Computational Analysis of the Energetic Stability of High-Entropy Structures of a Prototypical Lanthanide-Based Metal–Organic Framework

High-entropy materials are characterized by their complex compositions, typically comprising five or more elements in near-equiatomic proportions. Applying this concept to metal ions in metal-organic frameworks (MOFs) has paved the way for exploring a new class of high-entropy MOFs. While the compositional strategy of high-entropy materials leverages configurational entropy to aid thermodynamic stability, it also poses significant analytical challenges due to the vast compositional landscape and diverse phases that these materials can adopt. We present a computational study of several complexities associated with selecting potential high-entropy versions of a prototype lanthanide-based MOF. We compute the energetics of metal mixing of these heterometallic MOFs using density functional theory (DFT) and machine learning interatomic potential (MLIP) methods. The use of MLIP methods allows a systematic exploration of the convex hull of thermodynamically stable MOF structures containing up to 5 distinct metals.

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Journal
The Journal of Physical Chemistry C
Published
2025-10-09
DOI
https://doi.org/10.1021/acs.jpcc.5c04892
Primary Topic
Machine Learning in Materials Science
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article
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article

Computational Analysis of the Energetic Stability of High-Entropy Structures of a Prototypical Lanthanide-Based Metal–Organic Framework

Dorina F. Sava Gallis, David S. Sholl, S. D. Madhu Kumar
The Journal of Physical Chemistry C
Machine Learning in Materials Science
article

Computational Analysis of the Energetic Stability of High-Entropy Structures of a Prototypical Lanthanide-Based Metal–Organic Framework

Dorina F. Sava Gallis, David S. Sholl, S. D. Madhu Kumar
article en

Abstract

High-entropy materials are characterized by their complex compositions, typically comprising five or more elements in near-equiatomic proportions. Applying this concept to metal ions in metal-organic frameworks (MOFs) has paved the way for exploring a new class of high-entropy MOFs. While the compositional strategy of high-entropy materials leverages configurational entropy to aid thermodynamic stability, it also poses significant analytical challenges due to the vast compositional landscape and diverse phases that these materials can adopt. We present a computational study of several complexities associated with selecting potential high-entropy versions of a prototype lanthanide-based MOF. We compute the energetics of metal mixing of these heterometallic MOFs using density functional theory (DFT) and machine learning interatomic potential (MLIP) methods. The use of MLIP methods allows a systematic exploration of the convex hull of thermodynamically stable MOF structures containing up to 5 distinct metals.

The Journal of Physical Chemistry CVol. 129(42)
Oak Ridge Associated Universities (US), Oak Ridge National Laboratory (US), Georgia Institute of Technology (US), Nanosc (Sweden) (SE), Sandia National Laboratories (US), Institute of Chemical Engineering (BG), University of Tennessee at Knoxville (US)
Openalex Percentile: Top 78%
Machine Learning in Materials Science
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Computational Analysis of the Energetic Stability of High-Entropy Structures of a Prototypical Lanthanide-Based Metal–Organic Framework — Dorina F. Sava Gallis, David S. Sholl, et al. · The Journal of Physical Chemistry C (2025) | TGRS Research Map | TGRS