eprintid: 10193135 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/19/31/35 datestamp: 2024-06-07 09:10:47 lastmod: 2024-06-07 09:10:47 status_changed: 2024-06-07 09:10:47 type: article metadata_visibility: show sword_depositor: 699 creators_name: Wang, Fanjin creators_name: Harker, Anthony creators_name: Edirisinghe, Mohan creators_name: Parhizkar, Maryam title: Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray ispublished: inpress divisions: UCL divisions: B02 divisions: B04 divisions: C08 divisions: C05 divisions: C06 divisions: D10 divisions: F45 divisions: G08 divisions: F60 keywords: Active learning; machine learning; materials development; materials discovery; small data note: Copyright © 2024 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. abstract: Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show several disadvantages: one-factor-at-a-time (OFAT) experimentation became impractical due to limited laboratory resources; conventional design of experiments (DoE) failed to incorporate high-dimensional features in ML; Surrogate-based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training data accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, query-by-Committee (QBC) algorithm and Greedy method, are benchmarked against random query baseline on various materials datasets. AL is shown to efficiently reduce model prediction errors with minimal additional experiment data. Investigation of hyperparameters revealed benefits of applying AL at an early stage of experimental dataset construction. Moreover, AL is implemented and validated for an in-house materials development task - electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulation purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers. date: 2024-05-23 date_type: published publisher: Wiley official_url: http://dx.doi.org/10.1002/aisy.202300798 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2279048 doi: 10.1002/aisy.202300798 lyricists_name: Wang, Fanjin lyricists_name: Parhizkar, Maryam lyricists_name: Harker, Anthony lyricists_name: Edirisinghe, Mohan lyricists_id: FWANA07 lyricists_id: MPARH34 lyricists_id: AHHAR69 lyricists_id: MJEDE48 actors_name: Basit, Abdul actors_name: Harris, Jean actors_id: ABASI56 actors_id: JAHAR68 actors_role: owner actors_role: impersonator full_text_status: public publication: Advanced Intelligent Systems article_number: 2300798 issn: 2640-4567 citation: Wang, Fanjin; Harker, Anthony; Edirisinghe, Mohan; Parhizkar, Maryam; (2024) Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray. Advanced Intelligent Systems , Article 2300798. 10.1002/aisy.202300798 <https://doi.org/10.1002/aisy.202300798>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10193135/1/Advanced%20Intelligent%20Systems%20-%202024%20-%20Wang.pdf