A genetic algorithmic approach to determine the structure of Li-Al layered double hydroxides

Josiah Roberts, Yang Song, Mark Crocker, Chad Risko

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Layered double hydroxides (LDH) demonstrate significant potential across a range of applications, including as catalysts, delivery vehicles for pharmaceuticals, environmental remediation, and supercapacitors. Explaining the mechanism of LDH action at the atomic scale in these and other applications is challenging, however, due to the difficulty in precisely defining the bulk and surface structure and chemical compositions. Here, we focus on the determination of the structure of lithium-aluminum (Li-Al) LDH, which has shown promise in the catalytic depolymerization of lignin, both directly as the catalyst and as a support for gold nanoparticles. While the relative positions of the Li and Al metals are generally well resolved by X-ray crystallography, it is the structures of the anionic layers, consisting of water and carbonate, that are less well established. Combinatorial analyses of all possible positions and rotations of the water and carbonate in the three-layered Li-AL LDH polytope reveals that the phase space is much too large to examine in any reasonable time frame in a one-by-one structure exploration. To overcome this limitation, we develop and deploy a genetic algorithm (GA) wherein fitness is determined by matching a calculated X-ray diffraction (XRD) pattern for a given structure to the known experimental XRD pattern. The GA approach results in structures of high fitness that portend the bulk Li-Al LDH structure. Importantly, the GA approach offers the potential to determine the structures of other LDH, and more generally layered materials, which are generally difficult to describe given the large chemical and structural space to be explored.

Original languageEnglish
Pages (from-to)4845-4855
Number of pages11
JournalJournal of Chemical Information and Modeling
Issue number10
StatePublished - Oct 26 2020

Bibliographical note

Funding Information:
C.R. and J.R. acknowledge funding by the Research Corporation for Science Advancement (RCSA) Cottrell Scholars Program (Award No. 24432). Computing resources on the Lipscomb High Performance Computing Cluster were provided by the University of Kentucky, Information Technology Department, and the Center for Computational Sciences (CCS). C.R. and J.R. also thank Dr. Karol Jarolimek and Dr. Qianxiang Ai for insightful discussions and contributions to the modeling effort. M.C. and Y.S. acknowledge funding by the National Science Foundation under Cooperative Agreement No. 1355438.

Publisher Copyright:
© 2020 American Chemical Society.

ASJC Scopus subject areas

  • Chemistry (all)
  • Chemical Engineering (all)
  • Computer Science Applications
  • Library and Information Sciences


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