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Figure 2. AI workflow and case studies with geometric tolerance factor. (A) General workflow incorporating geometric T f for AI-driven
materials discovery. Unlike standard approaches, this method eliminates the need for geometric optimization of unknown crystal
structures prior to property prediction (the red dotted arrow); (B) Garnet discovery with T f; (C) Chalcogenide discovery with T f.
HIGH-THROUGHPUT MATERIALS SCREENING WITH TOLERANCE FACTORS
At this stage, modeling with T f diverges slightly from typical methodologies [Figure 2A]. For instance, when
employing T f for high-throughput screening, researchers first define a specific system, to ensure that all
investigated materials adhere to consistent atomic arrangement criteria. The next step involves constructing
a dataset comprising site-specific elemental compositions and their associated properties. Leveraging the
known combinations, researchers can generate unexplored derivative configurations for further
investigation.
In systems characterized by a consistent atomic arrangement, descriptors for AI modeling can be constructed
