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Updated: Sep 10, 2026
11:25
Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
Published on: March 7, 2022
High-throughput computational screening and thermodynamics-informed machine learning for lithium-based ceramic
Zhihan Shen1, Hongjian Tang1, Yang Yang1
1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 211189, China. tanghongjian@seu.edu.cn.
Chemical Communications (Cambridge, England)
|September 9, 2026
Abstract:
First-principles thermodynamic screening identified 120 reversible carbonation reactions of lithium-based ceramic sorbents for high-temperature CO2 capture. A reaction-level graph neural network with transfer learning was proposed that efficiently predicted the temperature dependence of the carbonation reaction.
