Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal-Organic Frameworks.

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability.

Exploring the large chemical space in search of thermodynamically stable and mechanically robust MXenes machine learning.

To effectively utilize MXenes, a family of two-dimensional materials, in various applications that include thermoelectric devices, semiconductors, and transistors, their thermodynamic and mechanical properties, which are closely related to their stability, must be understood. However, exploring the large chemical space of MXenes and verifying their stability using first-principles calculations are computationally expensive and inefficient. Therefore, this study proposes a machine learning (ML)-based high-throughput MXene screening framework to identify thermodynamically stable MXenes and determine their mechanical properties.