Data-based Polymer-Unit Fingerprint (PUFp): A Newly Accessible Expression of Polymer Organic Semiconductors for Machine Learning
This work addresses the challenge of designing high-performance organic semiconductors for material science applications, presenting an incremental method that combines existing ML techniques with a new structural representation.
The authors tackled the problem of identifying key functional units in polymer organic semiconductors (OSCs) to establish structure-property relationships, resulting in a polymer-unit fingerprint (PUFp) framework that uses machine learning to predict mobility and guide new material design with 678 OSC data points and a library of 445 units.
In the process of finding high-performance organic semiconductors (OSCs), it is of paramount importance in material development to identify important functional units that play key roles in material performance and subsequently establish substructure-property relationships. Herein, we describe a polymer-unit fingerprint (PUFp) generation framework. Machine learning (ML) models can be used to determine structure-mobility relationships by using PUFp information as structural input with 678 pieces of collected OSC data. A polymer-unit library consisting of 445 units is constructed, and the key polymer units for the mobility of OSCs are identified. By investigating the combinations of polymer units with mobility performance, a scheme for designing polymer OSC materials by combining ML approaches and PUFp information is proposed to not only passively predict OSC mobility but also actively provide structural guidance for new high-mobility OSC material design. The proposed scheme demonstrates the ability to screen new materials through pre-evaluation and classification ML steps and is an alternative methodology for applying ML in new high-mobility OSC discovery.