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Over recent years, great efforts have been made to push the limits of layered transition metal oxides for secondary battery cathodes. This is particularly true for overall capacity, which has reached a terminal theoretical value for many materials. One avenue for increasing this capacity during charging is the intercalation of anions post cation deintercalation. This work investigates the charging mechanism of the P3-Na0.5Ni0.25 Mn0.75O2 cathode material through cation (Na) deintercalation and anion (ClO4) intercalation by means of density functional theory. The calculations corroborate experimental findings of increased capacity (135 mAh g-1 to 180 mAh g-1) through the intercalation of anions. However, this work demonstrates that a process of simultaneous cation deintercalation/anion intercalation is the primary charging mechanism, with charge compensation reactions of Ni2+/Ni4+ and O2-/O- occurring within the cathode material. To elucidate this simultaneous process, a novel method for computationally determining anion voltage in which one must consider full electrolyte interactions is proposed. Based on the results, it is believed that a simultaneous cation deintercalation/anion intercalation mechanism provides one potential avenue for the discovery of the next generation of secondary batteries.
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Machine Learning Prediction of Heat Capacity for Solid Inorganics
Abstract Many thermodynamic calculations and engineering applications require the temperature-dependent heat capacity (Cp) of a material to be known a priori. First-principle calculations of heat capacities can stand in place of experimental information, but these calculations are costly and expensive. Here, we report on our creation of a high-throughput supervised machine learning-based tool to predict temperature-dependent heat capacity. We demonstrate that material heat capacity can be correlated to a number of elemental and atomic properties. The machine learning method predicts heat capacity for thousands of compounds in seconds, suggesting facile implementation into integrated computational materials engineering (ICME) processes. In this context, we consider its use to replace Neumann-Kopp predictions as a high-throughput screening tool to help identify new materials as candidates for engineering processes. Also promising is the enhanced speed and performance compared to cation/anion contribution methods at elevated temperatures as well as the ability to improve future predictions as more data are made available. This machine learning method only requires formula inputs when calculating heat capacity and can be completely automated. This is an improvement to common best-practice methods such as cation/anion contributions or mixed-oxide approaches which are limited in application to specific materials and require case-by-case considerations.
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- Award ID(s):
- 1651668
- PAR ID:
- 10224577
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Integrating Materials and Manufacturing Innovation
- Volume:
- 7
- Issue:
- 2
- ISSN:
- 2193-9764
- Page Range / eLocation ID:
- p. 43-51
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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