arXiv subjects

Qiuhan Jia

Publications and source records attributed to Qiuhan Jia.

5 recordsLinked to original sources

arXiv2026

NEPMaker: Active learning of neuroevolution machine learning potential for large cells

Machine learning potentials (MLPs) achieve near first-principles accuracy but often fail for atomic environments outside the training distribution. Active learning can mitigate this limitation; however, its application to large-scale simulations is hindered by the prohibitive cost of labeling entire configurations. Here, we develop a D-optimality-driven active learning framework for the neuroevolution potential (NEP) implemented within the GPUMD package, named NEPMaker. Extrapolative atomic environments are identified on-the-fly and embedded into locally periodic structures, where boundary atoms are optimized to remain close to the training distribution. This strategy enables large-scale simulations to directly contribute to dataset construction, significantly reducing extrapolation errors while improving model robustness and transferability. The proposed framework provides a scalable route for constructing reliable machine learning potentials in complex materials systems, including those involving defects, interfaces, and phase transitions.

physics.comp-ph↗

arXiv2026

GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials

Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenomena. Both machine learning potentials and enhanced sampling approaches offer substantial improvements in high-accuracy simulation efficiency, which can be further boosted through GPU acceleration. However, an efficient framework combining these advances for extending simulations to large systems and long timescales remains elusive. In this work, we proposed a full-life-cycle GPU accelerated metadynamics simulations package GPU-MetaD. Benchmarking across molecular, interface, and bulk systems demonstrates that GPU-MetaD efficiently handles diverse atomic systems and delivers an order-of-magnitude performance improvement. Building on this demonstrated capability, it enables ab-initio-level rare-event sampling for systems comprising millions of atoms on a typical single GPU. This capability allows us to reveal a previously unknown size-dependent two-step nucleation mechanism in gallium nitride (GaN), highlighting the potential of GPU-MetaD for uncovering complex rare events in realistic large-scale materials systems.

physics.comp-ph↗

arXiv2025

Scalable cell filter nudged elastic band (CFNEB) for large-scale transition-path calculations

The nudged elastic band (NEB) method is one of the most widely used techniques for determining minimum-energy reaction pathways and activation barriers between known initial and final states. However, conventional implementations face steep computational scaling with system size, which makes nucleation-type transitions in realistically large supercells practically inaccessible. In this work, we develop a scalable cell-filter nudged elastic band (CFNEB) framework that enables efficient transition-path calculations in systems containing up to $10^5$ atoms. The method combines a deformation-based cell filtering scheme, which treats lattice vectors as generalized coordinates while removing spurious rotational degrees of freedom, with an adaptive image insertion and deletion strategy that dynamically refines the reaction path. We implement CFNEB both within the ASE environment and in a fully GPU-accelerated version using the Graphics Processing Units Molecular Dynamics (GPUMD) engine, achieving throughput on the order of $10^6$ atom$\cdot$steps per second on consumer GPUs. We demonstrate the method on two representative systems: the layer-by-layer $β$-$λ$ transition in $Ti_3O_5$ and the nucleation-driven graphite-to-diamond transformation. These examples illustrate that CFNEB not only reproduces known concerted pathways but also captures spontaneous symmetry breaking toward nucleated mechanisms when the simulation cell is sufficiently large. Our results establish CFNEB as a practical route to exploring realistic transition mechanisms in large-scale solid-state systems.

physics.comp-ph↗

arXiv2022

Prediction of surface reconstructions using MAGUS

In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher (MAGUS). In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common. To validate this newly developed module, we tested it with studies on the surface reconstructions of Si (100), Si (111) and 4H-SiC(1-102)-c(2x2), respectively. We successfully gave the known ground states as well as a new SiC surface model in an extremely Si-rich environment.

cond-mat.mtrl-sci↗

arXiv2020

On the nonlinearity of a tuning fork

Tuning fork experiments at the undergraduate level usually only demonstrate a tuning fork's linear resonance. In this paper, we introduce an experiment that can be used to measure the nonlinear tuning curve of a regular tuning fork. Using double-grating Doppler interferometry, we achieve measurement accuracy within ten microns. With this experiment setup, we observe typical nonlinear behaviors of the tuning fork such as the softening tuning curve and jump phenomena. Our experiment is inexpensive and easy to operate. It provides an integrated experiment for intermediate-level students and a basis for senior research projects.

physics.app-ph↗