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Uncertainty Quantification of Artificial Neural Network Based Machine Learning Potentials

[+] Author Affiliations
Yumeng Li, Weirong Xiao, Pingfeng Wang

University of Illinois at Urbana Champaign, Urbana, IL

Paper No. IMECE2018-88071, pp. V012T11A030; 10 pages
  • ASME 2018 International Mechanical Engineering Congress and Exposition
  • Volume 12: Materials: Genetics to Structures
  • Pittsburgh, Pennsylvania, USA, November 9–15, 2018
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5217-0
  • Copyright © 2018 by ASME


Atomistic simulations play an important role in the material analysis and design by being rooted in the accurate first principles methods that free from empirical parameters and phenomenological models. However, successful applications of MD simulations largely depend on the availability of efficient and accurate force field potentials used for describing the interatomic interactions. As a powerful tool revolutionizing many areas in science and technology, machine learning techniques have gained growing attentions in the field of material science and engineering due to their potentials to accelerate the material discovery through their applications in surrogate model assisted material design. Despite tremendous advantages of employing machine learning techniques for the development of force field potentials as compared to conventional approaches, the uncertainty involved in the machine learning interpolated atomic potential energy surface has not drew much attention although it is an important issue. In this paper, the uncertainty quantification study is performed for the machine learning interpolated atomic potentials, and applied to the titanium dioxide (TiO2), an industrially relevant and well-studies material. The study results indicated that quantifying uncertainties is an indispensable task that must be performed along with the atomistic simulation process for a successful application of the machine learning based force field potentials.

Copyright © 2018 by ASME



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