106 citations · 242 across the 13 of their papers we have counts for
13 papers
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…
Establishing baselines for generative discovery of inorganic crystals
Nathan J. Szymanski, Christopher J. Bartel
Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and ben…
AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories
Yuxing Fei, Bernardus Rendy, Rishi Kumar +10
The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation.…
Selective formation of metastable polymorphs in solid-state synthesis
Yan Zeng, Nathan J. Szymanski, Tanjin He +6
Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a l…
Autonomous decision making for solid-state synthesis of inorganic materials
Nathan J. Szymanski, Pragnay Nevatia, Christopher J. Bartel +2
To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target…
A universal synthetic dataset for machine learning on spectroscopic data
Jan Schuetzke, Nathan J. Szymanski, Markus Reischl
To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for mo…
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng +2
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilist…
Electronic, optical and thermoelectric properties of sodium pnictogen chalcogenides: A first principles study
I. S. Khare, N. J. Szymanski, D. Gall +1
Ternary chalcogenides have been of recent investigation for applications in photovoltaic and thermoelectric devices. We study the structural, electronic, optical, and thermoelectri…