Machine Learning Strategy for Accelerated Design of Polymer Dielectrics

By A. Mannodi‐Kanakkithodi, Ghanshyam Pilania, Tran Doan Huan, Turab Lookman, Rampi Ramprasad. Scientific Reports, 2016

360 citations recorded by the source corpus.

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. While this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/8533ce781a01ad6bbf3df91b12314ef3b479117c

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In Computational Theory and Mathematics

In Electrical and Electronic Engineering

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Citations in the corpus

De novo composite design based on machine learning algorithm (2018, methodology): “applied anML strategy for the design of polymer dielectrics by using data generated from first-principles computations [38].”
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions (2018, methodology): “In our past work, 6,13,26 we have successfully utilized kernel ridge regression (KRR) 61 to learn the nonlinear relationship between a polymer ’ s ﬁ n-gerprint and its properties.”
Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “Several iterations of this strategy help uncover previously unknown polymers, as demonstrated by exemplary work on the design of polymers for high-energy-capacitor applications 72,202 and biodegradable polymers with high glass-transition temperature ( T g ) 203 .”
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design (2019, neutral): “Mannodi-Kanakkithodi et al. [56] performed polymer dielectrics design by building statistical learning models based on data generated from ﬁrst-principles computations.”