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We combined synchrotron-based near field infrared spectroscopy and atomic force microscopy to image the properties of ferroelastic domain walls in Sr3Sn2O7. Although frequency shifts at the walls are near the limit of our sensitivity, we can confirm semiconducting rather than metallic character and widths between 20 and 60 nm. The latter is significantly narrower than in other hybrid improper ferroelectrics like Ca3Ti2O7. We attribute this trend to the softer lattice in Sr3Sn2O7, which may enable the octahedral tilt and rotation order parameters to evolve more quickly across the wall without significantly increased strain. These findings are crucial for the understanding of phononic properties at interfaces and the development of domain wall-based devices. 

The nodal-line semiconductor Mn3Si2Te6 is generating enormous excitment due to the recent discovery of a field-driven insulator-to-metal transition and associated colossal magnetoresistance as well as evidence for a new type of quantum state involving chiral orbital currents. Strikingly, these qualities persist even in the absence of traditional Jahn-Teller distortions and double-exchange mechanisms, raising questions about exactly how and why magnetoresistance occurs along with conjecture as to the likely signatures of loop currents. Here, we measured the infrared response of Mn3Si2Te6 across the magnetic ordering and field-induced insulator-to-metal transitions in order to explore colossal magnetoresistance in the absence of Jahn-Teller and double-exchange interactions. Rather than a traditional metal with screened phonons, the field-driven insulator-to-metal transition leads to a weakly metallic state with localized carriers. Our spectral data are fit by a percolation model, providing evidence for electronic inhomogeneity and phase separation. Modeling also reveals a frequency-dependent threshold field for carriers contributing to colossal magnetoresistance which we discuss in terms of polaron formation, chiral orbital currents, and short-range spin fluctuations. These findings enhance the understanding of insulator-to-metal transitions in new settings and open the door to the design of unconventional colossal magnetoresistant materials. 

How do people perform general-purpose physical reasoning across a variety of scenarios in everyday life? Across two stud ies with seven different physical scenarios, we asked participants to predict whether or where two objects will make contact. People achieved high accuracy and were highly consistent with each other in their predictions. We hypothesize that this robust generalization is a consequence of mental simulations of noisy physics. We designed an “intuitive physics engine” model to capture this generalizable simulation. We find that this model generalized in human-like ways to unseen stimuli and to a different query of predictions. We evaluated several state-of-the-art deep learning and scene feature models on the same task and found that they could not explain human predictions as well. This study provides evidence that human’s robust generalization in physics predictions are supported by a probabilistic simulation model, and suggests the need for structure in learned dynamics models. 

Many models of intuitive physical reasoning posit some kind of mental simulation mechanism, yet everyday environments frequently contain far more objects than people could plausibly represent with their limited cognitive capacity. What determines which objects are actually included in our representations? We asked participants to predict how a ball will bounce through a complex field of obstacles, and probed working memory for objects in the scene that were more and less likely to be relevant to the ball’s trajectory. We evaluate different accounts of relevance and find that successful object memory is best predicted by how frequently a ball’s trajectory is expected to contact that object under a probabilistic simulation model. This suggests that people construct representations for mental simulation efficiently and dynamically, on the fly, by adding objects “just in time”: only when they are expected to become relevant for the next stage of simulation.