Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Rachel Woods-Robinson, Matthew K. Horton, Kristin A. Persson, ,
| Summary:
Conventionally, high-throughput computational materials searches start from
an input set of bulk compounds extracted from material databases, and this set
is screened for candidate materials for specific applications. In contrast,
many functional materials, and especially semiconductors, are heavily
engineered alloys of multiple compounds rather than a single bulk compound. To
improve our ability to design functional materials, in this work we propose a
framework to automatically construct possible "alloy pair" and "alloy systems"
and detect "alloy members" from a set of existing, experimental or calculated
ordered compounds, without requiring any additional metadata beyond their
crystal structure. As a demonstration, we apply this framework to all inorganic
materials in the Materials Project database to create a new database of over
600,000 unique "alloy pair" entries which can then be used in materials
discovery studies to search for materials with tunable properties. This new
database has been incorporated into the Materials Project website and linked
with corresponding material identifiers for any user to query and explore.
Using an example of screening for p-type transparent conducting materials, we
demonstrate how using this methodology reveals candidate material systems that
might otherwise have been excluded by a traditional screening. This work lays a
foundation from which materials databases can go beyond stoichiometric
compounds, and approach a more realistic description of compositionally tunable
materials.
| Search Query: ArXiv Query: search_query=au:”Kristin A. Persson”&id_list=&start=0&max_results=10