52 citations · 66 across the 4 of their papers we have counts for
5 papers
Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm
Ling Tang, Weiyi Xia, Tyler J. Slade +2
Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In t…
Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
Ling Tang, Weiyi Xia, Gayatri Viswanathan +3
While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials h…
Structure and dynamics of Fe90Si3O7 liquids close to Earth's liquid core conditions
Ling Tang, Chao Zhang, Yang Sun +3
Using an artificial neural-network machine learning interatomic potential, we have performed molecular dynamics simulations to study the structure and dynamics of Fe90Si3O7 liquid…
Development of Interatomic Potential for Al-Tb Alloy by Deep Neural Network Learning Method
L. Tang, Z. J. Yang, T. Q. Wen +3
An interatomic potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep neural network (DNN) learning method. The atomic configurations and the cor…
Effects of Si solute on the glass formation and atomic structure of Pd liquid
Z. J. Yang, L. Tang, T. Q. Wen +2
Molecular dynamics simulations were performed to study the effects of Si solute on the glass formation and crystallization of Pd liquid. Pure Pd sample prepared by quenching proces…