3 citations · 8 across the 5 of their papers we have counts for
5 papers
Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
Tsz Wai Ko, Shyue Ping Ong
Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces…
Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling
Ji Qi, Tsz Wai Ko, Brandon C. Wood +2
Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computatio…
Machine Learning Moment Tensor Potential for Modelling Dislocation and Fracture in L1-TiAl and D0-TiAl Alloys
Ji Qi, Z. H. Aitken, Qingxiang Pei +7
Dual-phase -TiAl and -TiAl alloys exhibit high strength and creep resistance at high temperatures. However, they suffer from low tensile ductility and fracture toughn…
The Intercalation Chemistry of the Disordered RockSalt Li3V2O5 Anode from Cluster Expansions and Machine Learning Interatomic Potentials
Xingyu Guo, Chi Chen, Shyue Ping Ong
Disordered rocksalt (DRX) Li3V2O5 is a promising candidate for anode in rechargeable lithium-ion batteries because of its ideal low voltage, high rate capability, and superior cycl…
Synthetic control of structure and conduction properties in Na-Y-Zr-Cl solid electrolytes
Elias Sebti, Ji Qi, Peter M. Richardson +9
In the development of low cost, sustainable, and energy-dense batteries, chloride-based compounds are promising catholyte materials for solid-state batteries owing to their high Na…