Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Haiwen Dai, Haiwen Dai, , ,
| Summary:
Data-driven methodologies hold the promise of revolutionizing inorganic
materials discovery, but they often face challenges due to discrepancies
between theoretical predictions and experimental validation. In this work, we
present an end-to-end discovery framework that leverages synthesizability,
oxidation state probability, and reaction pathway calculations to guide the
exploration of transition metal oxide spaces. Two previously unsynthesized
target compositions, ZnVO3 and YMoO3, passed preliminary computational
evaluation and were considered for ultrafast synthesis. Comprehensive
structural and compositional analysis confirmed the successful synthesis ZnVO3
in a partially disordered spinel structure, validated via Density Functional
Theory (DFT). Exploration of YMoO3 led to YMoO3-x with elemental composition
close to 1:1:3; the structure was subsequently identified to be Y4Mo4O11
through micro-electron diffraction (microED) analysis. Our framework
effectively integrates multi-aspect physics-based filtration with in-depth
characterization, demonstrating the feasibility of designing, testing,
synthesizing, and analyzing (DTMA) novel material candidates, marking a
significant advancement towards inorganic materials by design.
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