Optimization of a Graph‐to‐graph Translation Generative Model Under Multiple Constraints: Discovering New Melt‐cast Explosives

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Main Authors: Joshua L. Lansford, Brian C. Barnes
Format: Artículo Open Access
Published: Wiley 2025
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author Joshua L. Lansford
Brian C. Barnes
author_facet Joshua L. Lansford
Brian C. Barnes
Joshua L. Lansford
Brian C. Barnes
collection Wiley Open Access
contents Optimization of a Graph‐to‐graph Translation Generative Model Under Multiple Constraints: Discovering New Melt‐cast Explosives Joshua L. Lansford Brian C. Barnes Propellants, Explosives, Pyrotechnics ABSTRACT Developing new molecules whose desired properties must fit within multiple constraints is a longstanding challenge, particularly when experimental data is limited and costly. This multi‐objective optimization problem in the nearly infinite parameter space of chemical structures makes physics‐based design models difficult to develop. In this work, we apply a graph‐to‐graph translation model with property constraints to generate new molecules with an iterative approach. We demonstrate this methodology to suggest molecules for melt‐cast explosives that minimize volatility while also improving performance as estimated by oxygen balance and constraining on synthesizability. We identify training epochs and initial training set size as key factors in determining generated molecule diversity. 10.1002/prep.70043 http://onlinelibrary.wiley.com/termsAndConditions#vor
doi_str_mv 10.1002/prep.70043
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institution Wiley Open Access
license_str_mv http://onlinelibrary.wiley.com/termsAndConditions#vor
publishDate 2025
publisher Wiley
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spellingShingle Optimization of a Graph‐to‐graph Translation Generative Model Under Multiple Constraints: Discovering New Melt‐cast Explosives
Joshua L. Lansford
Brian C. Barnes
Propellants, Explosives, Pyrotechnics
Optimization of a Graph‐to‐graph Translation Generative Model Under Multiple Constraints: Discovering New Melt‐cast Explosives Joshua L. Lansford Brian C. Barnes Propellants, Explosives, Pyrotechnics ABSTRACT Developing new molecules whose desired properties must fit within multiple constraints is a longstanding challenge, particularly when experimental data is limited and costly. This multi‐objective optimization problem in the nearly infinite parameter space of chemical structures makes physics‐based design models difficult to develop. In this work, we apply a graph‐to‐graph translation model with property constraints to generate new molecules with an iterative approach. We demonstrate this methodology to suggest molecules for melt‐cast explosives that minimize volatility while also improving performance as estimated by oxygen balance and constraining on synthesizability. We identify training epochs and initial training set size as key factors in determining generated molecule diversity. 10.1002/prep.70043 http://onlinelibrary.wiley.com/termsAndConditions#vor
title Optimization of a Graph‐to‐graph Translation Generative Model Under Multiple Constraints: Discovering New Melt‐cast Explosives
topic Propellants, Explosives, Pyrotechnics
url https://onlinelibrary.wiley.com/doi/10.1002/prep.70043