Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films

Joydeep Munshi; Wei Chen; Te Yu Chien; Ganesh Balasubramanian

doi:10.1016/j.commatsci.2020.110119

Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films

Joydeep Munshi, Wei Chen, Te Yu Chien, Ganesh Balasubramanian^*

^*Corresponding author for this work

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

We discuss results from a machine learned (ML) metaheuristic cuckoo search (CS) optimization technique that is coupled with coarse-grained molecular dynamics (CGMD) simulations to solve a materials and processing design problem for organic photovoltaic (OPV) devices. The method is employed to optimize the composition of donor and acceptor materials, and the thermal annealing temperature during the morphological evolution of a polymer blend active layer composed of poly-(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM), for an increased power conversion efficiency (PCE). The optimal solutions, which are in qualitative agreement with earlier experiments, identify correlation between the design variables that contributes to an enhanced material performance. The framework is extended to multi-objective design (MOCS-CGMD) to attain a Pareto optimality for the blend morphology, and enhance concurrently the exciton diffusion to charge transport probability and the ultimate tensile strength of the material. The predictions reveal that a higher annealing temperature enhances the exciton diffusion to charge transport probability, while a PCBM weight fraction between 0.4 and 0.6 increases the tensile strength of the underlying blend morphology.

Original language	English (US)
Article number	110119
Journal	Computational Materials Science
Volume	187
DOIs	https://doi.org/10.1016/j.commatsci.2020.110119
State	Published - Feb 1 2021

Funding

This material is based on the work supported by the National Science Foundation (NSF) under Award Nos. CMMI-1662435, 1662509 and 1753770 (the latter includes support from a supplement for data science related CMMI research activities). JM and GB acknowledge the allocation of leadership class computing time on Frontera (at the Texas Advanced Computing Center (TACC) in The University of Texas at Austin) to perform the simulations and support through the NSF award # OAC-2031682. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors’ and do not necessarily reflect the views of the NSF.

Keywords

Coarse-grained molecular dynamics
Cuckoo Search
Organic solar cells
P3HT:PCBM
Pareto frontier
Support vector machines

ASJC Scopus subject areas

General Computer Science
General Chemistry
General Materials Science
Mechanics of Materials
General Physics and Astronomy
Computational Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.commatsci.2020.110119

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title = "Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films",

abstract = "We discuss results from a machine learned (ML) metaheuristic cuckoo search (CS) optimization technique that is coupled with coarse-grained molecular dynamics (CGMD) simulations to solve a materials and processing design problem for organic photovoltaic (OPV) devices. The method is employed to optimize the composition of donor and acceptor materials, and the thermal annealing temperature during the morphological evolution of a polymer blend active layer composed of poly-(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM), for an increased power conversion efficiency (PCE). The optimal solutions, which are in qualitative agreement with earlier experiments, identify correlation between the design variables that contributes to an enhanced material performance. The framework is extended to multi-objective design (MOCS-CGMD) to attain a Pareto optimality for the blend morphology, and enhance concurrently the exciton diffusion to charge transport probability and the ultimate tensile strength of the material. The predictions reveal that a higher annealing temperature enhances the exciton diffusion to charge transport probability, while a PCBM weight fraction between 0.4 and 0.6 increases the tensile strength of the underlying blend morphology.",

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author = "Joydeep Munshi and Wei Chen and Chien, {Te Yu} and Ganesh Balasubramanian",

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doi = "10.1016/j.commatsci.2020.110119",

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AB - We discuss results from a machine learned (ML) metaheuristic cuckoo search (CS) optimization technique that is coupled with coarse-grained molecular dynamics (CGMD) simulations to solve a materials and processing design problem for organic photovoltaic (OPV) devices. The method is employed to optimize the composition of donor and acceptor materials, and the thermal annealing temperature during the morphological evolution of a polymer blend active layer composed of poly-(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM), for an increased power conversion efficiency (PCE). The optimal solutions, which are in qualitative agreement with earlier experiments, identify correlation between the design variables that contributes to an enhanced material performance. The framework is extended to multi-objective design (MOCS-CGMD) to attain a Pareto optimality for the blend morphology, and enhance concurrently the exciton diffusion to charge transport probability and the ultimate tensile strength of the material. The predictions reveal that a higher annealing temperature enhances the exciton diffusion to charge transport probability, while a PCBM weight fraction between 0.4 and 0.6 increases the tensile strength of the underlying blend morphology.

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Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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