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Colloquium: Dr. Mehmet Topsakal, Brookhaven National Labor.

Off Campus: via Webex

Location

Online

Date & Time

October 14, 2020, 3:30 pm4:30 pm

Description

TITLE:   
Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

ABSTRACT: 

Simulations of excited-state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. In a recent study, we demonstrated that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra of molecules to quantitative accuracy. Specifically, the predicted spectra reproduce nearly all prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Besides its own utility in spectral analysis and structure inference, our method can be combined with structure search algorithms to enable high-throughput spectrum sampling of the vast material configuration space, which opens up new pathways to material design and discovery.


Webex Info

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Meeting number:
120 324 3883
Password: 
UMBC-Physics


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