Accelerating materials property predictions using machine learning

By Ghanshyam Pilania, Chenchen Wang, Xun Jiang, Sanguthevar Rajasekaran, R. Ramprasad. 2013

761 citations recorded by the source corpus.

The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces and can significantly accelerate the discovery of new application-specific materials.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry

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Citations in the corpus

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (2018, background): “the computational task. Finally, the global state attributes u are updated using information from itself and all atoms and bonds, as follows: ue = 1 Ne XNe k=1 fe0 k g (4) uv = 1 Nv XNv i=1 fv0 i g (5) u0= ˚ u ue M uv M u (6) 6 where ˚ u is the global state update function. In addition to providin”
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019, background): “[68] show that bounded rationality with quantized priors may lead to discrimination.”
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, methodology): “To this end, many existing works apply predictive models including neural networks [14] or support vector machines [15] to material databases for building PSP links and, subsequently, estimate the material properties where no data (either experimental or computational) are available [16–20].”