4 papers
SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators
Xianglin Liu, Kai Yang, Fanli Zhou +9
The rapid advancement of deep learning is reshaping the hardware design landscape toward AI tasks, posing fundamental challenges for HPC workloads such as atomistic simulation. Her…
Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation
Fanli Zhou, Hao Chen, Pengxiang Xu +3
Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-drive…
SMC-X: A Distributed Scalable Monte Carlo Simulation Method for Chemically Complex Alloys
Xianglin Liu, Kai Yang, Fanli Zhou +1
To predict the complex chemical evolution in multicomponent alloys, it is highly desirable to have accurate atomistic simulation methods capable of reaching sufficiently large spat…
Revealing Nanostructures in High-Entropy Alloys via Machine-Learning Accelerated Scalable Monte Carlo Simulation
Xianglin Liu, Kai Yang, Yongxiang Liu +5
The computational cost of traditional first-principles method quickly becomes prohibitively expensive as the number of atoms increases. This challenge is further amplified by the n…