Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential

Hiroki Chikuma, Gentoku Takasao, Toru Wada, Patchanee Chammingkwan, Jörg Behler, Toshiaki Taniike

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The determination of catalyst nanostructures with first-principles accuracy using genetic algorithms (GA) is very demanding due to the cubic scaling of the computational cost of density functional theory (DFT) calculations. Here, we demonstrate, for the case of Ziegler–Natta MgCl2/TiCl4 nanoplates, how this structure determination can be accelerated by employing a high-dimensional neural network potential (HDNNP) of essentially DFT accuracy. First, when building HDNNPs for MgCl2/TiCl4 clusters with computationally tractable sizes, we found that the structural diversity in the training set is crucial for obtaining HDNNPs reliably describing the large variety of structures generated by GA. The resulting HDNNPs dramatically accelerated the structure determination while yielding results consistent with DFT. Subsequently, we developed a multistep adaptive procedure to construct a HDNNP for MgCl2/TiCl4 clusters consistent in size and TiCl4 coverage with experiments where prior DFT results were scarcely collected. The structure determination and analyses underline the importance of system size and composition in order to predict some experimentally known facts such as the surface morphology and population of isospecific sites.
Original languageEnglish (US)
JournalThe Journal of Physical Chemistry C
StatePublished - Jun 9 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-06-12
Acknowledgements: The work of T.T. was supported by JSPS Grants-in-Aid for Scientific Research (grant no. JP 22H01865). The work of H.C. is supported by JST SPRING, (grant no. JPMJSP2102). J.B. is grateful for the financial support by the Japan Advanced Institute of Science and Technology for a visiting professorship.

ASJC Scopus subject areas

  • Surfaces, Coatings and Films
  • General Energy
  • Physical and Theoretical Chemistry
  • Electronic, Optical and Magnetic Materials


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