SKM 2021 – wissenschaftliches Programm
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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 14: Condensed-Matter Simulations augmented by Advanced Statistical Methodologies (joint session DY/CPP)
CPP 14.4: Vortrag
Freitag, 1. Oktober 2021, 10:45–11:00, H2
Investigation of transferability in LDOS based DFT surrogate models for multiscale simulations — •Lenz Fiedler1,2 and Attila Cangi1,2 — 1Center for Advanced Systems Understanding (CASUS) — 2Helmholtz-Zentrum Dresden-Rossendorf
Density Functional Theory (DFT) is one of the most important computational tools for materials science, as it combines high accuracy with general computational feasibility. However, applications important to scientific progress can pose problems to even the most advanced and efficient DFT codes due to size and/or complexity of the underlying simulations. Namely the modeling of materials across multiple length and time scales at ambient or extreme conditions, necessary for the understanding of important physical phenomena such as radiation damages in fusion reactor walls, evade traditional ab-initio treatment. DFT surrogate models are a useful tool in achieving this goal by reproducing DFT results at drastically reduced computational cost by using machine learning methods. Yet, a lack of transferability of many approaches lead to repeated and costly training data generation procedures. Here, we present results of an investigation to transfer such machine learning DFT surrogate models between different simulation cell sizes, with the goal of reducing the overall amount of computational time for training data generation. The models are based upon the Materials Learning Algorithms (MALA) package [1] and the therein implemented LDOS based machine learning workflow [2].
[1]: https://github.com/mala-project
[2]: J. A. Ellis et al., Phys. Rev. B 104, 035120, 2021