Industrial Doctoral Landscape Award (PhD studentship): Utilising Graph-based Machine Leaning for Impact Sensitivity Prediction for Energetic Materials

Energetic Materials (explosives, propellants, pyrotechnics and gas generators) are used in a broad range of pubic and private sector applications. The design of novel, safe materials is therefore of critical importance. At present, the development of new energetic materials requires synthesis and experimental testing, with a posteriori rationalisation of material properties. This poses obvious safety hazards, yields limited fundamental understanding of structure/property relationships, and does not permit the targeted design of new materials with tailored properties.

This PhD research project concerns the application of graph-based machine learning tools to predict impact sensitivity, which is a measure of the amount of mechanical energy required to induce initiation of energetic materials. However, the available experimental dataset to train a machine-learning model is sparce and has a low numerical confidence rating. This therefore presents opportunities for innovation, to develop cutting edge machine learning tools based on graph-based models that can successfully sort energetic molecules according to their impact sensitivity values. Moreover, in this process we will create molecular graphs that directly encode for a defined impact sensitivity response. We will then seek to exploit these graphs, to predict new molecular structures that present with a desired impact sensitivity property.

Requirements:

The successful candidate will possess, or expect to obtain, a first class or upper-second class undergraduate degree (or equivalent) in chemistry. Desirable qualities include previous knowledge of machine learning, python and computational chemistry modelling. Essential attributes are good presentation and communication skills (written and oral).

Applying:

In the first instance informal enquiries (accompanied by a CV) should be directed to: Prof. Carole A. Morrison (c.morrison@ed.ac.uk) and Dr. Sohan Seth (Sohan.seth@ed.ac.uk). 

Formal applications to Edinburgh are made through the University’s EUCLID system: https://chem.ed.ac.uk/studying/phd/applications-and-entry-requirements

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