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20172022
most citedAccurate Force Field for Molybdenum by Machine Learning Large Materials Data

165 citations · 193 across the 8 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cond-mat.mtrl-sci2019

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…

cond-mat.mtrl-sci20192 cited

Random Forest Models for Accurate Identification of Coordination Environments from X-ray Absorption Near-Edge Structure

Chen Zheng, Chi Chen, Yiming Chen +1

Analyzing coordination environments using X-ray absorption spectroscopy has broad applications ranging from solid-state physics to material chemistry. Here, we show that random for…

cond-mat.mtrl-sci2019

Unified Theory of Thermal Quenching in Inorganic Phosphors

Mahdi Amachraa, Zhenbin Wang, Hanmei Tang +4

We unify two prevailing theories of thermal quenching (TQ) in rare-earth-activated inorganic phosphors - the cross-over and auto-ionization mechanisms - into a single predictive mo…

physics.comp-ph2019

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…

cond-mat.mtrl-sci20192 cited

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…

cond-mat.mtrl-sci20192 cited

An Electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Lithium Nitride

Zhi Deng, Chi Chen, Xiang-Guo Li +1

Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms o…