arXiv subjects

Zixiong Wei

Publications and source records attributed to Zixiong Wei.

7 recordsLinked to original sources

arXiv2026

PASS: Perturbation augmented space group structure sampling for transferable Fe-O machine learning interatomic potential

Accurate atomistic modelling of iron (Fe) oxidation requires a reliable interatomic potential, which necessitates an extensive and representative first-principles dataset for training the interatomic potential. However, Fe-oxygen (O) system is known for its structural and magnetic complexity, rendering the generation of high-quality dataset challenging. In this work, we propose the Perturbation Augmented Space group structure Sampling (PASS) method to generate extensive and representative dataset consisting of small-cell structures with less than 10 atoms. We present a systematic approach to developing a first of its kind transferable machine learning interatomic potential (MLIP) for Fe-O system based on the atomic cluster expansion (ACE) framework. We thoroughly validate the accuracy and capability of the ACE MLIP across both pure Fe and Fe-O systems through bulk, surface, and interface properties. We showcase the formation of FeO-like structure in large-scale Fe oxidation simulation using the ACE MLIP. This work demonstrates that the PASS method yields an accurate and transferable MLIP which is capable of capturing the reactive complexity of oxide growth while remaining computationally practical for extended systems.

cond-mat.mtrl-sci↗

arXiv2026

Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys

Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in large-scale molecular dynamics requires a balance among accuracy, efficiency, stability, transferability, and uncertainty quantification. Here, we benchmark two chemically scalable MLIP frameworks, neuroevolution potential (NEP) and graph atomic cluster expansion (GRACE), for 16 elemental metals and their multicomponent alloys. GRACE-FS shows higher training efficiency and generally better average accuracy, chemical transferability, and finite-temperature robustness, whereas UNEP-v1 provides substantially higher inference speed and remains competitive in selected stress and large-error metrics. We further show that chemical transferability is closely linked to high-temperature MD stability in highly multicomponent environments and that ensemble-based uncertainty provides a more reliable error indicator than D-optimality for the heterogeneous systems considered here. Finally, three-million-atom shock simulations demonstrate that UNEP-v1, combined with ensemble uncertainty, enables uncertainty-aware simulations under extreme dynamic conditions, yielding robust global spall-strength predictions while revealing model sensitivity in local damage pathways. These results provide practical guidelines for selecting and deploying MLIPs in large-scale simulations of multicomponent alloys.

cond-mat.mtrl-sci↗

arXiv2025

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) enable accurate atomistic modelling, but reliable uncertainty quantification (UQ) remains elusive. In this study, we investigate two UQ strategies, ensemble learning and D-optimality, within the atomic cluster expansion framework. It is revealed that higher model accuracy strengthens the correlation between predicted uncertainties and actual errors and improves novelty detection, with D-optimality yielding more conservative estimates. Both methods deliver well calibrated uncertainties on homogeneous training sets, yet they underpredict errors and exhibit reduced novelty sensitivity on heterogeneous datasets. To address this limitation, we introduce clustering-enhanced local D-optimality, which partitions configuration space into clusters during training and applies D-optimality within each cluster. This approach substantially improves the detection of novel atomic environments in heterogeneous datasets. Our findings clarify the roles of model fidelity and data heterogeneity in UQ performance and provide a practical route to robust active learning and adaptive sampling strategies for MLIP development.

cond-mat.mtrl-sci↗

arXiv2025

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models

Universal machine learning interatomic potentials (uMLIPs) are reshaping atomistic simulation as foundation models, delivering near \textit{ab initio} accuracy at a fraction of the cost. Yet the lack of reliable, general uncertainty quantification limits their safe, wide-scale use. Here we introduce a unified, scalable uncertainty metric \(U\) based on a heterogeneous model ensemble with reuse of pretrained uMLIPs. Across chemically and structurally diverse datasets, \(U\) shows a strong correlation with the true prediction errors and provides a robust ranking of configuration-level risk. Leveraging this metric, we propose an uncertainty-aware model distillation framework to produce system-specific potentials: for W, an accuracy comparable to full-DFT training is achieved using only \(4\%\) of the DFT labels; for MoNbTaW, no additional DFT calculations are required. Notably, by filtering numerical label noise, the distilled models can, in some cases, surpass the accuracy of the DFT reference labels. The uncertainty-aware approach offers a practical monitor of uMLIP reliability in deployment, and guides data selection and fine-tuning strategies, thereby advancing the construction and safe use of foundation models and enabling cost-efficient development of accurate, system-specific potentials.

cond-mat.mtrl-sci↗

arXiv2025

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning interatomic potentials (uMLIPs). These models offer broad applicability across the periodic table, achieving first-principles accuracy at a fraction of the computational cost of traditional DFT calculations. In this study, we demonstrate that state-of-the-art pretrained uMLIPs can effectively replace DFT for accurately modeling complex defects in a wide range of metals and alloys. Our investigation spans diverse scenarios, including grain boundaries and general defects in pure metals, defects in high-entropy alloys, hydrogen-alloy interactions, and solute-defect interactions. Remarkably, the latest EquiformerV2 models achieve DFT-level accuracy on comprehensive defect datasets, with root mean square errors (RMSE) below 5 meV/atom for energies and 100 meV/Å for forces, outperforming specialized machine learning potentials such as moment tensor potential and atomic cluster expansion. We also present a systematic analysis of accuracy versus computational cost and explore uncertainty quantification for uMLIPs. A detailed case study of tungsten (W) demonstrates that data on pure W alone is insufficient for modeling complex defects in uMLIPs, underscoring the critical importance of advanced machine learning architectures and diverse datasets, which include over 100 million structures spanning all elements. These findings establish uMLIPs as a robust alternative to DFT and a transformative tool for accelerating the discovery and design of high-performance materials.

cond-mat.mtrl-sci↗

arXiv2024

Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning

Understanding atomic hydrogen (H) diffusion in multi-principal element alloys (MPEAs) is essential for advancing clean energy technologies such as H transport, storage, and nuclear fusion applications. However, the vast compositional space and the intricate chemical environments inherent in MPEAs pose significant obstacles. In this work, we address this challenge by developing a multifaceted machine learning framework that integrates machine-learning force field, neural network-driven kinetic Monte Carlo, and machine-learning symbolic regression. This framework allows for accurate investigation of H diffusion across the entire compositional space of body-centered cubic (BCC) refractory MoNbTaW alloys, achieving density functional theory accuracy. For the first time, we discover that H diffusion in MPEAs exhibits super-Arrhenius behavior, described by the Vogel-Fulcher-Tammann model, where the Vogel temperature correlates with the 5th percentile of H solution energy spectrum. We also derive robust analytical expressions that can be used to predict H diffusivity in general BCC MPEAs. Our findings further elucidate that chemical short-range order (SRO) generally does not impact H diffusion, except it enhances diffusion when "H-favoring" elements (notably Nb and Ta) are present in low concentrations. These findings not only enhance our understanding of H diffusion dynamics in general MPEAs but also guide the development of advanced MPEAs in H-related applications by manipulating element type, composition and SRO.

cond-mat.mtrl-sci↗

arXiv2024

Machine Learning Potential Powered Insights into the Mechanical Stability of Amorphous Li-Si Alloys

Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive anode materials for solid-state Li-ion batteries but face mechanical instabilities due to significant volume variations with changing Li content. A fundamental grasp of the mechanical behavior in such systems is essential to address their poor mechanical integrity. Experimental methods offer insufficient information to elaborate on dynamic mechanical degradation mechanisms at the atomic scale, and computationally demanding first-principles methods, like DFT, struggle to access the system sizes needed for modeling mechanical phenomena. Machine learning potentials (MLPs) can overcome the computational constraints of traditional DFT-based simulations, enabling large-scale, accurate, and efficient simulations. Here, we provide a concise tutorial on developing and applying MLPs to investigate mechanical properties in materials systems, ranging from bulk to nanoparticles, using Li-Si alloys as an example. Trained on a comprehensive dataset (~45,000 DFT structures) with the aenet package accelerated by PyTorch, a robust MLP is constructed to reproduce results consistent with previous experimental observations. We demonstrate applying the MLP to realistic structures to visualize the deformation mechanism and determine the origin of mechanical instabilities caused by fracturing. This work aims to establish MLP-based simulations as a tool to understand the atomic-scale mechanical behavior in different materials systems.

cond-mat.dis-nn↗