Portrait de Huan Zhang

Huan Zhang

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Bang Liu
Sujets de recherche
Interaction humain-machine (IHM)
Modèles génératifs
Traitement du langage naturel
Site web
Google Scholar

Publications

Sparsity-Aware Evolution for Model Merging
Huan Zhang
Yanjian Zhang
Nadi Tomeh
Guillaume Wisniewski
Bang Liu
We propose a sparsity-aware evolutionary (SAE) framework for model merging that involves iterative pruning-merging cycles to act as a novel … (voir plus)mutation operator. We incorporate the sparsity constraints into the score function, which steers the evolutionary process to favor more sparse models, in addition to other conventional performance scores. Interestingly, the by-product of \textit{competition} for sparsity introduces an extra local \textit{attraction} and interplay into the evolutionary process: if one competitor has more zero elements, the other competitor's non-zero elements will occupy those positions, even though the less sparse competitor loses to the more sparse competitor in other positions. The proposed pipeline is evaluated on a variety of large-scale LLM benchmarks. Experiments demonstrate that our approach can improve model merging reliability across multiple benchmarks, and is easy to incorporate due to its simplicity and being orthogonal to most existing approaches.
2026-02-08
arXiv (prépublication)
doi.org
arxiv.org
Towards Agentic Intelligence for Materials Science
Huan Zhang
Yizhan Li
Wenhao Huang
Ziyu Hou
Yu Song
Xuye Liu
Farshid Effaty
Jinya Jiang
Sifan Wu
Qianggang Ding
Izumi Takahara
Leonard R. MacGillivray
Teruyasu Mizoguchi
Tianshu Yu
Lizi Liao
Yuyu Luo
Yu Rong
Jia LI
Ying Diao
Heng Ji … (voir 1 de plus)
Bang Liu
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in d… (voir plus)iscovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.
2025-12-31
arXiv (prépublication)
doi.org
arxiv.org
HoneyComb: A Flexible LLM-Based Agent System for Materials Science
Huan Zhang
Yu Song
Ziyu Hou
Santiago Miret
Bang Liu
The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks in materials science. Many LLMs, how… (voir plus)ever, often struggle with the distinct complexities of materials science tasks, such as computational challenges, and rely heavily on outdated implicit knowledge, leading to inaccuracies and hallucinations. To address these challenges, we introduce HoneyComb, the first LLM-based agent system specifically designed for materials science. HoneyComb leverages a reliable, high-quality materials science knowledge base (MatSciKB) and a sophisticated tool hub (ToolHub) tailored specifically for materials science to enhance its reasoning and computational capabilities. MatSciKB is a curated, structured knowledge collection based on reliable literature, while ToolHub employs an Inductive Tool Construction method to generate, decompose, and refine API tools for materials science. Additionally, HoneyComb leverages a retriever module that adaptively selects the appropriate knowledge source or tools for specific tasks, thereby ensuring accuracy and relevance. Our results demonstrate that HoneyComb significantly outperforms baseline models across various tasks in materials science, effectively bridging the gap between current LLM capabilities and the specialized needs of this domain. Furthermore, our adaptable framework can be easily extended to other scientific domains, highlighting its potential for broad applicability in advancing scientific research and applications.
2024-10-06
NeurIPS.cc/2024/Workshop/AI4Mat (spotlight)
openreview.net
openreview.net
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
Yu Song
Santiago Miret
Huan Zhang
Bang Liu
2023-10-06
EMNLP/2023/Conference (accepté)
openreview.net
openreview.net