Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
We present a distributed scheme for initialization from atomic wavefunctions in ab initio molecular dynamics simulations. Good initial guesses for approximate wavefunctions are very important in order to enable practical simulations with thousands of atoms. The new scheme is based on a distributed implementation of the Lanczos algorithm for very large dense eigenproblems. We show that the massively parallel BG/L (Blue Gene/L) supercomputer with its very fast separate network for collective communications is an ideal platform for the parallel Lanczos algorithm. We have implemented the new scheme in the popular plane-wave code CPMD. We showcase the applicability of the distributed initialization by a series of examples on a family of Silicon super cells ranging from 512 to 2048 atoms. © 2008 Elsevier B.V. All rights reserved.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Leonid Karlinsky, Joseph Shtok, et al.
CVPR 2019
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SC 2024
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AAMAS 2008