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Deep Generative Modeling of Two-Dimensional Crystals

Engineering and Technology

Deep Generative Modeling of Two-Dimensional Crystals

P. Lyngby and K. Thygesen

This research by P. Lyngby and K.S. Thygesen delves into the innovative use of a Crystal Diffusion Variational Autoencoder (CDVAE) paired with a lattice decoration protocol (LDP) to create new two-dimensional crystals. The findings showcase how CDVAE excels in generating a wider array of stable 2D materials compared to LDP.

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Playback language: English
Abstract
This paper explores the use of a deep generative model, specifically a Crystal Diffusion Variational Autoencoder (CDVAE), in conjunction with a lattice decoration protocol (LDP), to generate novel two-dimensional (2D) crystals. The study compares the performance of CDVAE and LDP in generating stable and diverse 2D materials, evaluating their thermodynamic stability and structural characteristics. The results demonstrate CDVAE's ability to produce a significantly larger and more diverse range of 2D materials than LDP, while maintaining comparable thermodynamic stability.
Publisher
npj Computational Materials
Published On
Nov 11, 2022
Authors
P. Lyngby, K.S. Thygesen
Tags
Deep Generative Model
Variational Autoencoder
2D Crystals
Thermodynamic Stability
Lattice Decoration Protocol

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