Environmental Stability of Crystals: A Greedy Screening

Identifying materials that maintain stability in environmental conditions while fulfilling functional requirements remains a challenge. Prior research has shown that thermodynamic stability is a key factor in material performance. However, no prior work had resolved how to efficiently screen large datasets for environmental stability. Existing methods often rely on energy above the hull metrics, which are limited to inert conditions. This gap motivated the development of a new screening algorithm. The approach needed to balance computational efficiency with accuracy. Researchers aimed to expand beyond vacuum conditions to real-world environments. The Pilling-Bedworth ratio is a known criterion for self-passivation. But its application to large-scale datasets had not been fully explored.

The goal was to create a rapid screening method for environmental stability in materials. The focus was on thermodynamic stability under common environmental conditions. The study aimed to test the algorithm against existing metrics like the energy above the hull. The researchers wanted to assess accuracy using a large dataset of crystals. They also sought to evaluate performance in oxygen-rich environments. The algorithm was designed to estimate decomposition driving forces. The team aimed to determine if the method could identify stable materials. The study also aimed to explore the potential for self-passivation in 39,654 materials.

The team developed a greedy algorithm to assess thermodynamic stability. The algorithm was tested using 126,320 crystal structures as input data. The standard energy above the hull metric served as a benchmark. The mean absolute error was calculated to evaluate accuracy. The method was applied to in-oxygen stability for 39,654 materials. The enthalpy of oxidation was computed for each material. The Pilling-Bedworth ratio was used to assess self-passivation. The researchers compared results against known stability criteria.

The greedy algorithm achieved a mean absolute error of 39.5 meV/atom. This level of accuracy is sufficient for identifying stable materials. The method outperformed the energy above the hull metric in computational speed. The algorithm was tested on a dataset of 126,320 crystal structures. In oxygen-rich conditions, the enthalpy of oxidation was largely exothermic. The analysis identified 1438 materials with potential for self-passivation. These materials met the Pilling-Bedworth ratio criteria. The results suggest the algorithm is effective for environmental screening.

The greedy algorithm provides a rapid and accurate method for environmental stability screening. The approach is suitable for large-scale datasets of crystal structures. The results align with the authors' claim about the algorithm's resolution. The method's performance in oxygen-rich conditions supports its utility. The Pilling-Bedworth ratio analysis highlights potential for self-passivation. The findings suggest the algorithm can identify stable materials efficiently. The study confirms the algorithm's ability to estimate decomposition driving forces. The authors propose the method as a tool for materials discovery.