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Haowen Wu

Publications and source records attributed to Haowen Wu.

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Observation of cooperative strong coupling between optical phonon and crystal-field excitations in a pseudo Jahn-Teller system

Cooperative interactions between localized electronic excitations and crystal lattice are central to the emergence of complex structural phases in materials. However, the scaling relations governing these collective behaviors remain largely unexplored. Here, using magneto-Raman spectroscopy, we report the direct observation of the strongly coupled optical phonon and non-degenerate crystal-field excitations (CFEs) in ErFeO3. By independently tuning the effective population of Jahn-Teller-active erbium ions through temperature and chemical dilution with Jahn-Teller-inactive yttrium ions, we identify the coupling strength varies linearly with the square root of electronic excitations population. Notably, Y-doping reveals the hybridization gap reduces significantly faster than predicted by density scaling alone, indicating phonon coherence is essential for establishing this cooperative interaction. Our findings highlight the role of optical phonons in mediating short-range interactions that drive cooperative Jahn-Teller effect, evidencing the pathway for tailoring electronic and vibrational properties of Jahn-Teller materials through population control.

cond-mat.mtrl-sci

Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework

Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a generative artificial intelligence model to capture the nonlinear evolution in sea surface temperature. Under variational constraints, DeepDA embedded with nonlinear features can effectively fuse heterogeneous data. The results show that DeepDA remains highly stable in capturing and generating nonlinear evolutions even when a large amount of observational information is missing. It can be found that when only 10% of the observation information is available, the error increase of DeepDA does not exceed 40%. Furthermore, DeepDA has been shown to be robust in the fusion of real observations and ensemble simulations. In particular, this paper provides a mechanism analysis of the nonlinear evolution generated by DeepDA from the perspective of physical patterns, which reveals the inherent explainability of our DL model in capturing multi-scale ocean signals.

physics.ao-ph