most citedLu-H-N phase diagram from first-principles calculations

48 citations · 87 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci20241 cited

Recent Breakthrough in AI-Driven Materials Science: Tech Giants Introduce Groundbreaking Models

Miao Liu, Sheng Meng

A close look of Google's GNoME inorganic materials dataset [Nature 624, 80 (2023)], and 11 things you would like to know.

cs.LG20231 cited

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration

Shuai Zhang, Hongkang Li, Meng Wang +6

This paper provides a theoretical understanding of Deep Q-Network (DQN) with the -greedy exploration in deep reinforcement learning. Despite the tremendous empirical a…

cond-mat.mtrl-sci202337 cited

MatChat: A Large Language Model and Application Service Platform for Materials Science

Ziyi Chen, Fankai Xie, Meng Wan +5

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehen…

cond-mat.supr-con202348 cited

Lu-H-N phase diagram from first-principles calculations

Fankai Xie, Tenglong Lu, Ze Yu +4

Using a comprehensive structure search and high-throughput first-principles calculations of 1483 compounds, this study presents the phase diagram of Lu-H-N. The formation energy la…

cs.LG2023

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

Shuai Zhang, Meng Wang, Pin-Yu Chen +3

Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and sto…