scholarly journals Machine Learning to Accelerate Screening for Marcus Reorganization Energies

2021 ◽  
Author(s):  
Omri Abaarbanel ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Charge Transport ◽  
Density Functional ◽  
Activation Barrier ◽  
Reorganization Energy ◽  
Transport Parameters ◽  
Light Emitting Devices ◽  
Wide Range ◽  
Important Challenge ◽  
Reorganization Energies

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic applications, including solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy, which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use towards screening new compounds with low internal reorganization energies. Our models have an overall root mean square error of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

scholarly journals Machine Learning to Accelerate Screening for Marcus Reorganization Energies

2021 ◽  
Author(s):  
Omri Abaarbanel ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Charge Transport ◽  
Density Functional ◽  
Activation Barrier ◽  
Reorganization Energy ◽  
Transport Parameters ◽  
Light Emitting Devices ◽  
Wide Range ◽  
Important Challenge ◽  
Reorganization Energies

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic applications, including solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy, which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use towards screening new compounds with low internal reorganization energies. Our models have an overall root mean square error of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

scholarly journals A machine learning platform for the discovery of materials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carl E. Belle ◽  
Vural Aksakalli ◽  
Salvy P. Russo
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Gw Approximation ◽  
Dft Methods ◽  
Functional Theory ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

Theoretical study of charge-transport and optical properties of indeno[1,2-b]fluorene-6,12-dione-based semiconducting materials

2018 ◽  
Vol 74 (6) ◽  
pp. 705-711 ◽  
Author(s):  
Jin-Dou Huang ◽  
Jinfeng Zhao ◽  
Kun Yu ◽  
Xiaohua Huang ◽  
Shi-Bo Cheng ◽  
...  
Keyword(s):  
Optical Properties ◽  
Charge Transport ◽  
Near Infrared ◽  
Reorganization Energy ◽  
Hole Mobility ◽  
Side Chains ◽  
Semiconducting Materials ◽  
Π Stacking ◽  
Electron Transfer Theory ◽  
Reorganization Energies

The conducting and optical properties of a series of indeno[1,2-b]fluorene-6,12-dione (IFD)-based molecules have been systematically studied and the influences of butyl, butylthio and dibutylamino substituents on the reorganization energies, intermolecular electronic couplings and charge-injection barriers of IFD have been discussed. The quantum-chemical calculations combined with electron-transfer theory reveal that the incorporation of sulfur-linked side chains decreases reorganization energy associated with hole transfer and optimizes intermolecular π–π stacking, which results in excellent ambipolar charge-transport properties (μh = 1.15 cm2 V−1 s−1 and μe = 0.08 cm2 V−1 s−1); in comparison, addition of dibutylamino side chains increases intermolecular steric interactions and hinders perfect intermolecular π–π stacking, which results in the weak electronic couplings and finally causes the low intrinsic hole mobility (μh = 0.01 cm2 V−1 s−1). Furthermore, electronic spectra of butyl-IFD, butylthio-IFD and dibutylamino-IFD were simulated and compared with the reported experimental data. Calculations demonstrate that IFD-based molecules possess potential for developing novel infrared and near-infrared probe materials via suitable chemical modifications.

Theoretical Investigation of Structural Effects on the Charge Transfer Properties in Modified Phthalocyanines

MRS Advances ◽  
2015 ◽  
Vol 1 (7) ◽  
pp. 453-458 ◽  
Author(s):  
Patrick J. Dwyer ◽  
Stephen P. Kelty
Keyword(s):  
Organic Semiconductors ◽  
Electron Affinity ◽  
Density Functional ◽  
Reorganization Energy ◽  
High Electron ◽  
Optoelectronic Materials ◽  
Research Attention ◽  
Efficient Charge ◽  
Reorganization Energies ◽  
P Type

ABSTRACTFor efficient charge separation and charge transport in optoelectronic materials, small internal reorganization energies are desired. While many p-type organic semiconductors have been reported with low internal reorganization energies, few n-type materials with low reorganization energy are known. Metal phthalocyanines have long received extensive research attention in the field of organic device electronics due to their highly tunable electronic properties through modification of the molecular periphery. In this study, density functional theory (DFT) calculations are performed on a series of zinc-phthalocyanines (ZnPc) with various degrees of peripheral per-fluoroalkyl (-C3F7) modification. Introduction of the highly electron withdrawing groups on the periphery leads to a lowering in the energy of the molecular frontier orbitals as well as an increase in the electron affinity. Additionally, all molecules studies are found to be most stable in their anionic form, demonstrating their potential as n-type materials. However, the calculated internal reorganization energy slightly increases as a function of peripheral modification. By varying the degree of modification we develop a strategy for obtaining an optimal balance between low reorganization energy and high electron affinity for the development of novel n-type optoelectronic materials.

scholarly journals Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

2021 ◽  
Author(s):  
Ke Chen ◽  
Christian Kunkel ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Organic Semiconductors ◽  
Organic Molecules ◽  
Predictive Accuracy ◽  
Molecular Design ◽  
Reorganization Energy ◽  
Charge Carrier Mobility ◽  
Design Rule ◽  
Reorganization Energies

The molecular reorganization energy $\lambda$ strongly influences the charge carrier mobility of organic semiconductors and is therefore an important target for molecular design. Machine learning (ML) models generally have the potential to strongly accelerate this design process (e.g. in virtual screening studies) by providing fast and accurate estimates of molecular properties. While such models are well established for simple properties (e.g. the atomization energy), $\lambda$ poses a significant challenge in this context. In this paper, we address the questions of how ML models for $\lambda$ can be improved and what their benefit is in high-throughput virtual screening (HTVS) studies. We find that, while improved predictive accuracy can be obtained relative to a semiempirical baseline model, the improvement in molecular discovery is somewhat marginal. In particular, the ML enhanced screenings are more effective in identifying promising candidates but lead to a less diverse sample. We further use substructure analysis to derive a general design rule for organic molecules with low $\lambda$ from the HTVS results.

scholarly journals Quantum Mechanical Modeling Unveil the Effect of Substitutions on the Activation Barriers of the Diels-Alder Reactions of an Antiviral Compound 7H-Benzo[a]phenalene

2021 ◽  
Author(s):  
Umatur Rehman ◽  
Asim Mansha ◽  
Muhammad Zahid ◽  
Sadia Asim ◽  
Ameer Fawad Zahoor ◽  
...  
Keyword(s):  
Maleic Anhydride ◽  
Density Functional ◽  
Activation Barrier ◽  
Experimental Studies ◽  
Alder Reaction ◽  
Diels Alder ◽  
Antiviral Compound ◽  
Fukui Functions ◽  
Diels Alder Reaction ◽  
Wide Range

Abstract Density functional theory has been utilized for exploring the mechanism of Diels-Alder reaction between 7H-benzo[a]phenalene and maleic anhydride. 7H-Benzo[a]phenalene is an antiviral compound and information available about its cycloaddition reactions with possible reaction path and mechanism is scarce. In order to work on the synthesis of its further potential derivatives, the mechanism of its reaction with all aspects should be well understood. Two novel intermediates involved in this reaction have been reported. Diels-Alder reaction of maleic anhydride has found many applications in the synthesis of wide range of useful products. The major concern of this work is to evaluate the consequences of introducing electron donating and electron withdrawing substituents on the reactivity of maleic anhydride towards 7H-benzo[a]phenalene. Thermodynamic parameters, activation parameters, energies of frontier orbitals, global reactivity indices and global electron density transfer (GEDT) have been determined for all the reactions. Fukui functions are computed for each reactant in order to identify the most reactive sites. All the reactions have been found to proceed via normal electron demand having polar nature. The substituents with opposite electronic properties were expected to affect the reactivity of dienophile in an inverse manner, however, the results are not according to this assumption. Rather, both kinds of substituents increased the activation barrier of the reaction. This behavior has been explained in the light of various parameters such as the stability of reacting species, gap of frontier molecular orbitals etc. Experimental studies reported previously are in agreement with these results.

scholarly journals Reorganization energies and spectral densities for electron transfer problems in charge transport materials

2020 ◽  
Vol 22 (38) ◽  
pp. 21630-21641
Author(s):  
Chao-Ping Hsu
Keyword(s):  
Electron Transfer ◽  
Charge Transport ◽  
Reorganization Energy ◽  
Transfer System ◽  
Spectral Densities ◽  
Electron Transfer System ◽  
Computational Aspects ◽  
Reorganization Energies ◽  
Transfer Problems

Various contributions to the outer reorganization energy of an electron transfer system and their theoretical and computational aspects have been discussed.

scholarly journals Charge transport parameters for carbon based nanohoops and donor–acceptor derivatives

2019 ◽  
Vol 21 (4) ◽  
pp. 2057-2068 ◽  
Author(s):  
Sofia Canola ◽  
Christina Graham ◽  
Ángel José Pérez-Jiménez ◽  
Juan-Carlos Sancho-García ◽  
Fabrizia Negri
Keyword(s):  
Charge Transport ◽  
Crystalline Structure ◽  
Organic Molecules ◽  
Transport Parameters ◽  
Conjugated Organic Molecules ◽  
Donor Acceptor ◽  
Reorganization Energies ◽  
Carbon Based

The effect of donor–acceptor (D–A) moieties on magnitudes such as reorganization energies and electronic couplings in cycloparaphenylene (CPP) carbon based nanohoops (i.e. conjugated organic molecules with cyclic topology) is highlighted via model computations and analysis of the available crystalline structure of N,N-dimethylaza[8]CPP.

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