Jianpeng Chen
2026
PhyVer: Physics-Grounded Material Claim Verification with Multi-Fidelity Physical Evidence
Jianpeng Chen | Wangzhi Zhan | Haohui Wang | Brian Mayer | Dongqi Fu | Dawei Zhou
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Jianpeng Chen | Wangzhi Zhan | Haohui Wang | Brian Mayer | Dongqi Fu | Dawei Zhou
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Material claims in papers, patents, etc., often involve physical feasibility (e.g., stability under conditions, property consistency), not just textual feasibility. Yet most claim verifiers operate over language, therefore producing ungrounded judgments. On the other hand, directfirst-principles verification (e.g., density functional theory, DFT) is inflexible and hard to invoke from underspecified free-form claims.Therefore, we introduce **PhyVer**, a **phy**sics-grounded material claim **ver**ification system that bridges this gap by translating claimsinto multi-fidelity physical evidence and interpretable verdicts. To support human-in-the-loop inspection, we present an interactive web interface that visualizes the instantiated structure, optimization trajectories, DFT summaries, and the final decision. On expert-labeled claims, **PhyVer** improves agreement with experts over text-only GPT-5.1, reducing MAE from 1.54 to 1.20 and Signed MAE from0.95 to 0.82, and increasing Accuracy@±1 from 50% to 70%.
2025
MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
Jingyuan Qi | Zian Jia | Minqian Liu | Wangzhi Zhan | Junkai Zhang | Xiaofei Wen | Jingru Gan | Jianpeng Chen | Qin Liu | Mingyu Derek Ma | Bangzheng Li | Haohui Wang | Adithya Kulkarni | Muhao Chen | Dawei Zhou | Ling Li | Wei Wang | Lifu Huang
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
Jingyuan Qi | Zian Jia | Minqian Liu | Wangzhi Zhan | Junkai Zhang | Xiaofei Wen | Jingru Gan | Jianpeng Chen | Qin Liu | Mingyu Derek Ma | Bangzheng Li | Haohui Wang | Adithya Kulkarni | Muhao Chen | Dawei Zhou | Ling Li | Wei Wang | Lifu Huang
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert oversight with two primary phases: (1) hypothesis generation, where the system performs complex reasoning to generate novel and scientifically sound hypotheses, supported with domain-specific foundation models and inductive biases retrieved from existing literature; (2) 3D structure synthesis, where a 3D structure is synthesized with a novel 3D diffusion model based on the textual hypothesis and refined it with a LLM-based refinement model to achieve better structure properties. At each phase, domain experts iteratively validate the system outputs, and provide feedback and supplementary materials to ensure the alignment of the outputs with scientific principles and human preferences. Through extensive evaluation from human scientists, MetaScientist is able to deliver novel and valid mechanical metamaterial designs that have the potential to be highly impactful in the metamaterial field.