Speaker
Description
Abstract:
Machine learning force fields (MLFFs) are set to become an indispensable tool in computational catalysis. In this talk, we provide a detailed walkthrough on how to train an MLFF to accurately predict energy barriers for catalytic reaction pathways. We demonstrate the capabilities of the resulting interatomic potential that offers near ab-initio accuracy at a fraction of the cost. Specifically, we illustrate that MLFFs not only speed up routine catalytic tasks by orders of magnitude but also allow for a more realistic treatment of catalytic systems, identifying lower energy barriers and capturing finite temperature effects. We also present a Jupyter notebook that highlights the simplicity of training a state-of-the-art many-body equivariant graph neural network, namely MACE. The capacity of MLFFs to deepen our understanding of extensively studied catalysts emphasizes the importance of fast and accurate alternatives to direct ab-initio simulations. Automated training procedures are paramount in enhancing the accessibility of MLFFs for both academic and industrial applications, and for effective use of HPC resources.
Bio:
Lars is a 4th-year PhD student specializing in machine learning force fields with an emphasis on catalysis and non-local effects. His academic background is rooted in theoretical physics, which he studied at the University of Birmingham with a concentration on Astrophysics. During his internship at the Max Plank Institute for Nuclear Physics, Lars made his first contact with scientific computing while working on a high energy camera that is set to observe x-rays emitted by cosmic particle accelerators. Changing to the University of Cambridge for his masters, Lars started focusing on condensed matter physics with his thesis on quantum information. Here he discovered his passion for computational modelling at the atomic scale.
