Dresden 2020 – scientific programme

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 71: Modelling and Simulation of Soft Matter II (joint session CPP/DY)

CPP 71.1: Talk

Wednesday, March 18, 2020, 15:00–15:15, ZEU 255

Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide — •Anand Narayanan Krishnamoorthy^1,2, Ganesh Sivaraman³, Matthias Baur¹, Christian Holm¹, Chris Benmore⁶, Marius Stan⁴, Gabor Csanyi⁵, and Álvaro Vázquez--Mayagoitia⁷ — ¹Institute for Computational Physics, University of Stuttgart — ²Helmholtz Institute Muenster — ³Leadership Computing Facility, Argonne National Laboratory - USA — ⁴Applied Materials Division, Argonne National Laboratory, USA — ⁵Department of Engineering, University of Cambridge, UK — ⁶X-ray Science Division, Argonne National Laboratory, USA — ⁷Computational Science Division, Argonne National Laboratory, USA

We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a melt-quench ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144-atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates (ie 1.0 K/ps) not accessible via AIMD.