TY - JOUR
T1 - Machine learning-based screening of complex molecules for polymer solar cells
AU - Jørgensen, Peter Bjørn
AU - Mesta, Murat
AU - Shil, Suranjan
AU - García Lastra, Juan Maria
AU - Jacobsen, Karsten Wedel
AU - Thygesen, Kristian Sommer
AU - Schmidt, Mikkel N.
PY - 2018
Y1 - 2018
N2 - Polymer solar cells admit numerous potential advantages including low
energy payback time and scalable high-speed manufacturing, but the power
conversion efficiency is currently lower than for their inorganic
counterparts. In a Phenyl-C_61-Butyric-Acid-Methyl-Ester (PCBM)-based
blended polymer solar cell, the optical gap of the polymer and the
energetic alignment of the lowest unoccupied molecular orbital (LUMO) of
the polymer and the PCBM are crucial for the device efficiency.
Searching for new and better materials for polymer solar cells is a
computationally costly affair using density functional theory (DFT)
calculations. In this work, we propose a screening procedure using a
simple string representation for a promising class of donor-acceptor
polymers in conjunction with a grammar variational autoencoder. The
model is trained on a dataset of 3989 monomers obtained from DFT
calculations and is able to predict LUMO and the lowest optical
transition energy for unseen molecules with mean absolute errors of 43
and 74 meV, respectively, without knowledge of the atomic positions. We
demonstrate the merit of the model for generating new molecules with the
desired LUMO and optical gap energies which increases the chance of
finding suitable polymers by more than a factor of five in comparison to
the randomised search used in gathering the training set.
AB - Polymer solar cells admit numerous potential advantages including low
energy payback time and scalable high-speed manufacturing, but the power
conversion efficiency is currently lower than for their inorganic
counterparts. In a Phenyl-C_61-Butyric-Acid-Methyl-Ester (PCBM)-based
blended polymer solar cell, the optical gap of the polymer and the
energetic alignment of the lowest unoccupied molecular orbital (LUMO) of
the polymer and the PCBM are crucial for the device efficiency.
Searching for new and better materials for polymer solar cells is a
computationally costly affair using density functional theory (DFT)
calculations. In this work, we propose a screening procedure using a
simple string representation for a promising class of donor-acceptor
polymers in conjunction with a grammar variational autoencoder. The
model is trained on a dataset of 3989 monomers obtained from DFT
calculations and is able to predict LUMO and the lowest optical
transition energy for unseen molecules with mean absolute errors of 43
and 74 meV, respectively, without knowledge of the atomic positions. We
demonstrate the merit of the model for generating new molecules with the
desired LUMO and optical gap energies which increases the chance of
finding suitable polymers by more than a factor of five in comparison to
the randomised search used in gathering the training set.
U2 - 10.1063/1.5023563
DO - 10.1063/1.5023563
M3 - Journal article
C2 - 29960358
SN - 0021-9606
VL - 148
JO - Journal of Chemical Physics
JF - Journal of Chemical Physics
M1 - 241735
ER -