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Accelerating high-throughput simulations using machine learning methods

Thomas Bligaard

Recorded 29 May 2017 in Lausanne, Vaud, Switzerland

Course: High-throughput computations: general methods and applications using AiiDA - May, 2017

Abstract

Modern machine learning techniques allows one to fit (non-linear) models with many unknown parameters while maintaining control of issues relating to overfitting. When moving towards massively high-throughput simulations many parts of the simulations can benefit from being integrated with machine learning. Examples relate to improved initial guesses, faster search algorithms using surrogate machine learning models, systematic error estimation, reduction of model complexity, and improved simulation model accuracy.

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