6 papers
DeePAW: A universal machine learning model for orbital-free ab initio calculations
Tianhao Su, Shunbo Hu, Yue Wu +5
Developing universal machine learning models for ab initio calculations is the frontier of materials cutting edge research in the new era of artificial intelligence. Here, we prese…
Epistemic Closure: Autonomous Mechanism Completion for Physically Consistent Simulation
Yue Wua, Tianhao Su, Rui Hu +4
The integration of Large Language Models (LLMs) into scientific discovery is currently hindered by the Implicit Context problem, where governing equations extracted from literature…
Automated Extraction of Mechanical Constitutive Models from Scientific Literature using Large Language Models: Applications in Cultural Heritage Conservation
Rui Hu, Yue Wu, Tianhao Su +3
The preservation of cultural heritage is increasingly transitioning towards data-driven predictive maintenance and "Digital Twin" construction. However, the mechanical constitutive…
Engineering-Oriented Symbolic Regression: LLMs as Physics Agents for Discovery of Simulation-Ready Constitutive Laws
Yue Wu, Tianhao Su, Mingchuan Zhao +2
The discovery of constitutive laws for complex materials has historically faced a dichotomy between high-fidelity data-driven approaches, which demand prohibitive full-field experi…
Skill-Based Autonomous Agents for Material Creep Database Construction
Yue Wu, Tianhao Su, Shunbo Hu +1
The advancement of data-driven materials science is currently constrained by a fundamental bottleneck: the vast majority of historical experimental data remains locked within the u…
KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns
Chenlei Xu, Tianhao Su, Jie Xiong +6
Accurate determination of crystal structures is central to materials science, underpinning the understanding of composition-structure-property relationships and the discovery of ne…