Data-Driven Design of a New Organic Semiconductor via an Electronic Structure Chart
Daniel M. Packwood, Yu Kaneko, Daiji Ikeda, Mitsuru Ohno

TL;DR
This paper introduces a novel data-driven approach using an electronic structure chart to design organic crystals with desired solid-state electronic properties, bypassing direct property regression.
Contribution
It presents the first method to design molecules based on actual solid-state electronic properties using a two-dimensional electronic structure chart.
Findings
Identified a new molecule with targeted band gap.
Predicted a molecule with improved band curvatures.
Demonstrated the effectiveness of the electronic structure chart approach.
Abstract
Data-driven methodologies for designing new materials are developing apace, yet advances for organic crystals have been infrequent. For organic crystals, the need to predict solid-state electronic properties from molecular structure alone is an exceedingly difficult task for typical, regression-based design strategies. In this paper, we present a new strategy for designing organic crystals which circumvents the need to regress solid-state physical properties directly. At the core of this strategy is an electronic structure chart, a two-dimensional projection of an organic crystal database in which each material is positioned according to its solid-state electronic properties. We illustrate this strategy by identifying a new molecule which is predicted to show a targeted band gap and better-than-average band curvatures in the crystalline state. This strategy is the first data-driven…
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Taxonomy
TopicsMachine Learning in Materials Science · Computational Drug Discovery Methods · Conducting polymers and applications
