Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

Schindler, Peter; Antoniuk, Evan R.; Cheon, Gowoon; Zhu, Yanbing; Reed, Evan J.

doi:10.1002/adfm.202401764

Title: Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

Journal Article · 19 March 2024 · Advanced Functional Materials

DOI: https://doi.org/10.1002/adfm.202401764 · OSTI ID:2326115

^[1];

^[2];

^[3]; Zhu, Yanbing ^[4];

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Department of Mechanical and Industrial Engineering Northeastern University Boston MA 02115 USA
Materials Science Division Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore CA 94550 USA
Google Research Mountain View CA 94043 USA
Department of Applied Physics Stanford University Stanford CA 94305 USA
Department of Materials Science and Engineering Stanford University Stanford CA 94305 USA

Abstract The work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO ₃ and the (001)‐F surface of Ag ₂ F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 10 ⁵ faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2326115

Report Number(s):: LLNL-JRNL-860962; 2401764

Journal Information:: Advanced Functional Materials, Journal Name: Advanced Functional Materials Journal Issue: 19 Vol. 34; ISSN 1616-301X

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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97 MATHEMATICS AND COMPUTING
Ab initio
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Title: Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

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