To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

In studying solidification process by simulations on the atomic scale, the modeling of crystal
nucleation or amorphization requires the construction of interatomic interactions that are …

Using artificial neural-network machine learning (ANN-ML) to generate interatomic
potentials has been demonstrated to be a promising approach to address the longstanding …

The modular assembly of organic molecules commonly guides the design of metal–organic
frameworks (MOFs), yet the systematic exploration of inorganic building blocks remains …

Main observation and conclusion As a common electrocatalytic system, Au‐Pt alloy particles
are often prepared as Au‐core‐Pt‐shell (Au@ Pt) to make full use of platinum. However, Au …

In the present study, a suitable machine learning potential for SiBCNZr amorphous materials
was used in classical molecular dynamics (CMD) for creating the large-scale SiBCNZr …

Determining surface structures is a substantial challenge due to the limitations of
experimental techniques and the complexity of theoretical models. In this work, we use a …

The thermodynamic stability and growth kinetics of Pt-Sn phases are investigated by
atomistic simulations utilizing a neural-network machine learning (NN-ML) interatomic …

Abstract Shape Memory Ceramics (SMCs) are an emerging class of materials featuring
several properties of interest. First among these is the shape memory effect itself, enabling …