Issue 9, 2024

Machine learning of stability scores from kinetic data

Abstract

The absence of computational methods to predict stressor-specific degradation susceptibilities represents a significant and costly challenge to the introduction of new materials into applications. Here, a machine-learning framework is developed that predicts stressor-specific stability scores from computationally generated reaction data. The thermal degradation of alkanes was studied as an exemplary system to demonstrate the approach. The half-lives of ∼32k alkanes were simulated under pyrolysis conditions using 59 model reactions. Using a hinge-loss function, these half-life data were used to train machine learning models to predict a scalar representing the relative stability based only on the molecular graph. These models were successful in transferability case studies using distinct training and testing splits to recapitulate known stability trends with respect to the degree of branching and alkane size. Even the simplest models showed excellent performance in these case studies, demonstrating the relative ease with which thermal stability can be learned. The stability score is also shown to be useful in a design study, where it is used as part of the objective function of a genetic algorithm to guide the search for more stable species. This work provides a framework for converting kinetic reaction data into stability scores that provide actionable design information and opens avenues for exploring more complex chemistries and stressors.

Graphical abstract: Machine learning of stability scores from kinetic data

Supplementary files

Article information

Article type
Paper
Submitted
30 Jan 2024
Accepted
28 Jun 2024
First published
01 Jul 2024
This article is Open Access
Creative Commons BY license

Digital Discovery, 2024,3, 1729-1737

Machine learning of stability scores from kinetic data

V. Singla, Q. Zhao and B. M. Savoie, Digital Discovery, 2024, 3, 1729 DOI: 10.1039/D4DD00036F

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.

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