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

by   Nikhil V. S. Avula, et al.

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.


page 8

page 9

page 10

page 11

page 13

page 32

page 33

page 34


Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection

As the main workhorse for model selection, Cross Validation (CV) has ach...

Model selection for estimation of causal parameters

A popular technique for selecting and tuning machine learning estimators...

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

The solvation free energy of organic molecules is a critical parameter i...

Cross Validation Based Model Selection via Generalized Method of Moments

Structural estimation is an important methodology in empirical economics...

Automatic Catalog of RRLyrae from ∼ 14 million VVV Light Curves: How far can we go with traditional machine-learning?

The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the ...

Please sign up or login with your details

Forgot password? Click here to reset