SNIC SUPR
Machine-learning based materials-design using the Organic Materials Database
Dnr:

SNIC 2019/1-50

Type:

SNIC Large Compute

Principal Investigator:

Alexander Balatsky

Affiliation:

NORDITA

Start Date:

2019-07-01

End Date:

2020-07-01

Primary Classification:

10304: Condensed Matter Physics

Secondary Classification:

10299: Other Computer and Information Science

Allocation

Abstract

Organic molecular crystals come with wide ranging applications, such as organic OLEDs (found in almost every smart phone today), solar cells, carbon sequestration materials, or explosives, and were discussed in the context of spintronic devices and magnon spintronics, molecular qubits, and spin-liquid physics. The theoretical modeling of these materials is bound to characteristic challenges such as distinct energy scales for inter- and intramolecular electron hopping, and strongly localized and correlated electrons. With the specific aim of identifying and designing novel functional materials in the class of organic materials, we have initiated the organic materials database OMDB in January 2016. The OMDB is a freely accessible electronic structure database for previously synthesized 3-dimensional organic crystals, available via https://omdb.mathub.io . The contained data was calculated by us in the framework of the density functional theory using VASP and RSPt. Currently, we are implementing state-of-the-art machine learning tools for prediction of band structure gaps and other properties we might glean from the OMDB. We request SNIC resources to extend and improve the current dataset of the OMDB. This development is also crucial to produce a valid training set for machine learning guided methods in novel functional materials prediction.