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Training sets based on uncertainty estimates in the cluster-expansion method

David Kleiven1*, Jaakko Akola1,2, Andrew Peterson3,4, Tejs Vegge4, Jin Hyun Chang4*

1 Department of Physics, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway

2 Computational Physics Laboratory, Tampere University, P.O. Box 692, FI-33014 Tampere, Finland

3 School of Engineering, Brown University, Providence, RI 02912, United States of America

4 Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

* Corresponding authors emails: david.kleiven@ntnu.no, jchang@dtu.dk
DOI10.24435/materialscloud:ha-ca [version v1]

Publication date: Feb 03, 2022

How to cite this record

David Kleiven, Jaakko Akola, Andrew Peterson, Tejs Vegge, Jin Hyun Chang, Training sets based on uncertainty estimates in the cluster-expansion method, Materials Cloud Archive 2022.21 (2022), doi: 10.24435/materialscloud:ha-ca.

Description

Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to first-principles calculation results. The paper reports a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. We call the new strategy a "bootstrapping uncertainty structure selection" (BUSS) scheme and compared its performance against a popular scheme where one uses a combination of random structure and ground-state search (referred to as RGS). The provided dataset contains the training sets generated using BUSS and RGS for constructing a CE model for disordered Cu2ZnSnS4 material. The files are in the format of the Atomic Simulation Environment (ASE) database (please refer to ASE documentation for more information https://wiki.fysik.dtu.dk/ase/index.html). Each `.db` file contains 100 DFT calculations, which were generated using iteration cycles. Each iteration cycle is referred to as a generation (marked with `gen` key in the database) and each database contains 10 generations where each generation consists of 10 training structures. See more details in the paper.

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Files

File name Size Description
buss.db
MD5md5:b894ad3a6916a4f3ca6b2da0d4b5fd49
972.0 KiB ASE database containing the training structures generated using BUSS scheme
rgs.db
MD5md5:4bbbc675bdfcb53813ca300e9a68da2a
544.0 KiB ASE database containing the training structures generated using RGS scheme

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper in which the method is described)

Keywords

BIG-MAP cluster expansion Monte Carlo phase transition bootstrapping machine learning energy materials

Version history:

2022.21 (version v1) [This version] Feb 03, 2022 DOI10.24435/materialscloud:ha-ca