Machine learning bandgaps of double perovskites

By Ghanshyam Pilania, A. Mannodi‐Kanakkithodi, B. Uberuaga, Rampi Ramprasad, James E. Gubernatis, Turab Lookman. Scientific Reports, 2016

448 citations recorded by the source corpus.

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/44c04f1f6af260cd3d36cd88bb27cec550339b9e

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In Electrical and Electronic Engineering

In Materials Chemistry

Citations in the corpus

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design (2019, unknown): “Pilania et al. [7] developed a robust ML framework for the efﬁcient and accurate prediction of the electronic bandgaps of double perovskites.”
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials (2016, unknown): “…the melting temperatures of binary inorganic compounds, 21 the formation enthalpy crystalline compounds, 14,15,28 which crystal structure is likely to form at a certain composition, 5,16,29 – 31 band gap energies of certain classes of crystals 32,33 and the mechanical properties of metal alloys.”
Machine Learning and Materials Informatics: Recent Applications and Prospects (2017, methodology): “In a diﬀerent study, Pilania and co-workers [30] used a database consisting of computed band gaps of ∼ 1,300 AA’BB’O 6 type double perovskites to train a kernel ridge regression (KRR) machine learning model, a scheme that allows for nonlinear relationships based on measures of (dis)similarity…”