Dataset Information

Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics.

ABSTRACT: Microkinetic analysis based on density functional theory (DFT) was combined with a generative adversarial network (GAN) to enable the artificial proposal of heterogeneous catalysts based on the DFT-calculated dataset. The approach was applied to the NH₃ formation reaction on Rh-Ru alloy surfaces as an example. The NH₃ formation turnover frequency (TOF) was calculated by DFT-based microkinetics. Six elementary reactions, namely, N₂ dissociation, H₂ dissociation, NH_x (x = 1-3) formation, and NH₃ desorption, were explicitly considered, and their reaction energies were evaluated by DFT calculations. Based on the TOF values and atomic compositions, new alloy surfaces were generated using the GAN. This approach successfully generated the surfaces that were not included in the initial dataset but exhibited higher TOF values. The N₂ dissociation reaction was more exothermic for the generated surfaces, leading to higher TOF. The present study demonstrates that the automatic improvement of catalyst materials is possible using DFT calculations and GAN sample generation.

SUBMITTER: Ishikawa A

PROVIDER: S-EPMC9270484 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics.

Ishikawa Atsushi A

Scientific reports 20220708 1

Microkinetic analysis based on density functional theory (DFT) was combined with a generative adversarial network (GAN) to enable the artificial proposal of heterogeneous catalysts based on the DFT-calculated dataset. The approach was applied to the NH3 formation reaction on Rh-Ru alloy surfaces as an example. The NH3 formation turnover frequency (TOF) was calculated by DFT-based microkinetics. Six elementary reactions, namely, N2 dissociation, H2 diss ...[more]

PMID: 35803991

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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

Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics.

Publications

Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics.

Similar Datasets

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