12 papers · 1 filter
Scalable machine learning framework for multiphase identification from powder X-ray diffraction
Xinyang Tong, Ethan Jin, Jiahan Xu +3
X-ray diffraction (XRD) is the primary tool for identifying crystalline phases following synthesis, but automated phase identification remains challenging, particularly for multiph…
Thermodynamic assessment of machine learning models for solid-state synthesis prediction
Jane Schlesinger, Simon Hjaltason, Nathan J. Szymanski +1
Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles model…
Cation vacancies mediate thermochemical water splitting with iron aluminates
Nathan J. Szymanski, Kent J. Warren, Alan W. Weimer +1
Solar thermochemical water splitting enables hydrogen production by cycling metal oxides between reduced and oxidized states, typically through an oxygen vacancy mechanism. However…
Thermodynamics of proton insertion across the perovskite-brownmillerite transition in La0.5Sr0.5CoO3-δ
Armand J. Lannerd, Nathan J. Szymanski, Christopher J. Bartel
LaSrCoO3- is a promising off-stoichiometric metal oxide that undergoes a topotactic perovskite ( = 0) to brownmillerite ( = 0.5) transition under electrochem…
Computational search for materials having a giant anomalous Hall effect in the pyrochlore and spinel crystal structures
Sean Sullivan, Seungjun Lee, Nathan J. Szymanski +4
Ferromagnetic pyrochlore and spinel materials with topological flat bands are of interest for their potential to exhibit a giant anomalous Hall effect (AHE). In this work, we prese…
Topological descriptors for the electron density of inorganic solids
Nathan J. Szymanski, Alexander Smith, Prodromos Daoutidis +1
Descriptors play an important role in data-driven materials design. While most descriptors of crystalline materials emphasize structure and composition, they often neglect the elec…