular dynamics

Interatomic potentials

  1. To model multiprincipal elemental alloys using molecular dynamics, we need to know their interatomic interactions. In our previous work, we have parameterized the EAM potential for V based on the formalisms proposed by Zhou et al.[Phys. Rev. B. 69 144113 (2004)]. Combine with available parameters, we are able to simulate multicomponent alloys composed of different metals, including Fe, V, Cr, Mo, Ta, W. The complete potential file that we have used is provided here, or if it does not work, please go to our github site.

Machine learning

  1. We have developed Machine learning models based on ANN and SVM to predict the single-phase formation ability of different high-entropy carbide ceramics. The code is available on github from Ph.D. student Zhang Jun.

  2. We have developed an Atomic Graph Attention Network (AGAT) Machine learning model to predict energy and force of different configurations with high accuracies. The code is available on github from Ph.D. student Zhang Jun.