Towards Automated Characterization of Heterogeneous Materials: A Case Study Using X-ray Absorption Spectroscopy of Detonation Products

Tuan Anh Pham | 22-ERD-014

Executive Summary

Optimizing advanced materials performance often requires precise knowledge of local chemical composition and structure; however, it can be challenging to extract this information from experiments using existing spectroscopic analytical techniques. We will deliver new protocols for integrating high-fidelity simulations, experiments, and data science towards automated and reliable interpretation of x-ray spectroscopic experiments, advancing our understanding of the chemical properties of detonation products, which plays a key role in multiple national security missions.

Publications, Presentations, and Patents

Kwon, H., W, Sun, T, Hsu, W. Jeong, F. Aydin, S, Sharma, F. Meng, M.R. Carbone, X. Chen, D. Lu, L.F. Wan, M. Nielsen, T.A. Pham. (2023). “Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon.” The Journal of Physical Chemistry C. 127, 33, 16473–16484.

W. Jeong, W. Sun, T. Hsu, F. Aydin, X. Chen,M.  Bagge-Hansen, L.F. Wan, M.H. Nielsen, T.A. Pham, “Toward Automated Characterization of Disordered Materials: Integrating Atomistic Simulations, Machine Learning and X-ray Absorption Spectroscopy” (Presentation, MRS Spring Meeting, San Francisco, CA, April 2023). LLNL-PRES-847862.

H. Kwon, T.A. Pham, “Harnessing Neural Network for Predicting X-Ray Spectroscopy of Amorphous Carbon Materials” (Presentation, MRS Spring Meeting, San Francisco, CA, April 2023). LLNL-PRES-847860.

W. Jeong, T.A. Pham, “Toward Automated Characterization of Disordered Materials” (Presentation, LLNL Annual Postdoc Poster Symposium, Livermore, CA, May 2023). LLNL-PRES-854280.

H. Kwon, T.A. Pham, “Decoding X-Ray Absorption in a-C with Neural Networks” (Presentation, LLNL Annual Postdoc Poster Symposium, Livermore, CA, May 2023). LLNL-POST-848100.