Data-driven discovery of 2D materials by deep generative models

Abstract

Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here, we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and formation energies mirroring the training structures. Specifically, we train the CDVAE on 2615 2D materials with energy above the convex hull ΔHhull < 0.3 eV/atom, and generate 5003 materials that we relax using density functional theory (DFT). We also generate 14192 new crystals by systematic element substitution of the training structures. We find that the generative model and lattice decoration approach are complementary and yield materials with similar stability properties but very different crystal structures and chemical compositions. In total we find 11630 predicted new 2D materials, where 8599 of these have ΔHhull < 0.3 eV/atom as the seed structures, while 2004 are within 50 meV of the convex hull and could potentially be synthesised. The relaxed atomic structures of all the materials are available in the open Computational 2D Materials Database (C2DB). Our work establishes the CDVAE as an efficient and reliable crystal generation machine, and significantly expands the space of 2D materials.

Hundreds of new, stable, one-dimensional materials from a generative machine learning model

“…In analogy with Ref. [23], where the method is applied to two-dimensional materials, we fix this by introducing an artificial periodicity in the two nonperiodic directions, but with a periodicity length scale that is much larger than the length scale along the 1D materials. Thus the graph neural networks of CDVAE only connects atoms within the 1D structure and it learns to create 1D structures.…”

“…Recently, a generative machine-learning model was used to create new periodic materials [22], and the approach was subsequently adapted for two-dimensional materials [23]. Here we apply the same methodology to generate one-dimensional materials.…”

Discovery of 2D materials using Transformer Network based Generative Design

“…Two-dimensional (2D) materials have been emerging as a promsing functional materials with wide applications due to the novel fundamental physics with the reduced dimension [1] The systematic discovery and synthesis of functional 2D materials has been the focus of many studies [2,3,4,5,6,7,8]. Having exceptional and tunable properties, 2D materials hold strong promise in semiconductor, energy, and health applications [9,10].…”

“…Currently, there are three approaches for generating 2D materials: the top-down exfoliation method starts with a bulk material and exfoliates to make it thinner and peel the layers to obtain 2D materials; the bottom-up approach instead starts with existing 2D materials and uses element substitution to generate new materials. The third one is the de novo structure generation approach [3] based on deep learning generative models such as CDVAE [19]. To get new 2D materials through the exfoliation method, we need to judge whether the 3D bulk material is layered so that it can be exfoliated.…”

“…The most recent approach for 2D material generation is based on the deep learning generative model. Lyngby et al [3] use a crystal diffusion variational autoencoder (CDVAE) [19] to generate new 2D structures of high chemical and structural diversity and with formation energies mirroring the training structures. They also use the element substitution method to generate new possible 2D materials based on the newly generated 2D structures.…”