Queraltó A, Pacheco A, Jiménez N, Ricart S, Obradors X, Puig T. Defining inkjet printing conditions of superconducting cuprate films through machine learning.
JOURNAL OF MATERIALS CHEMISTRY. C 2022;
10:6885-6895. [PMID:
35665056 PMCID:
PMC9069570 DOI:
10.1039/d1tc05913k]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/06/2022] [Indexed: 05/13/2023]
Abstract
The design and optimization of new processing approaches for the development of rare earth cuprate (REBCO) high temperature superconductors is required to increase their cost-effective fabrication and promote market implementation. The exploration of a broad range of parameters enabled by these methods is the ideal scenario for a new set of high-throughput experimentation (HTE) and data-driven tools based on machine learning (ML) algorithms that are envisaged to speed up this optimization in a low-cost and efficient manner compatible with industrialization. In this work, we developed a data-driven methodology that allows us to analyze and optimize the inkjet printing (IJP) deposition process of REBCO precursor solutions. A dataset containing 231 samples was used to build ML models. Linear and tree-based (Random Forest, AdaBoost and Gradient Boosting) regression algorithms were compared, reaching performances above 87%. Model interpretation using Shapley Additive Explanations (SHAP) revealed the most important variables for each study. We could determine that to ensure homogeneous CSD films of 1 micron thickness without cracks after the pyrolysis, we need average drop volumes of 190-210 pl, and no. of drops between 5000 and 6000, delivering a total volume deposited close to 1 μl.
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