Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications

Dai, Donghai; Liu, Qing; Hu, Rui; Wei, Xiao; Ding, Guofu; Xu, Bin; Xu, Tao; Zhang, Jincang; Xu, Yan; Zhang, Huiran

doi:10.1016/j.matdes.2020.109194

Cited by 26 publications

(12 citation statements)

References 45 publications

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“…We therefore followed a semi-empirical approach in this work, similar to the work of Xu et al [57], with experimental data as the essential basis, combined with a deep analysis of the available data before they became part of the Pecon.py program and the resulting training data (Figure 1). We paid particular attention to structure-property relations [58] and limited ourselves to a maximum of five different pre-selected cations at the A and B positions, as well as two physical quantities to be predicted (electronic and ionic conductivity). Searching the periodic table for suitable chemical compositions was explicitly not our goal.…”

Section: Discussionmentioning

confidence: 99%

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

et al. 2022

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The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications.

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Section: Discussionmentioning

confidence: 99%

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

et al. 2022

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“…The modeling speed of linear regression is rapid, it does not need sophisticated calculation, and it may even run quickly when dealing with huge amounts of data [48] . The gained linear expression can be understood and interpreted according to the coefficient of each variable, and the influence of each feature on the result can be directly seen from the weight, which is much easier to grasp [43,49] . Nonlinear expressions are more complex than other machine learning methods, and the related process is difficult to learn [48] .…”

Section: Regressionmentioning

confidence: 99%

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin¹,

Lin²

2022

Energy Mater

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“…68 The pool can be expanded by using feature-engineering to construct new candidate descriptors through, e.g., arithmetic combinations of the current descriptors in the pool. [65][66][67][68] Both supervised and unsupervised methods have been developed to reduce this pool. Supervised learning methods sift through the candidate pool and select only those descriptors that most significantly correlate with the property of interest.…”

Section: Forward Predictive Modelingmentioning

confidence: 99%

Machine learning-assisted design of material properties

Kadulkar,

Sherman,

Ganesan

et al. 2022

Preprint

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Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time-and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. Here, we review how to leverage machine learning to reduce dimensionality to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.

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Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications

Cited by 26 publications

References 45 publications

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Machine learning-assisted design of material properties

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