Issue 36, 2019

A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Abstract

The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density. We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations.

Graphical abstract: A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Supplementary files

Article information

Article type
Edge Article
Submitted
02 jun 2019
Accepted
08 jul 2019
First published
09 jul 2019
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2019,10, 8374-8383

A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

M. A. F. Afzal, A. Sonpal, M. Haghighatlari, A. J. Schultz and J. Hachmann, Chem. Sci., 2019, 10, 8374 DOI: 10.1039/C9SC02677K

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements