scholarly journals Active learning in Gaussian process interpolation of potential energy surfaces

2018 ◽  
Vol 149 (17) ◽  
pp. 174114 ◽  
Author(s):  
Elena Uteva ◽  
Richard S. Graham ◽  
Richard D. Wilkinson ◽  
Richard J. Wheatley
Author(s):  
Sergei Manzhos ◽  
Eita Sasaki ◽  
Manabu Ihara

Abstract We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.


2021 ◽  
Vol 155 (14) ◽  
pp. 144109
Author(s):  
Yahya Saleh ◽  
Vishnu Sanjay ◽  
Armin Iske ◽  
Andrey Yachmenev ◽  
Jochen Küpper

2021 ◽  
Vol 155 (14) ◽  
pp. 144106
Author(s):  
Jack Broad ◽  
Simon Preston ◽  
Richard J. Wheatley ◽  
Richard S. Graham

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