DoE‐It‐Yourself (DoEIY): An Open‐Access Web Application for Democratizing Experimental Design in Chemical and Materials Research

Achieving predictable and reproducible outcomes is a central challenge across synthetic workflows. Design of Experiments (DoE) offers a structured, multivariate framework for exploring complex parameter spaces, yet its wider adoption has been limited by statistical complexity, licensing costs, and steep learning curves. To address these barriers, we introduce DoEIY.app, an open-access web application that streamlines the experimental design process. The software supports guided design generation, data entry and analysis, and interactive model exploration through an intuitive interface. We illustrate its use in two case studies on gold nanoparticle synthesis, focusing on minimizing size dispersity and controlling mean particle diameter. These examples demonstrate how DoEIY.app enables efficient, reproducible process optimization and highlight its potential to democratize DoE implementation across a broad range of scientific domains.

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Publication Details

Journal
Small Methods
Published
2026-01-21
DOI
https://doi.org/10.1002/smtd.202501779
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

DoE‐It‐Yourself (DoEIY): An Open‐Access Web Application for Democratizing Experimental Design in Chemical and Materials Research

Stefan Guldin, Niamh Mac Fhionnlaoich, Federico Galvanin, Yang Ye et al.
Small Methods
Machine Learning in Materials Science
article

DoE‐It‐Yourself (DoEIY): An Open‐Access Web Application for Democratizing Experimental Design in Chemical and Materials Research

Stefan Guldin, Niamh Mac Fhionnlaoich, Federico Galvanin, Yang Ye, Runzhang Qi
article en

Abstract

Achieving predictable and reproducible outcomes is a central challenge across synthetic workflows. Design of Experiments (DoE) offers a structured, multivariate framework for exploring complex parameter spaces, yet its wider adoption has been limited by statistical complexity, licensing costs, and steep learning curves. To address these barriers, we introduce DoEIY.app, an open-access web application that streamlines the experimental design process. The software supports guided design generation, data entry and analysis, and interactive model exploration through an intuitive interface. We illustrate its use in two case studies on gold nanoparticle synthesis, focusing on minimizing size dispersity and controlling mean particle diameter. These examples demonstrate how DoEIY.app enables efficient, reproducible process optimization and highlight its potential to democratize DoE implementation across a broad range of scientific domains.

Small MethodsVol. 10(4)
First People's Hospital of Yuhang District (CN), Aptiv (Ireland) (IE), The London College (GB), Leibniz-Institute for Food Systems Biology at the Technical University of Munich (DE), CREATe Centre (GB), University College London (GB), Technical University of Munich (DE)
Openalex Percentile: Top 81%
Machine Learning in Materials Science
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