# Predicting physical properties of alkanes with neural networks

## TL;DR

This paper demonstrates that neural networks can accurately predict various physical properties of alkanes, leveraging fragmented data and chemical descriptors, outperforming traditional methods.

## Contribution

Introduces neural network models that predict alkane properties using chemical descriptors and property correlations, even with fragmented data, improving prediction accuracy over existing methods.

## Key findings

- Neural networks accurately predict boiling point, heat capacity, vapor pressure, and melting point.
- Property-property correlations enhance prediction quality.
- Modeling of viscosity and density as functions of temperature and pressure.

## Abstract

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods.