arXiv subjects

Fanli Zhou

Publications and source records attributed to Fanli Zhou.

4 recordsLinked to original sources

arXiv2026

SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators

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. Here we present SMC-AI, a general algorithmic framework that extends the SMC-X method for efficient canonical Monte Carlo simulation on AI accelerators, including GPUs and NPUs, while maintaining extreme scalability. The implementation of SMC-AI on an NPU cluster reaches unprecedented performance, achieving MC simulation of 4 trillion atoms on 4096 NPU dies. This represents the largest ML-accelerated atomistic simulation reported, delivering 32X system size and 1.3X throughput than previous records, with a relatively small computational budget. Excellent strong and weak scaling efficiency are reached for both the NPU and GPU implementation. By decoupling ML models from simulation, SMC-AI creates an abstraction that facilitates integration and porting of diverse ML models, laying a foundation for the future development of scalable scientific software.

physics.comp-ph↗

arXiv2026

Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation

Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder complexity demands atomistic simulations that achieve high accuracy, efficiency, and generalizability across vast spatial, temporal, and especially chemical scales. While machine learning (ML) interatomic potentials have transformed molecular dynamics, they remain limited in capturing diffusion-driven chemical evolution over long timescales. The recently introduced SMC-X method brings exciting opportunities. Realizing its full potential requires a comprehensive study, which is the focus of this work. To assess model performance, we systematically benchmark invariant and equivariant architectures using a density functional theory dataset of more than 10,000 configurations spanning seven elements: Fe, Co, Ni, Al, Ti, Ta, and V. To understand the roles of pairwise and higher-order interactions, we decouple their contributions across chemical space using an explainable machine learning approach. We also examine the impact of lattice relaxation by comparing models trained on datasets with and without structural relaxation. Our results clarify how to choose ML surrogate models for Monte Carlo simulations, bridge the gap between theory and experiment, and lay a foundation for establishing ML-accelerated Monte Carlo as a computational microscope for chemical complexity.

cond-mat.mtrl-sci↗

arXiv2025

SMC-X: A Distributed Scalable Monte Carlo Simulation Method for Chemically Complex Alloys

To predict the complex chemical evolution in multicomponent alloys, it is highly desirable to have accurate atomistic simulation methods capable of reaching sufficiently large spatial and temporal scales. In this work, we advance the recently proposed SMC-X method through distributed computation on either GPUs or CPUs, pushing both spatial and temporal scales of atomistic simulation of chemically complex alloys to previously inaccessible scales. This includes a record-breaking 128-billion-atom HEA system extending to the micrometer regime in space, and a 1-billion-atom HEA evolved over more than three million Monte Carlo swap steps, approaching the minute regime in time. We show that such large-scale simulations are essential for bridging the gap between experimental observations and theoretical predictions of the nanoprecipitate sizes in HEAs, based on analysis using the Lifshitz-Slyozov-Wagner (LSW) theory for diffusion-controlled coarsening. This work demonstrates the great potential of SMC-X for simulation-driven exploration of the chemical complexity in high-entropy materials at large spatial and temporal scales.

cond-mat.mtrl-sci↗

arXiv2025

Revealing Nanostructures in High-Entropy Alloys via Machine-Learning Accelerated Scalable Monte Carlo Simulation

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 need to evaluate finite-temperature properties with Monte Carlo (MC) simulations, which is inherently challenging to parallelize due to sequential Markov chain updates. Here, we introduce Scalable Monte Carlo (SMC), an efficient MC simulation method that overcomes the parallelization bottlenecks in conventional MC simulation, reducing the computational complexity of a MC sweep from quadratic to linear. We present a GPU implementation of the SMC method, SMC-GPU, which simultaneously harnesses the thousands of processing cores on a GPU to accelerate the computation. By adopting a data-driven workflow that surrogates the computationally expensive density functional theory (DFT) with ML models, we demonstrate that SMC-GPU is capable of simulating systems of more than one-billion atoms, while maintaining the accuracy of first-principles methods. Using this unprecedented capability, we performed billion-atom MC simulations to investigate the nanostructure evolution of two important high-entropy alloys (HEAs), FeCoNiAlTi and MoNbTaW, in which the nanostructures are believed to be responsible for their superb mechanical properties. Our results reveal a rich diversity of nanostructures, including nanoparticles (NP), 3D-connected NP, and disorder protected nanophases. We quantitatively analyze the size, composition, and morphology of the nanostructures, as well as directly simulate the atom-probe-tomography (APT) needle. The results align well with available experimental observations. This work underscores the promising potential of leveraging large-scale MC simulation to explore the largely uncharted territory of nanostructure evolution in HEAs.

cond-mat.mtrl-sci↗