High Throughput XAS Calculations and Machine Learning: XAS Journal Club, Chi Chen
Автор: Global XAS Journal Club
Загружено: 2021-05-06
Просмотров: 568
Title: High Throughput Calculations and Machine Learning for X-ray Absorption Spectroscopy
Speaker: Dr. Chi Chen (UC San Diego)
Abstract: High-throughput calculations and machine learning characterizations of X-ray absorption spectroscopy (XAS) have recently gained popularity within the research community. This seminar will introduce the high-throughput calculation workflow for constructing the largest computational XAS database, the XASDb in Materials Project. Using the XASDb, we show an ensemble-based similarity matching algorithm for fast spectral retrieval and identification. Then, we develop random forest classification models for predicting atom coordination environment directly from the spectra.
Recommended reading:
1. Mathew, K., Zheng, C., Winston, D. et al. High-throughput computational X-ray absorption spectroscopy. Sci Data 5, 180151 (2018). https://doi.org/10.1038/sdata.2018.151
2. Zheng, C., Mathew, K., Chen, C. et al. Automated generation and ensemble-learned matching of X-ray absorption spectra. npj Comput Mater 4, 12 (2018). https://doi.org/10.1038/s41524-018-00...
3. Zheng, C., Chen, C., Chen, Y. and Ong S.P. Random forest models for accurate identification of coordination environments from X-ray absorption near-edge structure. Patterns 1(2), 10013 (2020). https://doi.org/10.1016/j.patter.2020...
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