# Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity

arXiv: https://arxiv.org/abs/2208.10784 · HTML: https://mlsanity.com/paper/2208.10784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 138 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Machine learning for molecular simulation](https://mlsanity.com/paper/1911.02792/) (2019)
- [Uncertainty Quantification Using Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2005.10036/) (2020)
- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [TorchMD: A deep learning framework for molecular simulations](https://mlsanity.com/paper/2012.12106/) (2020)
- [Cross-validation failure: small sample sizes lead to large error bars](https://mlsanity.com/paper/1706.07581/) (2017)
- [Deeptime: a Python library for machine learning dynamical models from time series data](https://mlsanity.com/paper/2110.15013/) (2021)
- [Uncertainty Prediction for Machine Learning Models of Material Properties](https://mlsanity.com/paper/2107.07997/) (2021)
- [Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry](https://mlsanity.com/paper/2010.00110/) (2020)
- [Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction](https://mlsanity.com/paper/1905.11681/) (2019)
- [Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks](https://mlsanity.com/paper/2201.01195/) (2022)

## Abstract (arXiv)

Shear viscosity, though being a fundamental property of all liquids, is computationally expensive to estimate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML methods face significant challenges like overfitting when the size of the data set is small, as is the case with viscosity. In this work, we train several ML models to predict the shear viscosity of a Lennard-Jones (LJ) fluid, with particular emphasis on addressing issues arising from a small data set. Specifically, the issues related to model selection, performance estimation and uncertainty quantification were investigated. First, we show that the widely used performance estimation procedure of using a single unseen data set shows a wide variability on small data sets. In this context, the common practice of using Cross validation (CV) to select the hyperparameters (model selection) can be adapted to estimate the generalization error (performance estimation) as well. We compare two simple CV procedures for their ability to do both model selection and performance estimation, and find that k-fold CV based procedure shows a lower variance of error estimates. We discuss the role of performance metrics in training and evaluation. Finally, Gaussian Process Regression (GPR) and ensemble methods were used to estimate the uncertainty on individual predictions. The uncertainty estimates from GPR were also used to construct an applicability domain using which the ML models provided more reliable predictions on another small data set generated in this work. Overall, the procedures prescribed in this work, together, lead to robust ML models for small data sets.