Generative BigSMILES: an extension for polymer informatics, computer simulations & ML/AI
Name
d3dd00147d.pdf
Description
Published version
Size
1.15 MB
Format
Adobe PDF
Checksum (MD5)
0726b9be12b25a59ee47e2f33b5f3739
Author(s)
Schneider, Ludwig
Walsh, Dylan
Olsen, Bradley
de Pablo, Juan
Date Issued
November 17, 2023
Journal
Digital Discovery
Publisher
Royal Society of Chemistry
Citation
Schneider, Ludwig, Walsh, Dylan, Olsen, Bradley and de Pablo, Juan. 2023. "Generative BigSMILES: an extension for polymer informatics, computer simulations & ML/AI." Digital Discovery, 3 (1).
Version
Final published version
Abstract
The BigSMILES notation, a concise tool for polymer ensemble representation, is augmented here by introducing an enhanced version called generative BigSMILES. G-BigSMILES is designed for generative workflows, and is complemented by tailored software tools for ease of use. This extension integrates additional data, including reactivity ratios (or connection probabilities among repeat units), molecular weight distributions, and ensemble size. An algorithm, interpretable as a generative graph is devised that utilizes these data, enabling molecule generation from defined polymer ensembles. Consequently, the G-BigSMILES notation allows for efficient specification of complex molecular ensembles via a streamlined line notation, thereby providing a foundational tool for automated polymeric materials design. In addition, the graph interpretation of the G-BigSMILES notation sets the stage for robust machine learning methods capable of encapsulating intricate polymeric ensembles. The combination of G-BigSMILES with advanced machine learning techniques will facilitate straightforward property determination and in silico polymeric material synthesis automation. This integration has the potential to significantly accelerate materials design processes and advance the field of polymer science.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d3dd00147d
