2026-05-28T02:11:04Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1240 2022-02-03T17:42:15Z openaire_data community-mcarchive

Kleiven, David Chang, Jin Hyun Kleiven, David Akola, Jaakko Peterson, Andrew Vegge, Tejs Chang, Jin Hyun 2022-02-03 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. application/octet-stream application/octet-stream text/markdown https://doi.org/10.24435/materialscloud:ha-ca oai:materialscloud.org:1240 mcid:2022.21 eng Materials Cloud https://doi.org/10.1088/2515-7655/abf9ef https://iopscience.iop.org/article/10.1088/2515-7655/abf9ef/meta https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:wq-me info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode BIG-MAP cluster expansion Monte Carlo phase transition bootstrapping machine learning energy materials Training sets based on uncertainty estimates in the cluster-expansion method info:eu-repo/semantics/other