Hydrogen in Disordered Titania: Connecting Local Chemistry, Structure, and Stoichiometry through Accelerated Exploration
James Chapman, Kyoung E. Kweon, Yakun Zhu, Kyle Bushick and, Leonardus Bimo Bayu Aji, Christopher Colla, Nir Goldman, Nathan, Keilbart, Roger Qui, Tae Wook Heo, Brandon C. Wood

TL;DR
This paper presents a machine learning and density functional theory workflow to analyze how local chemistry and structure influence hydrogen binding in disordered titania, aiding material design for hydrogen-related applications.
Contribution
It introduces an integrated computational approach combining machine learning, DFT, and graph-based sampling to study hydrogen interactions in complex disordered oxides.
Findings
Hydrogen binding energies depend on local oxygen coordination.
Stoichiometry influences local oxygen environments and hydrogen energetics.
Methodology enables tailored material design for hydrogen environments.
Abstract
Hydrogen incorporation in native surface oxides of metal alloys often controls the onset of metal hydriding, with implications for materials corrosion and hydrogen storage. A key representative example is titania, which forms as a passivating layer on a variety of titanium alloys for structural and functional applications. These oxides tend to be structurally diverse, featuring polymorphic phases, grain boundaries, and amorphous regions that generate a disparate set of unique local environments for hydrogen. Here, we introduce a workflow that can efficiently and accurately navigate this complexity. First, a machine learning force field, trained on ab initio molecular dynamics simulations, was used to generate amorphous configurations. Density functional theory calculations were then performed on these structures to identify local oxygen environments, which were compared against…
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Taxonomy
TopicsMachine Learning in Materials Science · Catalysis and Hydrodesulfurization Studies · Hydrogen Storage and Materials
