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Machine learning with bond information for local structure optimizations in surface science

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/5.0033778· OSTI ID:1768006
 [1];  [1];  [2];  [3];  [1]
  1. Technical Univ. of Denmark, Lyngby (Denmark)
  2. Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis; SLAC National Accelerator Lab., Menlo Park, CA (United States); Columbia Univ., New York, NY (United States). Columbia Electrochemical Energy Center
  3. Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis; Technical Univ. of Denmark, Lyngby (Denmark)
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Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. Furthermore, we introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

Research Organization:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); VILLUM Fonden
Grant/Contract Number:
AC02-76SF00515
OSTI ID:
1768006
Alternate ID(s):
OSTI ID: 1970570
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 23 Vol. 153; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Gaussian processes
adsorption
atomic structure
density functional theory
machine learning
optimization algorithms
potential energy surfaces
surface science