collaborators

9 papers

physics.comp-ph2026

High-order tensor neural network for iteration-free structure relaxation

Shaobo Yu, Haoting Zhang, Yu Han +5

Structure relaxation is important for the discovery of new materials, yet conventional ab initio optimization remains a major bottleneck in high-throughput screening workflows. Mac…

cs.AI2026

LLM-Augmented Digital Twin for Policy Evaluation in Short-Video Platforms

Haoting Zhang, Yunduan Lin, Jinghai He +3

Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This feedback structure makes counterfac…

physics.comp-ph2026

Differentiable Particle-Mesh Ewald with Cartesian Tensor Message Passing for Learning Long-Range Electrostatics and Dipole Response

Zhiyue Guo, Junjie Wang, Haoting Zhang +4

Machine learning interatomic potentials (MLIPs) can approach quantum accuracy for short-range chemistry, but most architectures remain local and fail to capture the long-range elec…

cs.AI2026

Bilevel Optimization of Agent Skills via Monte Carlo Tree Search

Chenyi Huang, Haoting Zhang, Jingxu Xu +2

Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. E…

physics.comp-ph2026

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

Junjie Wang, Shuning Pan, Haoting Zhang +4

Machine learning potentials (MLPs) achieve near first-principles accuracy but often fail for atomic environments outside the training distribution. Active learning can mitigate thi…

physics.comp-ph2026

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

Haoting Zhang, Qiuhan Jia, Zhennan Zhang +6

Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenome…