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| 1 | Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy显示文摘Understanding lattice deformations is crucial in determining the properties of nanomaterials,which can become more prominent in future applications ranging from energy harvesting to electronic devices.However,it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area.Here,we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials.Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy(4D-STEM).Our approach overcomes the current barriers of large 4D data analysis without a priori knowledge of the sample.Using this purely data-driven analysis,we have uncovered different types of material deformations,such as strain,lattice distortion,bending contour,etc.,which can significantly impact the band structure and subsequent performance of nanomaterials-based devices.We envision that this data-driven procedure will provide insight into materials’intrinsic structures and accelerate the discovery of materials. | Chuqiao Shi Michael C.Cao Sarah M.Rehn Sang-Hoon Bae Jeehwan Kim Matthew RJones David A.Muller Yimo Han | 2022 | npj Computational Materials2022，，1： | 3 |
| 2 | 'Delineation of urban footprints from TerraSAR-X data by analyzing speckle characteristics and intensity information,'显示文摘 | T.Esch M.Thiel A.Schenk A.Roth A.Muller S.Dech | | 0，，02： | 1 |
| 3 | Mathematical modelling of the spread of COVID-19 on a university campus显示文摘In this paper we present a deterministic transmission dynamic compartmental model for the spread of the novel coronavirus on a college campus for the purpose of analyzing strategies to mitigate an outbreak.The goal of this project is to determine and compare the utility of certain containment strategies including gateway testing,surveillance testing,and contact tracing as well as individual level control measures such as mask wearing and social distancing.We modify a standard SEIR-type model to reflect what is currently known about COVID-19.We also modify the model to reflect the population present on a college campus,separating it into students and faculty.This is done in order to capture the expected different contact rates between groups as well as the expected difference in outcomes based on age known for COVID-19.We aim to provide insight into which strategies are most effective,rather than predict exact numbers of infections.We analyze effectiveness by looking at relative changes in the total number of cases as well as the effect a measure has on the estimated basic reproductive number.We find that the total number of infections is most sensitive to parameters relating to student behaviors.We also find that contact tracing can be an effective control strategy when surveillance testing is unavailable.Lastly,we validate the model using data from Villanova University's online COVID-19 Dashboard from Fall 2020 and find good agreement between model and data when superspreader events are incorporated in the model as shocks to the number of infected individuals approximately two weeks after each superspreader event. | Kaitlyn Muller Peter A.Muller | 2021 | Infectious Disease Modelling2021，6，1： | 0 |
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