Accelerating materials property predictions using machine learning.

ABSTRACT: 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 distri

SUBMITTER: Pilania G 

PROVIDER: S-EPMC3786293 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature