Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation

文献信息

发布日期 2023-11-21
DOI 10.1039/D3CP04017H
影响因子 3.676
作者

Kevin P. McGuinness, Anton O. Oliynyk, Sangjoon Lee, Beatriz Molero-Sanchez, Paul Kwesi Addo


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摘要

Perovskite oxides have been of high-interest and relatively well studied over the last 20 years due to their various applications, specifically for solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs). One of the key properties for a perovskite to perform well as a component in SOFCs, SOECs, and other high-temperature applications is its thermal expansion coefficient (TEC). The use of machine learning (ML) to predict material properties has greatly increased over the years and has proven to be a very useful tool for materials screening. The process of synthesizing and testing perovskite oxides is laborious and costly, and the use of physics-based models is often highly computationally expensive. Due to the amount of elements able to be accommodated in the ABO3 structure and the ability for crystallographic mixing in both the A and B-sites, there are a massive amount of possible ABO3 perovskites. In this paper, a ML model for the prediction of the TECs of AA′BB′O3 perovskites is produced and applied to millions of potential compositions resulting in reliable TEC predictions for 150 451 of the compositions.

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来源期刊

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
自引率: 10.3%
年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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