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Curved-line search algorithm for ab initio atomic structure relaxation

Journal Article · · Physical Review B
 [1];  [2];  [2];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division; Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Superlattices and Microstructures
  2. Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Superlattices and Microstructures
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division
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Ab initio atomic relaxations often take large numbers of steps and long times to converge, especially when the initial atomic configurations are far from the local minimum or there are curved and narrow valleys in the multidimensional potentials. An atomic relaxation method based on on-the-flight force learning and a corresponding curved-line search algorithm is presented to accelerate this process. In this work, results demonstrate the superior performance of this method for metal and magnetic clusters when compared with the conventional conjugate-gradient method.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1544368
Alternate ID(s):
OSTI ID: 1393721
Journal Information:
Physical Review B, Journal Name: Physical Review B Journal Issue: 11 Vol. 96; ISSN 2469-9950; ISSN PRBMDO
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY