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In the pursuit of identifying improved amorphous oxide coatings for gravitational wave detector test masses, 44% Ti-doped GeO2 has emerged as a potential candidate due to its low mechanical loss [Vajente et al. Phys. Rev. Lett. 127, 071101 (2021)]. It has been proposed and experimentally demonstrated that a reduction in mechanical loss in amorphous oxides is associated with medium-range order (MRO) dominated by cornersharing links in the amorphous network. In this work, we describe a combined experimental and theoretical approach to investigate the MRO of Ti-doped GeO2. Using atomistic modeling and simulations, we calculate the Raman spectrum of 44% Ti-doped GeO2, and we show that it predicts the main features observed in the experiments and further provides insight into the MRO, which would otherwise be hidden. It is shown that the amorphous oxide mixture exhibits a distribution of predominantly large (>4 member) rings formed by corner-shared tetrahedral Ge-O-Ti connections. It is also shown that the addition of Ti to a-GeO2 does not largely compromise corner-sharing connections, as the smaller populations of Ti-O-Ti and Ge-O-Ge links in the network are predominantly corner-shared. The mainly covalently bonded representation of the MRO of Ti-doped GeO2 revealed by this analysis validates the importance of the MRO network organization in influencing mechanical loss in amorphous oxides. 

Abstract Wave‐particle duality, intertwining two inherently contradictory properties of quantum systems, remains one of the most conceptually profound aspects of quantum mechanics. By using the concept of energy capacity, the ability of a quantum system to store and extract energy, a device‐independent uncertainty relation is derived for wave‐particle duality. This relation is shown to be independent of both the representation space and the measurement basis of the quantum system. Furthermore, it is experimentally validated that this wave‐particle duality relation using a photon‐based platform. 

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a “spectrum-structure” correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments. 

The confined variational method is used to study the elastic scattering of the positron from the ground-state helium with the scattering energy in the range from 0.05 eV to 11.02 eV. Describing the correlation effect with explicitly correlated Gaussians, we obtain accurate phase shifts, S-wave scattering length, elastic scattering cross sections, and annihilation parameters for different incident momenta. Specifically, by a least-squares fit of the data to the effective-range theory, we determine the room temperature annihilation parameter Zeff = 3.955, which is in perfect agreement with the measured result of 3.94 ± 0.02 [J. Phys. B 8, 1734 (1975)].