MRS Meetings and Events


DS04.04.05 2023 MRS Fall Meeting

Data-Driven Bayesian Optimization of P-Type Polymer Dopant Complexes for Next-Generation Thermoelectrics

When and Where

Nov 28, 2023
9:45am - 10:00am

Sheraton, Second Floor, Back Bay B



Connor Ganley1,Tushita Mukhopadhyaya1,2,Howard Katz1,Paulette Clancy1

Johns Hopkins University1,Georgia Institute of Technology2


Connor Ganley1,Tushita Mukhopadhyaya1,2,Howard Katz1,Paulette Clancy1

Johns Hopkins University1,Georgia Institute of Technology2
Semiconducting polymers have shown remarkable performance in applications such as organic light-emitting diodes (OLEDs), organic solar cells (OSCs), and thermoelectric materials, establishing their status as formidable candidates for “greener” next-generation materials. These materials are solution-processable, hence their manufacture is low-cost and highly scalable. Doping suitable polymers with small molecules has a demonstrably positive impact on the electronic properties of the final polymeric thin film by increasing the charge carrier concentration and electrical conductivity. We have previously published several studies exploring different <i>p</i>- and <i>n-</i>type polymers, dopants, and their respective properties, showing, in some select cases, record-breaking thermoelectric performance. Unfortunately, the search for optimal polymer-dopant pairs thus far has been largely Edisonian and based on ill-quantified means like expert knowledge and chemical intuition. This work seeks to leverage the power of <i>ab initio</i> electronic structure calculations in conjunction with a machine-learning-based optimization for high-performing <i>p-</i>type doped semiconducting polymers.<br/>The goal of this work is to identify a novel high-performing <i>p-</i>type polymer-dopant combination from a combinatorically expansive pool of candidates, which will consequently demonstrate the utility of a data-based approach toward polymer engineering. Such an identification will be conducted using the Physical Analytics pipeLine (PAL), a Bayesian Optimization (BO) code written in Python and developed by the Clancy group. PAL has been shown previously to incorporate physical domain knowledge (<i>e.g.</i> DFT results) into an efficient optimization over a large compositional space. The input data will consist of electronic properties obtained from density functional theory (DFT) calculations, such as sigma profiles, HOMO-LUMO gaps, and ionization potentials/electron affinities. For reference, sigma profiles are unnormalized histograms of the screened surface charge of a molecule that provide important information about charge distribution in a molecule. This important information provides information on the degree of localization of charges, which impacts electron transfer. A large amount of model training data already exists from our previous papers about doped <i>p</i>-type polymers. The BO algorithm will attempt to maximize electron transport properties such as the Seebeck coefficient, power factor, and conductivity, in separate single-objective experiments. These properties will be calculated for candidate complexes using BoltzTraP2, a Python code that quantifies electron transport behavior.



Symposium Organizers

Andrew Detor, GE Research
Jason Hattrick-Simpers, University of Toronto
Yangang Liang, Pacific Northwest National Laboratory
Doris Segets, University of Duisburg-Essen

Symposium Support


Publishing Alliance

MRS publishes with Springer Nature