44 citations · 139 across the 20 of their papers we have counts for
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Fast and stable deep-learning predictions of material properties for solid solution alloys
Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin +3
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The m…
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin +6
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grou…
A scalable constructive algorithm for the optimization of neural network architectures
Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li +1
We propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to…
Robust data-driven approach for predicting the configurational energy of high entropy alloys
Jiaxin Zhang, Xianglin Liu, Sirui Bi +3
High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially charact…