Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Vir Karan, Max C. Gallant, Yuxing Fei, Gerbrand Ceder, Kristin A. Persson
| Summary:
Establishing viable solid-state synthesis pathways for novel inorganic
materials remains a major challenge in materials science. Previous pathway
design methods using pair-wise reaction approaches have navigated the
thermodynamic landscape with first-principles data but lack kinetic
information, limiting their effectiveness. This gap leads to suboptimal
precursor selection and predictions, especially for reactions forming competing
phases with similar formation energies, where ion diffusion is a critical
influence. Here, we demonstrate an inorganic synthesis framework by
incorporating machine learning-derived transport properties through
"liquid-like" product layers into a thermodynamic cellular reaction model. In
the Ba-Ti-O system, known for its competitive polymorphism, we obtain accurate
predictions of phase formation with varying BaO:TiO2 ratios as a function of
time and temperature. We find that diffusion-thermodynamic interplay governs
phase compositions, with cross-ion transport coefficients critical for
predicting diffusion-limited selectivity. This work bridges length and time
scales by integrating solid-state reaction kinetics with first-principles
thermodynamics and spatial reactivity.
| Search Query: ArXiv Query: search_query=au:”Kristin A. Persson”&id_list=&start=0&max_results=3