arXiv subjects

Roy Liu

Publications and source records attributed to Roy Liu.

2 recordsLinked to original sources

arXiv2026

Learning materials spectra across modalities and scales with structure-conditioned neural fields

Spectroscopic observables encode rich information about material behavior across diverse, often high-dimensional domains of energy, momentum, and other experimental coordinates, providing critical insights into the physical properties underlying material functionalities and applications. However, learning these responses directly from atomic structures remains challenging due to the complexity of both structural configurations and spectroscopic observation spaces. Here, we develop a structure-conditioned neural-field framework that learns continuous spectroscopic response functions by coupling an equivariant structural encoder with a coordinate-conditioned network. The framework accurately predicts spectroscopic observables across diverse observation domains, ranging from one-dimensional densities of states to multidimensional scattering functions. We show that fine-tuning with limited spectral supervision captures strain- and composition-dependent spectral changes, with validated transfer to alloy configurations containing hundreds of atoms and computational scalability demonstrated for configurations exceeding $10^4$ atoms. Our work establishes a versatile and computationally scalable approach to learning structure-spectroscopy relationships, providing a foundation for spectroscopic analysis and future materials characterization, inverse problems, and experimental design.

cond-mat.mtrl-sci↗

arXiv2026

Observation geometry for uncertainty-aware Hamiltonian inference and experimental design in quantum magnets

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.

cond-mat.str-el↗