arXiv subjects

Xianglin Liu

Publications and source records attributed to Xianglin Liu.

13 recordsLinked to original sources

arXiv2026

Ascend to Science: Exploration of AI Chips for Scientific Computing

The rapid rise of AI-oriented accelerators has reshaped compute systems around low-precision tensor engines, raising a practical question for the HPC community: under what conditions can such hardware support scientific workloads that demand numerical robustness, irregular memory access, and scalability? Using the Ascend 910 NPU series as a representative tensor-centric platform, we characterize precision, execution, and memory-hierarchy bottlenecks that hinder the direct deployment of scientific codes. We then develop and evaluate workload-specific mappings across five application studies -- HPL-MxP, LRSVD, SGEMM-cube, PQSim, and SMC-X -- combining heterogeneous execution, mixed-precision numerical formulations, precision emulation, hierarchical memory orchestration, and communication--computation overlap. These studies show that AI-native NPUs can achieve numerical robustness, competitive performance, and satisfactory scalability when numerical formulation, execution placement, and data movement are addressed in a coordinated manner. Our results provide a state-of-the-practice case study of how scientific workloads can be adapted to tensor-centric architectures, while distinguishing transferable optimization principles from Ascend-specific implementation details.

cs.DC↗

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↗

arXiv2022

Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these mechanisms to design new HEAs are confronted with their high-dimensional chemical complexity, which presents unique challenges to (i) the theoretical modeling that needs accurate atomic interactions for atomistic simulations and (ii) constructing reliable macro-scale models for high-throughput screening of vast amounts of candidate alloys. Machine learning (ML) sheds light on these problems with its capability to represent extremely complex relations. This review highlights the success and promising future of utilizing ML to overcome these challenges. We first introduce the basics of ML algorithms and application scenarios. We then summarize the state-of-the-art ML models describing atomic interactions and atomistic simulations of thermodynamic and mechanical properties. Special attention is paid to phase predictions, planar-defect calculations, and plastic deformation simulations. Next, we review ML models for macro-scale properties, such as lattice structures, phase formations, and mechanical properties. Examples of machine-learned phase-formation rules and order parameters are used to illustrate the workflow. Finally, we discuss the remaining challenges and present an outlook of research directions, including uncertainty quantification and ML-guided inverse materials design.

cond-mat.mtrl-sci↗

arXiv2020

Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach

High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is important to investigate the thermodynamics and elucidate the evolution of various chemical phases. In this work, we introduce a data-driven approach to construct the effective Hamiltonian and study the thermodynamics of HEAs through canonical Monte Carlo simulation. The main characteristic of our method is to use pairwise interactions between atoms as features and systematically improve the representativeness of the dataset using samples from Monte Carlo simulation. We find this method produces highly robust and accurate effective Hamiltonians that give less than 0.1 mRy test error for all the three refractory HEAs: MoNbTaW, MoNbTaVW, and MoNbTaTiW. Using replica exchange to speed up the MC simulation, we calculated the specific heats and short-range order parameters in a wide range of temperatures. For all the studied materials, we find there are two major order-disorder transitions occurring respectively at $T_1$ and $T_2$, where $T_1$ is near room temperature but $T_2$ is much higher. We further demonstrate that the transition at $T_1$ is caused by W and Nb while the one at $T_2$ is caused by the other elements. By comparing with experiments, {\color{black} the results provide insight into the role of chemical ordering in the strength and ductility of HEAs.

cond-mat.mtrl-sci↗

arXiv2019

Machine Learning the Effective Hamiltonian in High Entropy Alloys

The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effective Hamiltonian requires sufficiently large data sets, which is expensive to calculate with conventional density functional theory (DFT). To solve this problem, we propose to use the atomic local energy as the target variable, and harness the power of the linear-scaling DFT to accelerate the data generating process. Using the large amounts of DFT data sets, various complex models are devised and applied to learn the effective Hamiltonians of a range of refractory high entropy alloys (HEAs). The testing $R^2$ scores of the effective pair interaction model are higher than 0.99, demonstrating that the pair interactions within the 6-th coordination shell provide an excellent description of the atomic local energies for all the four HEAs. This model is further improved by including nonlinear and multi-site interactions. In particular, the deep neural networks (DNNs) built directly in the local configuration space (therefore no hand-crafted features) are employed to model the effective Hamiltonian. The results demonstrate that neural networks are promising for the modeling of effective Hamiltonian due to its excellent representation power.

cond-mat.mtrl-sci↗

arXiv2019

Robust data-driven approach for predicting the configurational energy of high entropy alloys

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys. The proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. Given limited data calculated by first-principles calculations, Bayesian regularized regression not only offers an accurate and stable prediction but also effectively quantifies the uncertainties associated with EPI parameters. Compared with the arbitrary determination of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence we can place in statistical estimates of configurational energy when data are sparse.

cond-mat.mtrl-sci↗

arXiv2019

Chemical complexity in high entropy alloys: A pair-interaction perspective

The recently proposed pair-interaction model is applied to study a series of refractory high entropy alloys. The results demonstrate the simplicity, robustness, and high accuracy of this model in predicting the configuration energies of NbMoTaW, NbMoTaWV and NbMoTaWTi. The element-element pair interaction parameters obtained from the linear regression of first-principle data provide a new perspective to understand the strengthening mechanism in HEAs, as revealed by comparing the effects of adding vanadium and titanium. Using the pair-interaction model, an expression for the intrinsic energy fluctuation is derived, which provides guidance on both theoretical modeling and first principles calculation.

cond-mat.mtrl-sci↗

arXiv2019

Machine learning modeling of high entropy alloy: the role of short-range order

The development of machine learning sheds new light on the traditionally complicated problem of thermodynamics in multicomponent alloys. Successful application of such a method, however, strongly depends on the quality of the data and model. Here we propose a scheme to improve the representativeness of the data by utilizing the short-range order (SRO) parameters to survey the configuration space. Using the improved data, a pair interaction model is trained for the NbMoTaW high entropy alloy using linear regression. Benefiting from the physics incorporated into the model, the learned effective Hamiltonian demonstrates excellent predictability over the whole configuration space. By including pair interactions within the 6th nearest-neighbor shell, this model achieves an $R^2$ testing score of 0.997 and root mean square error of 0.43 meV. We further perform a detailed analysis on the effects of training data, testing data, and model parameters. The results reveal the vital importance of representative data and physical model. On the other hand, we also examined the performance neural networks, which is found to demonstrate a strong tendency to overfit the data.

cond-mat.mtrl-sci↗

arXiv2017

Relativistic full-potential multiple scattering theory: An ab initio method and its applications

The Green function plays an essential role in the Kohn-Korringa-Rostocker (KKR) multiple scattering method. In practice, it is constructed from the regular and irregular solutions of the local Kohn-Sham equation and robust methods exist for spherical potentials. However, when applied to a non-spherical potential, numerical errors from the irregular solutions give rise to pathological behaviors of the charge density at small radius. Here we present a full-potential implementation of the relativistic KKR method to perform \textit{ab initio} self-consistent calculation by directly solving the Dirac differential equations. The pathology around the origin is completely eliminated with the help of an efficient pole-searching technique. This method is utilized to investigate the crystal structures of polonium and their bulk properties. The noble metals are also calculated, both as a test of our method and to study the relativistic effects.

cond-mat.mtrl-sci↗

arXiv2016

A full-potential approach to the relativistic single-site Green's function

One major purpose of studying the single-site scattering problem is to obtain the scattering matrices and differential equation solutions indispensable to multiple scattering theory (MST) calculations. On the other hand, the single-site scattering itself is also appealing because it reveals the physical environment experienced by electrons around the scattering center. In this paper we demonstrate a new formalism to calculate the relativistic full-potential single-site Green's function. We implement this method to calculate the single-site density of states and electron charge densities. The code is rigorously tested and with the help of Krein's theorem, the relativistic effects and full potential effects in group V elements and noble metals are thoroughly investigated.

cond-mat.mtrl-sci↗