posted on 2021-07-06, 17:05authored byAlexandru
B. Georgescu, Peiwen Ren, Aubrey R. Toland, Shengtong Zhang, Kyle D. Miller, Daniel W. Apley, Elsa A. Olivetti, Nicholas Wagner, James M. Rondinelli
Metal–insulator
transition (MIT) compounds are materials
that may exhibit metallic or insulating behavior, depending on the
physical conditions, and are of immense fundamental interest owing
to their potential applications in emerging microelectronics. An important
subset of MIT materials are those with a transition driven by temperature.
The number of thermally driven MIT materials, however, is scarce,
which makes delineating these compounds from those that are exclusively
insulating or metallic challenging. Most research that addresses thermal
MITs is limited by the domain knowledge of the scientists to a subset
of MIT materials and is often focused on a limited subset of possible
features. Here, using a combination of domain knowledge and natural
language processing (NLP) searches, we have built a material database
comprising thermally driven MITs as well as metals and insulators
with similar chemical composition and stoichiometries to the MIT compounds.
We featurized this data set using a wide variety of compositional,
structural, and energetic descriptors, including two MIT relevant
energy scales, the estimated Hubbard interaction and the charge transfer
energy, as well as the structure-bond-stress metric referred to as
the global-instability index (GII). We then performed supervised classification
on this data set, constructing three electronic-state classifiers:
metal vs nonmetal (M), insulator vs noninsulator (I), and MIT vs non-MIT
(T). This classification allows us to identify new features separating
MIT materials from non-MIT materials. These include the 2D feature
space consisting of the average deviation of the covalent radius,
the range of the Mendeleev number and Ewald energy. We discuss the
relationship of these atomic features to the physical interactions
underlying MITs in the rare-earth nickelate family. We then elaborate
on other features (GII and Ewald energy) and examine how they affect
the classification of binary vanadium and titanium oxides. Last, we
implement an online and publicly accessible version of the classifiers,
enabling quick probabilistic class predictions by uploading a crystallographic
structure file. The broad accessibility of our database, newly identified
features, and user-friendly classifier models will aid in accelerating
the discovery of MIT materials.