Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data.

By Ekin Dogus Cubuk, Austin D. Sendek, Evan J. Reed. 2019

118 citations recorded by the source corpus.

Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are not available for materials design applications. Here, we first show that a standard learning approach using generic descriptors does not work for small data, unless it is guided by insights from physical equations. We then propose a novel method for transferring such physical insights onto more generic descriptors, allowing us to screen billions of unknown compositions for Li-ion conductivity, a scale which was previously unfeasible. This is accomplished by using the accurate model trained with physical insights to create a large database, on which we train a new ML model using the generic descriptors. Unlike previous applications of ML, this approach allows us to screen materials which have not necessarily been tested before (i.e., not on ICSD or Materials Project). Our method can be applied to any materials design application where a small amount of data is available, combined with high details of physical understanding.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Materials Chemistry, Representation & Self-Supervised Learning

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Citations in the corpus

ScaleNet: An Unsupervised Representation Learning Method for Limited Information (2023, background): “However, collecting and labeling the large-scale training dataset is a very time-consuming and expensive task in fields such as neuroscience [42], medical diagnosis [40], material science [9], and chemistry application [17].”
Machine learning for a sustainable energy future (2022, background): “46,47 Flow batteries consist of active materials dissolved in electrolytes that flow into a cell with electrodes that facilitate redox reactions.”