Oxygen Vacancy Formation Energy in Metal Oxides: High Throughput Computational Studies and Machine Learning Predictions

The oxygen vacancy formation energy ($\Delta E_{vf}$) governs defect dynamics and is a useful metric to perform materials selection for a variety of applications. However, density functional theory (DFT) calculations of $\Delta E_{vf}$ come at a greater computational cost than the typical bulk calculations available in materials databases due to the involvement of multiple vacancy-containing supercells. As a result, available repositories of direct calculations of $\Delta E_{vf}$ remain relatively scarce, and the development of machine learning models capable of delivering accurate predictions is of interest. In the present, work we address both such points. We first report the results of new high-throughput DFT calculations of oxygen vacancy formation energies of the different unique oxygen sites in over 1000 different oxide materials, which together form the largest dataset of directly computed oxygen vacancy formation energies to date, to our knowledge. We then utilize the resulting dataset of $\sim$2500 $\Delta E_{vf}$ values to train random forest models with different sets of features, examining both novel features introduced in this work and ones previously employed in the literature. We demonstrate the benefits of including features that contain information specific to the vacancy site and account for both cation identity and oxidation state, and achieve a mean absolute error upon prediction of $\sim$0.3 eV/O, which is comparable to the accuracy observed upon comparison of DFT computations of oxygen vacancy formation energy and experimental results. Finally, we demonstrate the predictive power of the developed models in the search for new compounds for solar-thermochemical water-splitting applications, finding over 250 new AA$^{\prime}$BB$^{\prime}$O$_6$ double perovskite candidates

OPTIMADE, an API for exchanging materials data

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification

OPTIMADE, an API for exchanging materials data

: The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification

Identification of High-Dielectric Constant Compounds from Statistical Design

The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 $<$ $\epsilon$ $<$ 101) and large band gaps (2.9$<$ $E_{\text{g}}$(eV) $<$ 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu$_5$SiCl$_6$O$_4$ and HoClO), and are shown to be thermodynamically stable against common semiconductors via phase-diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20$<$$\epsilon$$<$40) and band gaps (2.3$<$$E_{\text{g}}$(eV)$<$2.7). While the ANN training data is obtained from Materials Project, the search-space consists of materials from Open Quantum Materials Database (OQMD) - demonstrating a successful implementation of cross-database materials design. Overall, we report dielectric properties of 17 materials calculated using ab-initio calculations, that were selected in our design workflow. The dielectric materials with high dielectric properties predicted in this work open up further experimental research opportunities.Comment: 38 pages, 6 figures, To be published in npj Computational Material

A dataset of DFT-computed oxygen vacancy formation energies of metal oxides

&lt;p&gt;This dataset contains DFT-computed oxygen vacancy formation energies of different oxygen sites in over 1000 metal oxides present on the Open Quantum Materials Database (OQMD). Entries in the Data.csv file are indexed using the OQMD entry ID of the pristine structure, and list, alongside the oxygen vacancy formation energy, a number of other properties used as features in machine learning models. An exemplary code allowing to test the performance of a random forest regressor using different sets of features to predict the vacancy formation energy, as a function of training set size, is provided in Test_Models.py. More details on the dataset and machine learning models can be found in: arXiv:2309.01160. &nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgments&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The work was funded by the U.S. Department of Energy under Grant DE-EE0008089. S. G. acknowledges the Air Force Office of Scientific Research for support under Award No. FA9550-18-1-0136 (OQMD database). A. G. acknowledges the &nbsp;Center for Hierarchical Materials Design (ChiMaD) under Award No. 70NANB19H005 (ML models). A. J.A. S.-C. acknowledges the financial support to National Agency for Research and Development (ANID)/DOCTORADO BECAS CHILE/2018 - 56180024. A. J.A. S.-C. and T. L. acknowledge funding from the Toyota Research Institute through the Accelerated Materials Design and Discovery program (ML representations). The data was produced relying on the computing power provided by Quest high performance computing facility at Northwestern University.&lt;/p&gt

The OPTIMADE Specification

The Open Databases Integration for Materials Design (OPTIMADE) consortium aims to make materials databases interoperational by developing a common REST API. This repository contains the specification of the OPTIMADE API