量子磁体中不确定性感知哈密顿量推断与实验设计的观测几何
Observation geometry for uncertainty-aware Hamiltonian inference and experimental design in quantum magnets
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中文总结 AI 辅助
该研究提出AI赋能的不确定性感知哈密顿量推断与自适应实验设计框架，结合哈密顿量条件神经代理等方法，通过NiPS₃的中子散射测量验证，为量子材料表征与实验设计提供通用策略。
中文摘要 AI 辅助
从光谱与散射测量确定微观相互作用是理解量子材料的核心，但现有实验数据可可靠揭示哪些相互作用、应设计何种额外实验模态解决剩余模糊性，仍常不明确。本文提出一种人工智能赋能的不确定性感知哈密顿量推断与自适应实验设计框架，将哈密顿量条件神经代理与贝叶斯推断、观测几何结合，该框架可表征测量对哈密顿量参数空间的约束方式、量化微观相互作用的可识别性，并直接在物理哈密顿量参数空间而非抽象学习表示中传播后验不确定性。通过量子磁体NiPS₃的多模态粉末与单晶非弹性中子散射测量，展示了该框架可实现物理解释性哈密顿量推断、模态感知不确定性量化及自适应实验设计，为量子材料的不确定性感知微观表征与多模态实验设计提供通用策略。
英文摘要
Determining microscopic interactions from spectroscopic and scattering measurements is central to understanding quantum materials, yet it often remains unclear which interactions can be reliably revealed by the available experimental data and how additional experimental modalities should be designed to resolve the remaining ambiguities. Here we present an artificial intelligence-enabled framework for uncertainty-aware Hamiltonian inference and adaptive experimental design. By combining Hamiltonian-conditioned neural surrogates with Bayesian inference and observation geometry, the framework characterizes how measurements constrain Hamiltonian parameter space, quantifies the identifiability of microscopic interactions, and propagates posterior uncertainty directly in the physical Hamiltonian parameter space rather than an abstract learned representation. Using multimodal powder and single-crystal inelastic neutron scattering measurements of the quantum magnet NiPS$_{3}$, we demonstrate physically interpretable Hamiltonian inference, modality-aware uncertainty quantification, and adaptive experimental design. The framework provides a general strategy for uncertainty-aware microscopic characterization and multimodal experimental design across quantum materials.
