48 citations · 159 across the 6 of their papers we have counts for
7 papers
Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Yunzhe Jia, Fankai Xie, Yunfei Bai +3
Nonadiabatic coupled electron-nuclear dynamics upon electronic excitation underpin the microscopic mechanism and rational modulation of diverse photoinduced functional phenomena in…
Graph Neural Network Force Fields (GPTFF-mol) for Organic Molecules from Optimization Trajectories (OpenGEM26)
Yifan Huang, Fankai Xie, Jiangnan Zheng +3
Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accurac…
GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials
Fankai Xie, Tenglong Lu, Sheng Meng +1
This study introduces a novel AI force field, namely graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and…
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…
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…
Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning
Zhendong Cao, Guanghui Cai, Fankai Xie +7
DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes e…