CaSToRC, the HPC National Competence Centre,
 invites you to the EuroCC and SimEA Seminar Series

Abstract

Machine learning (ML) represents a novel theoretical approach in computational material science which yields the modern paradigm of virtual material design based on known experimental data. The main motivation to use ML methods in computer-aided material design is based on the following fact: the total number of small organic molecules is about 10⁶⁰, while the number of currently known ones does not exceed 10⁸.

Focusing on polyimides (PI), which are high-performance heterocyclic polymers with big variance of chemical structure, we developed graph convolutional neural network (GCNN), being one of the most promising tools for working with big-data, to predict their glass transition temperature (Tg) as an example of the most fundamental polymer property. To train developed GCNN, we propose an original methodology based on using “synthetic” datasets for pre-training and an available, rather small experimental dataset for its fine-tuning. Using known “chemical rules” we combinatorically generated a huge “synthetic”  dataset of PI chemical structures and calculated their temperature of glass transition Tg with the help of Askadskii's QSPR computational scheme.

By using our database, we developed GCNN which allows estimating Tg of PI with mean absolute error (MAE) around 20 degrees that is almost two times lower than in the case of using quantum-chemically calculated dataset for small molecules (QM9). The proposed methodology may be generalized for predicting any other polymer properties.

This work has been financially supported by by the Russian Science Foundation, grant No. 22-13-00066.
 

About the Speaker

Sergey V. Lyulin was awarded his PhD in 1998 by the Institute of Macromolecular Compounds at the Russian Academy of Sciences. He held numerous postdoctoral positions, and in 2013 became head of the “Theory and Modelling of Polymer Systems” Laboratory at the Institute of Macromolecular Compounds, a position which he still holds today. In 2015, he was promoted to Director of the Institute of Macromolecular Compounds, and in 2020 became the Deputy President of the Russian Academy of Sciences.

Download the Summer 2022 EuroCC & SimEA Seminar Series Programme here.

The EuroCC project has received funding from the European Union’s Horizon 2020 research and innovation programme grant agreement No. 951732

The SimEA project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 810660

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