7 papers · 1 filter
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…
Synthetic accessibility and stability rules of NASICONs
Bin Ouyang, Jingyang Wang, Tanjin He +6
In this paper we develop the stability rules for NASICON structured materials, as an example of compounds with complex bond topology and composition. By applying machine learning t…
Alloying behavior of wide band gap alkaline-earth chalcogenides
Samantha L. Millican, Jacob M. Clary, Christopher J. Bartel +3
Alloying is a powerful tool for tuning materials that facilitates the targeted design of desirable properties for a variety of applications. In this work, we provide a comprehensiv…
A critical examination of compound stability predictions from machine-learned formation energies
Christopher J. Bartel, Amalie Trewartha, Qi Wang +3
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of…
The role of decomposition reactions in assessing first-principles predictions of solid stability
Christopher J. Bartel, Alan W. Weimer, Stephan Lany +2
The performance of density functional theory (DFT) approximations for predicting materials thermodynamics is typically assessed by comparing calculated and experimentally determine…
A Map of the Inorganic Ternary Metal Nitrides
Wenhao Sun, Christopher Bartel, Elisabetta Arca +12
Exploratory synthesis in novel chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials s…