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. 2020 Aug 26;6(8):1412-1420.
doi: 10.1021/acscentsci.0c00426. Epub 2020 Jul 10.

Generative Adversarial Networks for Crystal Structure Prediction

Sungwon Kim  1 , Juhwan Noh  1 , Geun Ho Gu  1 , Alan Aspuru-Guzik  2   3   4 , Yousung Jung  1
Affiliations

Generative Adversarial Networks for Crystal Structure Prediction

Sungwon Kim et al. ACS Cent Sci. .

Erratum in

Abstract

The constant demand for novel functional materials calls for efficient strategies to accelerate the materials discovery, and crystal structure prediction is one of the most fundamental tasks along that direction. In addressing this challenge, generative models can offer new opportunities since they allow for the continuous navigation of chemical space via latent spaces. In this work, we employ a crystal representation that is inversion-free based on unit cell and fractional atomic coordinates and build a generative adversarial network for crystal structures. The proposed model is applied to generate the Mg-Mn-O ternary materials with the theoretical evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The proposed generative HTVS framework predicts 23 new crystal structures with reasonable calculated stability and band gap. These findings suggest that the generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Point cloud representation of crystal structure. The representation is composed of unit cell parameters and the sets of rescaled fractional coordinates of atoms.
Figure 2
Figure 2
Composition-Conditioned Crystal GAN proposed in this work for inorganic crystal design. Z, Cgen, and Creal denote a random input noise, user-desired composition condition, and composition of real material, respectively. The variables x̃ and x denote the feature (representation) of generated and real materials, respectively. Ĉgen and Ĉreal denote the predicted composition of the generated and real features, respectively. D(x) is the critic function also known as the critic network.
Figure 3
Figure 3
Schematic of the generation process for crystals with the desired composition. The composition of generated material is determined by the output of the classifier network.
Figure 4
Figure 4
Phase diagram and DFT calculated thermodynamic stability (i.e., the energy above the convex hull) for the generated Mg–Mn–O materials. Ternary phase diagram of the Mg–Mn–O system constructed using the convex hull stable phases taken from the materials project database (green circle), including (a) metastable Mg–Mn–O compositions (red circle) taken from materials project or (b) possible compositions that can be explored by our proposed generative model. The stability of the crystal structure in the form of the energy above the convex hulls is computed using DFT for (c) 11 compositions included in the MP database, and (d) 20 new compositions not in the MP database. Red crosses are the generated materials with composition-conditioned, and blue stars in part c correspond to the materials in the MP database. (There are no metastable (Ehull ≤ 200 meV/atom) structures having Mg2Mn2O5 composition in MP database.) The horizontal dotted red lines represent 80 and 0 meV/atom, respectively.
Figure 5
Figure 5
Pourbaix stabilities and HSE band gap energies of stable structures generated by the proposed crystal GAN model (red circles). The dashed blue box is the target region for the promising photoanode material. Stars represent the promising photoanode materials discovered by other previous works (i.e., conventional HTVS),,, and purple stars are materials synthesized experimentally.
See this image and copyright information in PMC

References

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