PhoenixD Team veröffentlicht Arbeit zu Multiskalenmodellierung

PhoenixD researchers establish first framework of first-principles multiscale modelling

© Zhuang Xioaying/PhoenixD
DFT - MLIP - CMD – FEM are the acronyms of Density Functional Theory – Machine Learning based Interatomistic Potential – Classic Molecular Dynamics – Finite Element Method.

In PhoenixD, our researchers from the Institute of Photonics, lead by Prof. Xiaoying Zhuang and Dr. Bohayra Mortazavi, are exploring the multiscale modelling and machine learning approach for finding, characterizing and designing new materials. We have recently established the first framework of first-principles multiscale modelling, especially with machine learning, which has been accepted in the journal "Advanced Materials“ (DOI: 10.1002/adma.202102807).

Previously, our preliminary works for 2D materials has explored promising photocatalysis in the two-dimensional MoSi2N4 family (Nano Energy, 82, 105716). This work proposes the robust concept of first-principles multiscale modelling of mechanical properties based on machine-learning interatomic potentials, conveniently and rapidly trainable over short ab-initio datasets.

We show that mechanical/failure responses of complex nanostructures at continuum scale can now be explored with the precision of sophisticated first-principles calculations, affordable computational cost, and without the need for empirical data. Such an approach shows great potential to develop fully automated and coupled platforms, design, optimize, and explore various properties of materials at continuum level, considering atomistic effects and inherent precision of first-principles calculations.