Abstract
Layered intercalation compounds, where atoms or molecules (intercalants) are inserted into layered materials (hosts), hold great potential for diverse applications. However, the lack of a systematic understanding of stable host-intercalant combinations poses challenges in materials design due to the vast combinatorial space. In this study, we performed first-principles calculations on 9,024 compounds, unveiling a novel linear regression equation based on the hard and soft acids and bases principle. This equation, incorporating intercalant ion formation energy and ionic radius, quantitatively reveals the stability factors. Additionally, employing machine learning, we predicted regression coefficients from host properties, offering a comprehensive understanding and a predic-tive model for estimating intercalation energy. Our work provides valuable insights into the energetics of layered intercalation compounds, facilitating targeted materials design.
Supplementary materials
Title
Supporting Information
Description
Structures of layered intercalation compounds, comparison of total energy in different energy convergence thresholds, results of regression in other models, prediction of Eint by machine learning machine, comparison of the vdW correction methods, the values of 〖〖∆G〗^°〗_(f,M^n ) and r_(M^n ) of intercalant, the conditions of random forest regression, and information about the .xlsx file of Supporting Information. (PDF)
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Title
Supporting Information database
Description
The values of Eint, Ef of all layered intercalation compounds, Adj. R2, α_(M"-" Host), β_(M"-" Host), and γ_(M"-" Host) of each layered intercalation compounds with same host and stacking, the value of descriptors for random forest regressions.
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