144 citations · 165 across the 4 of their papers we have counts for
9 papers
Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning
Yunxing Zuo, Mingde Qin, Chi Chen +4
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equi…
Bridging the Gap Between Simulated and Experimental Ionic Conductivities in Lithium Superionic Conductors
Ji Qi, Swastika Banerjee, Yunxing Zuo +5
Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While molecular dynamics…
Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data
Chi Chen, Yunxing Zuo, Weike Ye +2
Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new pa…
Complex Strengthening Mechanisms in the NbMoTaW Multi-Principal Element Alloy
Xiang-Guo Li, Chi Chen, Hui Zheng +2
Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here…
A Performance and Cost Assessment of Machine Learning Interatomic Potentials
Yunxing Zuo, Chi Chen, Xiangguo Li +8
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the d…
Grain Boundary Properties of Elemental Metals
Hui Zheng, Xiang-Guo Li, Richard Tran +5
The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional…