3 papers
physics.comp-ph2026
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…
cs.LG2026
PCL-Reasoner-V1.5: Advancing Math Reasoning with Offline Reinforcement Learning
Yao Lu, Dengdong Fan, Jianzheng Nie +4
We present PCL-Reasoner-V1.5, a 32-billion-parameter large language model (LLM) for mathematical reasoning. The model is built upon Qwen2.5-32B and refined via supervised fine-tuni…
cond-mat.mtrl-sci2025
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…