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Unveiling two-dimensional magnesium hydride as a hydrogen storage material via a generative adversarial network.

ABSTRACT: This study used an artificial intelligence (AI)-based crystal inverse-design approach to investigate the new phase of two-dimensional (2D) pristine magnesium hydride (Mg _x H _y ) sheets and verify their availability as a hydrogen storage medium. A 2D binary phase diagram for the generated crystal images was constructed, which was used to identify significant 2D crystal structures. Then, the electronic and dynamic properties of the Mg _x H _y sheets in low-energy periodic phases were identified via density functional theory (DFT) calculations; this revealed a previously unknown phase of 2D MgH₂ with a P4̄m2 space group. In the proposed structure, the adsorption behaviors of the Li-decorated system were investigated for multiple hydrogen molecules. It was confirmed that Li-decorated MgH₂ has an expected theoretical gravimetric density of 6 wt%, with an average H₂ adsorption energy of -0.105 eV. Therefore, it is anticipated that P4̄m2 MgH₂ sheets can be employed effectively as a medium for hydrogen storage. Additionally, this finding indicates that a deep learning-based approach is beneficial for exploring unrevealed 2D materials.

SUBMITTER: Lee J

PROVIDER: S-EPMC9418009 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Unveiling two-dimensional magnesium hydride as a hydrogen storage material via a generative adversarial network.

Lee Junho J Sung Dongchul D Chung You Kyoung YK Song Seon Bin SB Huh Joonsuk J

Nanoscale advances 20220408 10

This study used an artificial intelligence (AI)-based crystal inverse-design approach to investigate the new phase of two-dimensional (2D) pristine magnesium hydride (Mg x H y ) sheets and verify their availability as a hydrogen storage medium. A 2D binary phase diagram for the generated crystal images was constructed, which was used to identify significant 2D crystal structures. Then, the electronic and dynamic properties of the Mg x H ...[more]

PMID: 36133700

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Project description:Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine [Formula: see text] receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

Dataset Information

Unveiling two-dimensional magnesium hydride as a hydrogen storage material via a generative adversarial network.

Publications

Unveiling two-dimensional magnesium hydride as a hydrogen storage material <i>via</i> a generative adversarial network.

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