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)
Attila Cangi is head of the Department of Machine Learning for Materials Design at the Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf. With his team, he conducts research on the application of artificial intelligence and machine learning to computational materials modeling. His main goal is to develop AI-based solutions for sustainable materials, focusing on areas such as thermoelectric materials, spintronics, neuromorphic devices, and advanced semiconductor modeling. He received his Ph.D. in chemistry from the University of California, Irvine, and previously worked as a postdoctoral fellow at the Max Planck Institute of Microstructure Physics and as a staff member at Sandia National Laboratories.
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)
Attila Cangi is head of the Department of Machine Learning for Materials Design at the Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf. With his team, he conducts research on the application of artificial intelligence and machine learning to computational materials modeling. His main goal is to develop AI-based solutions for sustainable materials, focusing on areas such as thermoelectric materials, spintronics, neuromorphic devices, and advanced semiconductor modeling. He received his Ph.D. in chemistry from the University of California, Irvine, and previously worked as a postdoctoral fellow at the Max Planck Institute of Microstructure Physics and as a staff member at Sandia National Laboratories.
