Introduction

What is odatse-aenet

odatse-aenet is a solver module for integrating the machine learning potential AENET (Atom-centered Neural Network) with the ODAT-SE (Open Data Analysis Tool for Science and Engineering) framework.

Using the optimization algorithms provided by ODAT-SE, you can compute energies with machine learning potentials constructed by AENET and search for atomic structure parameters (such as bond distances) that minimize the energy.

Available algorithms

The following algorithms are available in the ODAT-SE framework:

  • Nelder-Mead method (minsearch)

  • Grid search method (mapper)

  • Bayesian optimization (bayes)

  • Replica exchange Monte Carlo method (exchange)

  • Population annealing Monte Carlo method (pamc)

Package structure

  • AenetSolver: Executes AENET predict.x as an ODAT-SE solver class to perform energy calculations.

Developers

  • 谷田秀哉 (Graduate School of Sustainability Science, Tottori University) — Initial code development (Master’s thesis)

  • 星健夫 (National Institute for Fusion Science) — ODAT-SE co-development, research supervision

  • 吉見一慶 (Institute for Solid State Physics, The University of Tokyo) — Code organization and packaging

License

This software is released under the Mozilla Public License 2.0 (MPL-2.0).

Citation

When publishing research results using ODAT-SE, please cite the following reference:

Y. Motoyama, K. Yoshimi, I. Mochizuki, H. Iwamoto, H. Ichinose, and T. Hoshi, “Data-analysis software framework 2DMAT and its application to experimental measurements for two-dimensional material structures”, Computer Physics Communications 280, 108465 (2022).