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Studying the so-called ”hidden” phases of quantum materials—phases that do not exist under equilibrium conditions, but can be accessed with light—reveals new insights into the broader field of structural phase transitions. Using terahertz irradiation as well as hard x-ray probes made available by x-ray free electron lasers (XFELs) provides unique capabilities to study phonon dispersion in these materials. Here, we study the cubic peak of the quantum paraelectric strontium titanate (SrTiO3, STO) below the 110 K cubic-to-tetragonal tran- sition. Our results reveal a temperature and field strength dependence of the transverse acoustic mode in agreement with previous work on the avoided crossing occurring at finite wavevector, as well as evidence of anharmonic coupling between transverse optical phonons and a fully symmetric A1g phonon. These results elucidate previous optical studies on STO and hold promise for future studies on the hidden metastable phases of quantum materials.
Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
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