35 citations · 44 across the 15 of their papers we have counts for
15 papers
Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction
Haoting Zhang, Haoxian Chen, Jiayuan Sheng +4
Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibrating its parameters against…
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…
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…
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…
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…
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…
LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection
Haoting Zhang, Shekhar Jain
Time series anomaly detection is critical for supply chain management to take proactive operations, but faces challenges: classical unsupervised anomaly detection based on exploiti…
SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations
Haoting Zhang, Haoxian Chen, Donglin Zhan +5
The field of simulation optimization (SO) encompasses various methods developed to optimize complex, expensive-to-sample stochastic systems. Established methods include, but are no…
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…
Collaborative Bayesian Optimization via Wasserstein Barycenters
Donglin Zhan, Haoting Zhang, Rhonda Righter +2
Motivated by the growing need for black-box optimization and data privacy, we introduce a collaborative Bayesian optimization (BO) framework that addresses both of these challenges…
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data
Haoting Zhang, Donglin Zhan, Yunduan Lin +4
In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor phy…
Language Model Prompt Selection via Simulation Optimization
Haoting Zhang, Jinghai He, Rhonda Righter +1
With the advancement in generative language models, the selection of prompts has gained significant attention in recent years. A prompt is an instruction or description provided by…
E(n)-Equivariant Cartesian Tensor Passing Potential
Junjie Wang, Yong Wang, Haoting Zhang +6
Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, mes…
Multi-Facet Clustering Variational Autoencoders
Fabian Falck, Haoting Zhang, Matthew Willetts +3
Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one co…
Adaptive Transfer Learning of Multi-View Time Series Classification
Donglin Zhan, Shiyu Yi, Dongli Xu +6
Time Series Classification (TSC) has been an important and challenging task in data mining, especially on multivariate time series and multi-view time series data sets. Meanwhile,…