Data-driven deep generative design of stable spintronic materials

文献情報

出版日 2023-09-19

DOI 10.1039/D3CE00765K

インパクトファクター 3.545

著者

Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Jianjun Hu

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要旨

Discovering novel magnetic materials is essential for advancing the spintronic technology with significant applications in data communication, data storage, quantum computing, etc. While density functional theory (DFT) has been widely used for designing materials, its high computational demand for estimating the magnetic ground states of even a single material limits its ability to explore the vast chemical design space to find the right materials for spintronic applications. In this work, we developed a computational framework combining generative adversarial networks (GANs), machine learning (ML) classifiers, and DFT for de novo magnetic material discovery. We used the CubicGAN generative crystal structure design model for creating new ternary cubic structures. Machine learning classifiers were developed with around 90% accuracy to screen candidate ternary magnetic materials, which were then subjected to DFT based stability validation. Our calculations discovered and confirmed that Na6TcO6, K6TcO6, and BaCuF6 are stable ferromagnetic compounds, while Rb6IrO6 is a stable antiferromagnetic material. All these materials have zero energy above hull. Moreover, Na6TcO6 and BaCuF6 are found to be half metals that are highly favorable for spintronic applications. Due to the structural differences, the A6MO6 materials have a higher thermal capacity (Cv) compared to BaCuF6. At 300 K temperature, the Cv of the A6MO6 materials is around 1100 J K−1 mol−1 and that of BaCuF6 is about 176 J K−1 mol−1. This work demonstrates the promising potential of deep generative design for discovering novel functional materials.

掲載誌

CrystEngComm

CiteScore: 5.5

自己引用率: 7.7%

年間論文数: 643

CrystEngComm is the forum for the design and understanding of crystalline materials. We welcome studies on the investigation of molecular behaviour within crystals, control of nucleation and crystal growth, engineering of crystal structures, and construction of crystalline materials with tuneable properties and functions. We publish hypothesis-driven research into… how crystal design affects thermodynamics, phase transitional behaviours, polymorphism, morphology control, solid state reactivity (crystal-crystal solution-crystal, and gas-crystal reactions), optoelectronics, ferroelectric materials, non-linear optics, molecular and bulk magnetism, conductivity and quantum computing, catalysis, absorption and desorption, and mechanical properties. Using Techniques and methods including… Single crystal and powder X-ray, electron, and neutron diffraction, solid-state spectroscopy, spectrometry, and microscopy, modelling and data mining, and empirical, semi-empirical and ab-initio theoretical evaluations. On crystalline and solid-state materials. We particularly welcome work on MOFs, coordination polymers, nanocrystals, host-guest and multi-component molecular materials. We also accept work on peptides and liquid crystals. All papers should involve the use or development of a design or optimisation strategy. Routine structural reports or crystal morphology descriptions, even when combined with an analysis of properties or potential applications, are generally considered to be outside the scope of the journal and are unlikely to be accepted.