# MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

arXiv: https://arxiv.org/abs/2208.09751 · HTML: https://mlsanity.com/paper/2208.09751/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Artifact Identification in X-ray Diffraction Data using Machine Learning Methods](https://mlsanity.com/paper/2207.14804/) (2022)
- [Graph Model Implementation of Attribute-Based Access Control Policies](https://mlsanity.com/paper/1909.09904/) (2019)

## Built on by (most cited first, 4 of 4)

- [DLSIA: Deep Learning for Scientific Image Analysis](https://mlsanity.com/paper/2308.02559/) (2023)
- [Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop Annotations](https://mlsanity.com/paper/2408.12720/) (2024)
- [Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders](https://mlsanity.com/paper/2606.14999/) (2026)
- [Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films](https://mlsanity.com/paper/2505.23064/) (2025)

## Abstract (arXiv)

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios -- users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.