NDLI: Uncertainty Quantification of Artificial Neural Network Based Machine Learning Potentials

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Li, Yumeng Xiao, Weirong Wang, Pingfeng
Copyright Year	2018
Abstract	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.
File Format	PDF
ISBN	9780791852170
DOI	10.1115/IMECE2018-88071
Volume Number	Volume 12: Materials: Genetics to Structures
Conference Proceedings	ASME 2018 International Mechanical Engineering Congress and Exposition
Language	English
Publisher Date	2018-11-09
Publisher Place	Pittsburgh, Pennsylvania, USA
Access Restriction	Subscribed
Subject Keyword	Design Potential energy Uncertainty Simulation Artificial neural networks Machine learning Uncertainty quantification Titanium Materials science Molecular dynamics simulation
Content Type	Text
Resource Type	Article

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