2022
DOI: 10.21203/rs.3.rs-1407122/v1
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A machine learning route between band mapping and band structure

Abstract: Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials. While convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting quasiparticle dispersion (closely related to BS) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, we develop a pipeline including probabilistic machin… Show more

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Cited by 3 publications
(3 citation statements)
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“…5 These two important and widely used models of the photoemission process can be further extended with the help of simulation methods such as Monte-Carlo, [54][55][56][57] density functional theory (DFT) [58][59][60][61][62] and machine learning. [63][64][65][66][67][68] DFT, for example, is used to calculate energy states based on electron densities and their energies. The calculations show different scenarios of covalent binding interactions between different atoms and the resulting work functions.…”
Section: Photoemission Models: One Step and Three-step Modelmentioning
confidence: 99%
“…5 These two important and widely used models of the photoemission process can be further extended with the help of simulation methods such as Monte-Carlo, [54][55][56][57] density functional theory (DFT) [58][59][60][61][62] and machine learning. [63][64][65][66][67][68] DFT, for example, is used to calculate energy states based on electron densities and their energies. The calculations show different scenarios of covalent binding interactions between different atoms and the resulting work functions.…”
Section: Photoemission Models: One Step and Three-step Modelmentioning
confidence: 99%
“…Figure 2a-d shows energyresolved photoemission signals along the K 0 À K cut of the Brillouin zone, at selected time delays. The band mappings are contrastenhanced using a multidimensional extension of the contrast limited adaptive histogram equalization (MCLAHE) 30,31 for better visualization of the band structure. The momentum distributions above E F within the first 400 fs reveal that the excited states are localized in three different types of valleys: the Dirac cones of graphene at its K points (K Gr ) and the K and Q valleys of WSe 2 (K W Se 2 ,Q WSe 2 ), as shown in Fig.…”
mentioning
confidence: 99%
“…For example, a deep layer of convolutional neural network (ConvNet) is trained to denoise ARPES data [28]. Afterwards, there are efforts to obtain how the bandstructure calculation based on the ARPES data [29,30], where reference [30] provides additional feature of obtaining the result even through a noisy data (simulated noise). There is also work in automation of spatial domain assignment with a smaller subset of data over a predetermined area, where subsequent position measurement is calculated with Gaussian process to give the possible highest amount of information [31].…”
mentioning
confidence: 99%