We encourage the microfluidic community to share their data-sets of design-parameters and observed performance on microfluidic components (can be any microfluidic component, such as droplet generator, micro-mixers, droplet sorters, cell-traps, etc.) with us and other researchers in the field to further expand DAFD. This would enable us to build new predictive models using DAFD Neural Optimizer and expand DAFD to support new microfluidic components and fluid combinations. If you already have a published data-set or a private data-set and want to contribute to the new era of microfluidic design automation contact us at lashkari@bu.edu or dougd@bu.edu to arrange a collaboration with proper attributions if you want to proceed with publishing the results.

The open-source nature of DAFD, alongside with DAFD Neural Optimizer allows you to build your own custom version of DAFD even if you are not an expert in machine learning or coding. If you already have a data-set you can easily train accurate predictive neural networks on your data-set using DAFD Neural Optimizer. You can download the models generated by DAFD Neural Optimizer and replace the default models in the current version of local DAFD, to built your own custom design automation tool. If you also want us to include your custom built design automation tool with proper attributions to you in the online version of DAFD contact us at lashkari@bu.edu or dougd@bu.edu.