# SOLIS: Autonomous Solubility Screening using Deep Neural Networks

arXiv: https://arxiv.org/abs/2203.10970 · HTML: https://mlsanity.com/paper/2203.10970/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)

## Built on by (most cited first, 6 of 6)

- [One-shot recognition of any material anywhere using contrastive learning with physics-based rendering](https://mlsanity.com/paper/2212.00648/) (2022)
- [ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry](https://mlsanity.com/paper/2305.14177/) (2023)
- [Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data](https://mlsanity.com/paper/2403.03309/) (2024)
- [Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping](https://mlsanity.com/paper/2209.14875/) (2022)
- [Automated Solubility Analysis System and Method Using Computer Vision and Machine Learning](https://mlsanity.com/paper/2304.12972/) (2023)
- [Human-Aware Robot Behaviour in Self-Driving Labs](https://mlsanity.com/paper/2603.08420/) (2026)

## Abstract (arXiv)

Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and higher throughput. However, it is common that sample preparation is still carried out manually. This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data. Crystallisation experiments are common in many chemical fields, both for purification and in polymorph screening experiments. The initial step often involves a solubility screen of the molecule; that is, understanding whether molecular compounds have dissolved in a particular solvent. This usually can be time consuming and work intensive. Moreover, accurate knowledge of the precise solubility limit of the molecule is often not required, and simply measuring a threshold of solubility in each solvent would be sufficient. To address this, we propose a novel cascaded deep model that is inspired by how a human chemist would visually assess a sample to determine whether the solid has completely dissolved in the solution. In this paper, we design, develop, and evaluate the first fully autonomous solubility screening framework, which leverages state-of-the-art methods for image segmentation and convolutional neural networks for image classification. To realise that, we first create a dataset comprising different molecules and solvents, which is collected in a real-world chemistry laboratory. We then evaluated our method on the data recorded through an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator, and show that our model can achieve 99.13% test accuracy across various setups.