activity
20172024
most citedAccurate Force Field for Molybdenum by Machine Learning Large Materials Data

165 citations · 350 across the 12 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

17 papers · 1 filter

cond-mat.mtrl-sci202113 cited

Proton distribution visualization in perovskite nickelate devices utilizing nanofocused X-rays

Ivan A. Zaluzhnyy, Peter O. Sprau, Richard Tran +17

We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO-based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-f…

cond-mat.mtrl-sci2021

Emergence of near-boundary segregation zones in face-centered cubic multi-principal element alloys

Megan J. McCarthy, Hui Zheng, Diran Apelian +6

Grain boundaries have been shown to dramatically influence the behavior of relatively simple materials such as monatomic metals and binary alloys. The increased chemical complexity…

cond-mat.mtrl-sci202117 cited

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…

cond-mat.mtrl-sci2021

AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets

Chi Chen, Shyue Ping Ong

Predicting materials properties from composition or structure is of great interest to the materials science community. Deep learning has recently garnered considerable interest in…

cond-mat.mtrl-sci2021

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…

cond-mat.mtrl-sci2020

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…