Stacked Ensemble Machine Learning for Range-Separation Parameters

High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange-correlation functionals were developed. The accurate but expensive �first-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree-Fock exchange at a distance characterized by the inverse of a molecule-specific, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning (SEML) model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the �first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a_0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved comparable predictive powers in various optical properties.

Photo-Induced Doping in a Graphene Field-Effect Transistor with Inkjet-Printed Organic Semiconducting Molecules

In this work, we report a novel method of maskless doping of a graphene channel in a field-effect transistor configuration by local inkjet printing of organic semiconducting molecules. The graphene-based transistor was fabricated via large-scale technology, allowing for upscaling electronic device fabrication and lowering the device’s cost. The altering of the functionalization of graphene was performed through local inkjet printing of N,N′-Dihexyl-3,4,9,10-perylenedicarboximide (PDI-C6) semiconducting molecules’ ink. We demonstrated the high resolution (about 50 µm) and accurate printing of organic ink on bare chemical vapor deposited (CVD) graphene. PDI-C6 forms nanocrystals onto the graphene’s surface and transfers charges via π–π stacking to graphene. While the doping from organic molecules was compensated by oxygen molecules under normal conditions, we demonstrated the photoinduced current generation at the PDI-C6/graphene junction with ambient light, a 470 nm diode, and 532 nm laser sources. The local (in the scale of 1 µm) photoresponse of 0.5 A/W was demonstrated at a low laser power density. The methods we developed open the way for local functionalization of an on-chip array of graphene by inkjet printing of different semiconducting organic molecules for photonics and electronics.

Universality of the giant Seebeck effect in organic small molecules

Various pure organic semiconducting molecules exhibit extraordinarily large Seebeck coefficient which cannot be elucidated by conventional physical models of thermoelectricity.

Direct C–H arylation: a “Greener” approach towards facile synthesis of organic semiconducting molecules and polymers

Direct C–H arylation enables “greener” synthesis of π-conjugated small molecules and polymers without tedious preactivation of C–H bonds.

Enhancing charge mobilities in organic semiconductors by selective fluorination: a design approach based on a quantum mechanical perspective

Selective fluorination of organic semiconducting molecules can enhance attractive intermolecular interactions without increasing the risk for charge trapping.