Predicting the electronic structure of matter at scale with machine learning

Date

Oct 17, 2024

Time

1:00 PM - 3:00 PM

Speaker

Attila Cangi

Affiliation

Helmholtz-Zentrum Dresden-Rossendorf

Series

TUD nanoSeminar

Language

en

Main Topic

Physik

Other Topics

Physik

Host

Arezoo Dianat

Description

In this presentation, I will discuss our recent advancements in utilizing machine learning to significantly enhance the efficiency of electronic structure calculations [1]. Specifically, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations by incorporating deep neural networks within the Materials Learning Algorithms framework [2,3]. Our results demonstrate substantial gains in calculation speed for metals across their melting point. Additionally, our implementation of automated machine learning has resulted in significant savings in computational resources when identifying optimal neural network architectures, laying the foundation for large-scale investigations [4]. Furthermore, I will present our most recent breakthrough, which enables neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [5]. This achievement opens up new avenues for studying complex materials systems that were previously computationally intractable. 
 
 [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials, 6, 040301 (2022) 
 [2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, Phys. Rev. B 104, 035120 (2021). 
 [3] J. Ellis, L. Fiedler, G. Popoola, N. Modine, J. Stephens, A. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B, 104, 035120 (2021) 
 [4] L. Fiedler, N. Hoffmann, P. Mohammed, G. Popoola, T. Yovell, V. Oles, J. Austin Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol., 3, 045008 (2022) 
 [5] L. Fiedler, N. Modine, S. Schmerler, D. Vogel, G. Popoola, A. Thompson, S. Rajamanickam, A. Cangi, npj. Comput. Mater., 9, 115 (2023)

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Last modified: Oct 17, 2024, 7:39:08 AM