2DMAT

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2DMAT is a framework for applying a search algorithm to a direct problem solver to find the optimal solution. In version 1.0, for solving a direct problem, 2DMAT offers the wrapper of the solver for the total-reflection high-energy positron diffraction (TRHEPD) experiment. As algorithms, it offers the Nelder-Mead method, the grid search method, the Bayesian optimization method, and the replica exchange Monte Carlo method. Users can define original direct problem solvers or the search algorithms.

Information

Official site

https://www.pasums.issp.u-tokyo.ac.jp/2dmat/en/

License

GNU General Public License version 3（GPL v3)

Availability	Required python >= 3.6 numpy >= 1.17 toml Optional scipy (for `minsearch` algorithm) mpi4py (for `exchange` algorithm) physbo >= 0.3 (for `bayes` algorithm)
Core Developers	Yuichi Motoyama (The Institute for Solid State Physics, The University of Tokyo) Kazuyoshi Yoshimi (The Institute for Solid State Physics, The University of Tokyo) Takeo Hoshi (Department of Applied Mathematics and Physics, Tottori University)
Methodology	Nelder-Mead method grid search method Bayesian optimization method replica exchange Monte Carlo method
Parallelization	MPI
Related App	PHYSBO (optimization tools for PHYsics based on Bayesian Optimization )
Related keywords	Monte Carlo
Document	T. Hanada, H. Daimon, and S. Ino, Phys. Rev. B 51, 13320 (1995). K. Tanaka, T. Hoshi, I. Mochizuki, T. Hanada, A. Ichimiya, and T. Hyodo, Acta. Phys. Pol. A 137, 188 (2020). K. Tanaka, I. Mochizuki, T. Hanada, A. Ichimiya, T. Hyodo, and T. Hoshi, JJAP Conf. Series, in press, arXiv:2002.12165. K. Hukushima and K. Nemoto, J. Phys. Soc. Japan, 65, 1604 (1996). R. Swendsen and J. Wang, Phys. Rev. Lett. 57, 2607 (1986). .