1
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Bhat V, Ganapathysubramanian B, Risko C. Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline. J Phys Chem Lett 2024; 15:7206-7213. [PMID: 38973725 DOI: 10.1021/acs.jpclett.4c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Organic semiconductors (OSC) offer tremendous potential across a wide range of (opto)electronic applications. OSC development, however, is often limited by trial-and-error design, with computational modeling approaches deployed to evaluate and screen candidates through a suite of molecular and materials descriptors that generally require hours to days of computational time to accumulate. Such bottlenecks slow the pace and limit the exploration of the vast chemical space comprising OSC. When considering charge-carrier transport in OSC, a key parameter of interest is the intermolecular electronic coupling. Here, we introduce a machine learning (ML) model to predict intermolecular electronic couplings in organic crystalline materials from their three-dimensional (3D) molecular geometries. The ML predictions take only a few seconds of computing time compared to hours by density functional theory (DFT) methods. To demonstrate the utility of the ML predictions, we deploy the ML model in conjunction with mathematical formulations to rapidly screen the charge-carrier mobility anisotropy for more than 60,000 molecular crystal structures and compare the ML predictions to DFT benchmarks.
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Affiliation(s)
- Vinayak Bhat
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Baskar Ganapathysubramanian
- Department of Mechanical Engineering & Translational AI Center, Iowa State University, Ames, Iowa 50010, United States
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States
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2
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Wang CI, Maier JC, Jackson NE. Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining. Chem Sci 2024; 15:8390-8403. [PMID: 38846409 PMCID: PMC11151863 DOI: 10.1039/d3sc06749a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/01/2024] [Indexed: 06/09/2024] Open
Abstract
Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these relationships require the complex integration of quantum-chemical (QC) calculations, all-atom and coarse-grained (CG) molecular dynamics simulations, and back-mapping approaches. However, these methods pose substantial computational challenges that limit their application to the requisite length scales of soft material morphologies. Here, we demonstrate the bottom-up electronic coarse-graining (ECG) of morphology-dependent electronic structure in the liquid-crystal-forming semiconductor, 2-(4-methoxyphenyl)-7-octyl-benzothienobenzothiophene (BTBT). ECG is applied to construct density functional theory (DFT)-accurate valence band Hamiltonians of the isotropic and smectic liquid crystal (LC) phases using only the CG representation of BTBT. By bypassing the atomistic resolution and its prohibitive computational costs, ECG enables the first calculations of the morphology dependence of the electronic structure of charge carriers across LC phases at the ∼20 nm length scale, with robust statistical sampling. Kinetic Monte Carlo (kMC) simulations reveal a strong morphology dependence on zero-field charge mobility among different LC phases as well as the presence of two-molecule charge carriers that act as traps and hinder charge transport. We leverage these results to further evaluate the feasibility of developing mesoscopic, field-based ECG models in future works. The fully CG approach to electronic property predictions in LC semiconductors opens a new computational direction for designing electronic processes in soft materials at their characteristic length scales.
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Affiliation(s)
- Chun-I Wang
- Department of Chemistry, University of Illinois at Urbana-Champaign 505 S Mathews Avenue Urbana Illinois 61801 USA
| | - J Charlie Maier
- Department of Chemistry, University of Illinois at Urbana-Champaign 505 S Mathews Avenue Urbana Illinois 61801 USA
| | - Nicholas E Jackson
- Department of Chemistry, University of Illinois at Urbana-Champaign 505 S Mathews Avenue Urbana Illinois 61801 USA
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3
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Maier JC, Wang CI, Jackson NE. Distilling coarse-grained representations of molecular electronic structure with continuously gated message passing. J Chem Phys 2024; 160:024109. [PMID: 38193551 DOI: 10.1063/5.0179253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/14/2023] [Indexed: 01/10/2024] Open
Abstract
Bottom-up methods for coarse-grained (CG) molecular modeling are critically needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a target molecule, the ideal reduced CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We introduce continuously gated message passing (CGMP), a graph neural network (GNN) method for atomically decomposing molecular electronic structure sampled over conformational ensembles. CGMP integrates 3D-invariant GNNs and a novel gated message passing system to continuously reduce the atomic degrees of freedom accessible for electronic predictions, resulting in a one-shot importance ranking of atoms contributing to a target molecular property. Moreover, CGMP provides the first approach by which to quantify the degeneracy of "good" CG representations conditioned on specific prediction targets, facilitating the development of more transferable CG representations. We further show how CGMP can be used to highlight multiatom correlations, illuminating a path to developing CG electronic Hamiltonians in terms of interpretable collective variables for arbitrarily complex molecules.
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Affiliation(s)
- J Charlie Maier
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Chun-I Wang
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Nicholas E Jackson
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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4
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Lin HH, Wang CI, Yang CH, Secario MK, Hsu CP. Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data. J Phys Chem A 2024; 128:271-280. [PMID: 38157315 DOI: 10.1021/acs.jpca.3c04524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Electronic coupling is important in determining charge-transfer rates and dynamics. Coupling strength is sensitive to both intermolecular, e.g., orientation or distance, and intramolecular degrees of freedom. Hence, it is challenging to build an accurate machine learning model to predict electronic coupling of molecular pairs, especially for those derived from the amorphous phase, for which intermolecular configurations are much more diverse than those derived from crystals. In this work, we devise a new prediction algorithm that employs two consecutive KRR models. The first model predicts molecular orbitals (MOs) from structural variation for each fragment, and coupling is further predicted by using the overlap integral included in a second model. With our two-step procedure, we achieved mean absolute errors of 0.27 meV for an ethylene dimer and 1.99 meV for a naphthalene pair, much improved accuracy amounting to 14-fold and 3-fold error reductions, respectively. In addition, MOs from the first model can also be the starting point to obtain other quantum chemical properties from atomistic structures. This approach is also compatible with a MO predictor with sufficient accuracy.
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Affiliation(s)
- Hung-Hsuan Lin
- Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan
- Molecular Science and Digital Innovation Center, Genetics Generation Advancement Corp, No. 28, Ln. 36, Xinhu First Rd., Neihu, Taipei 114, Taiwan
| | - Chun-I Wang
- Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Chou-Hsun Yang
- Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan
| | - Muhammad Khari Secario
- Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan
- Taiwan International Graduate Program on Sustainable Chemical Science & Technology, Academia Sinica Institute of Chemistry, 128 Academia Road Sec.2, Nankang, Taipei 115, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
| | - Chao-Ping Hsu
- Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan
- Division of Physics, National Center for Theoretical Sciences, 1, Section 4, Roosevelt Road, Taipei 106, Taiwan
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5
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Mostaghimi M, Pacheco Hernandez H, Jiang Y, Wenzel W, Heinke L, Kozlowska M. On-off conduction photoswitching in modelled spiropyran-based metal-organic frameworks. Commun Chem 2023; 6:275. [PMID: 38110545 PMCID: PMC10728195 DOI: 10.1038/s42004-023-01072-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 11/22/2023] [Indexed: 12/20/2023] Open
Abstract
Materials with photoswitchable electronic properties and conductance values that can be reversibly changed over many orders of magnitude are highly desirable. Metal-organic framework (MOF) films functionalized with photoresponsive spiropyran molecules demonstrated the general possibility to switch the conduction by light with potentially large on-off-ratios. However, the fabrication of MOF materials in a trial-and-error approach is cumbersome and would benefit significantly from in silico molecular design. Based on the previous proof-of-principle investigation, here, we design photoswitchable MOFs which incorporate spiropyran photoswitches at controlled positions with defined intermolecular distances and orientations. Using multiscale modelling and automated workflow protocols, four MOF candidates are characterized and their potential for photoswitching the conductivity is explored. Using ab initio calculations of the electronic coupling between the molecules in the MOF, we show that lattice distances and vibrational flexibility tremendously modulate the possible conduction photoswitching between spiropyran- and merocyanine-based MOFs upon light absorption, resulting in average on-off ratios higher than 530 and 4200 for p- and n-conduction switching, respectively. Further functionalization of the photoswitches with electron-donating/-withdrawing groups is demonstrated to shift the energy levels of the frontier orbitals, permitting a guided design of new spiropyran-based photoswitches towards controlled modification between electron and hole conduction in a MOF.
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Affiliation(s)
- Mersad Mostaghimi
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Helmy Pacheco Hernandez
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Yunzhe Jiang
- Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Wolfgang Wenzel
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Lars Heinke
- Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany.
| | - Mariana Kozlowska
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany.
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6
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Ma J, Du Z, Lei Z, Wang L, Yu Y, Ye X, Ou W, Wei X, Ai B, Zhou Y. Intermolecular 3D-MoRSE Descriptors for Fast and Accurate Prediction of Electronic Couplings in Organic Semiconductors. J Chem Inf Model 2023; 63:5089-5096. [PMID: 37566518 DOI: 10.1021/acs.jcim.3c00786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
The theoretical rational design of organic semiconductors faces an obstacle in that the performance of organic semiconductors depends very much on their stacking and local morphology (for example, phase domains), which involves numerous molecules. Simulation becomes computationally expensive as intermolecular electronic couplings have to be calculated from density functional theory. Therefore, developing fast and accurate methods for intermolecular electronic coupling estimation is essential. In this work, by developing a series of new intermolecular 3D descriptors, we achieved fast and accurate prediction of electronic couplings in both crystalline and amorphous thin films. Three groups of developed descriptors could perform faster and higher accuracy prediction on electronic couplings than the most advanced state-of-the-art descriptors. This work paves the way for large-scale simulations, high-throughput calculations, and screening of organic semiconductors.
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Affiliation(s)
- Jiacheng Ma
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhenya Du
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Guangzhou Xinhua University, Guangzhou 510520, China
| | - Zhanpeng Lei
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Lewen Wang
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Yinye Yu
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Micro-Nano Manufacturing and System Integration Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Ye
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Wen Ou
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Xingzhan Wei
- Micro-Nano Manufacturing and System Integration Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Bin Ai
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Yecheng Zhou
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, School of Material Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
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7
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Huynh H, Kelly TJ, Vu L, Hoang T, Nguyen PA, Le TC, Jarvis EA, Phan H. Quantum Chemistry-Machine Learning Approach for Predicting Properties of Lewis Acid-Lewis Base Adducts. ACS OMEGA 2023; 8:19119-19127. [PMID: 37273580 PMCID: PMC10233689 DOI: 10.1021/acsomega.3c02822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023]
Abstract
Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics.
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Affiliation(s)
- Hieu Huynh
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
| | - Thomas J. Kelly
- Loyola
Marymount University, Los Angeles, California 90045, United States
| | - Linh Vu
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
| | - Tung Hoang
- Independent
Researcher, Palo Alto, California 94303, Unites States
| | - Phuc An Nguyen
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
| | - Tu C. Le
- School
of Engineering, STEM College, RMIT University, Melbourne, Victoria 3000, Australia
| | - Emily A. Jarvis
- Loyola
Marymount University, Los Angeles, California 90045, United States
| | - Hung Phan
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
- Soka
University of America, Aliso Viejo, California 92656, United States
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8
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Hammes-Schiffer S. Exploring Proton-Coupled Electron Transfer at Multiple Scales. NATURE COMPUTATIONAL SCIENCE 2023; 3:291-300. [PMID: 37577057 PMCID: PMC10416817 DOI: 10.1038/s43588-023-00422-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 02/23/2023] [Indexed: 08/15/2023]
Abstract
The coupling of electron and proton transfer is critical for chemical and biological processes spanning a wide range of length and time scales and often occurring in complex environments. Thus, diverse modeling strategies, including analytical theories, quantum chemistry, molecular dynamics, and kinetic modeling, are essential for a comprehensive understanding of such proton-coupled electron transfer reactions. Each of these computational methods provides one piece of the puzzle, and all these pieces must be viewed together to produce the full picture.
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9
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Tan T, Wang D. Machine learning based charge mobility prediction for organic semiconductors. J Chem Phys 2023; 158:094102. [PMID: 36889940 DOI: 10.1063/5.0134379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Abstract
Transfer integral is a crucial parameter that determines the charge mobility of organic semiconductors, and it is very sensitive to molecular packing motifs. The quantum chemical calculation of transfer integrals for all the molecular pairs in organic materials is usually an unaffordable task; fortunately, it can be accelerated by the data-driven machine learning method now. In this work, we develop machine learning models based on artificial neutral networks to predict transfer integrals accurately and efficiently for four typical organic semiconductor molecules: quadruple thiophene (QT), pentacene, rubrene, and dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT). We test various forms of features and labels and evaluate the accuracy of different models. With the implementation of a data augmentation scheme, we have achieved a very high accuracy with the determination coefficient of 0.97 and mean absolute error of 4.5 meV for QT, and similar accuracy for the other three molecules. We apply these models to studying charge transport in organic crystals with dynamic disorders at 300 K and obtain the charge mobility and anisotropy in perfect agreement with the brutal force quantum chemical calculation. If more molecular packings representing the amorphous phase of organic solids are supplemented to the dataset, the current models can be refined to study charge transport in organic thin films with polymorphs and static disorders.
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Affiliation(s)
- Tianhao Tan
- Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, People's Republic of China and MOE Key Laboratory of Organic OptoElectronics and Molecular Engineering, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Dong Wang
- Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, People's Republic of China and MOE Key Laboratory of Organic OptoElectronics and Molecular Engineering, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
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10
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Protein engineering for electrochemical biosensors. Curr Opin Biotechnol 2022; 76:102751. [PMID: 35777077 DOI: 10.1016/j.copbio.2022.102751] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/14/2022] [Accepted: 06/02/2022] [Indexed: 11/23/2022]
Abstract
The development of electrochemical biosensors has gained tremendous attention. Protein engineering has been applied for enhancing properties of native redox enzymes, such as selectivity, sensitivity, and stability required for applicable biosensors. This review highlights recent advances of protein engineering to improve enzymatic catalysis of biosensors, facilitate electron transfer and enzyme immobilization, and construct allosteric protein biosensors. The pros and cons of different protein engineering strategies are briefly discussed, and perspectives are further provided.
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11
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Cignoni E, Cupellini L, Mennucci B. A fast method for electronic couplings in embedded multichromophoric systems. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 34:304004. [PMID: 35552268 DOI: 10.1088/1361-648x/ac6f3c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/12/2022] [Indexed: 06/15/2023]
Abstract
Electronic couplings are key to understanding exciton delocalization and transport in natural and artificial light harvesting processes. We develop a method to compute couplings in multichromophoric aggregates embedded in complex environments without running expensive quantum chemical calculations. We use a transition charge approximation to represent the quantum mechanical transition densities of the chromophores and an atomistic and polarizable classical model to describe the environment atoms. We extend our framework to estimate transition charges directly from the chromophore geometry, i.e., bypassing completely the quantum mechanical calculations using a regression approach. The method allows to rapidly compute accurate couplings for a large number of geometries along molecular dynamics trajectories.
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Affiliation(s)
- Edoardo Cignoni
- Dipartimento di Chimica e Chimica Industriale, University of Pisa, via G. Moruzzi 13, 56124, Pisa, Italy
| | - Lorenzo Cupellini
- Dipartimento di Chimica e Chimica Industriale, University of Pisa, via G. Moruzzi 13, 56124, Pisa, Italy
| | - Benedetta Mennucci
- Dipartimento di Chimica e Chimica Industriale, University of Pisa, via G. Moruzzi 13, 56124, Pisa, Italy
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12
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Sivaraman G, Jackson NE. Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning. J Chem Theory Comput 2022; 18:1129-1141. [PMID: 35020388 DOI: 10.1021/acs.jctc.1c01001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.
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Affiliation(s)
- Ganesh Sivaraman
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Nicholas E Jackson
- Department of Chemistry, University of Illinois at Urbana-Champaign, 505 South Mathews Avenue, Urbana, Illinois 61801, United States
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13
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Brian D, Sun X. Charge-Transfer Landscape Manifesting the Structure-Rate Relationship in the Condensed Phase Via Machine Learning. J Phys Chem B 2021; 125:13267-13278. [PMID: 34825563 DOI: 10.1021/acs.jpcb.1c08260] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in an explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with R2 > 0.97 and both mean absolute error and root-mean-square error within chemical accuracy. The CT landscape for millions of molecular dynamics sampled structures is thus constructed, which allows for instant prediction of CT rate properties, given any conformation of the molecule. We demonstrate some immediate utilities of the CT landscape such as calculating the ensemble-averaged CT rate constant and interpreting the effects of molecular structural features on the CT rate. The unprecedented CT landscape will be useful for investigating real-time CT dynamics in nanoscale- and mesoscale-condensed phase systems and for the optimal fabrication design for homogeneous and heterogeneous optoelectronic devices.
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Affiliation(s)
- Dominikus Brian
- Division of Arts and Sciences, NYU Shanghai, 1555 Century Avenue, Shanghai 200122, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China.,Department of Chemistry, New York University, New York, New York 10003, United States
| | - Xiang Sun
- Division of Arts and Sciences, NYU Shanghai, 1555 Century Avenue, Shanghai 200122, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China.,Department of Chemistry, New York University, New York, New York 10003, United States.,State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai 200241, China
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14
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Sahu H, Li H, Chen L, Rajan AC, Kim C, Stingelin N, Ramprasad R. An Informatics Approach for Designing Conducting Polymers. ACS APPLIED MATERIALS & INTERFACES 2021; 13:53314-53322. [PMID: 34038635 DOI: 10.1021/acsami.1c04017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Doping conjugated polymers, which are potential candidates for the next generation of organic electronics, is an effective strategy for manipulating their electrical conductivity. However, selecting a suitable polymer-dopant combination is exceptionally challenging because of the vastness of the chemical, configurational, and morphological spaces one needs to search. In this work, high-performance surrogate models, trained on available experimentally measured data, are developed to predict the p-type electrical conductivity and are used to screen a large candidate hypothetical data set of more than 800 000 polymer-dopant combinations. Promising candidates are identified for synthesis and device fabrication. Additionally, new design guidelines are extracted that verify and extend knowledge on important molecular fragments that correlate to high conductivity. Conductivity prediction models are also deployed at www.polymergenome.org for broader open-access community use.
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Affiliation(s)
- Harikrishna Sahu
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hongmo Li
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Lihua Chen
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Arunkumar Chitteth Rajan
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chiho Kim
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Natalie Stingelin
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Rampi Ramprasad
- Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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15
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Abstract
Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.
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16
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Feng J, Wang H, Ji Y, Li Y. Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning. NANO SELECT 2021. [DOI: 10.1002/nano.202100006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Jie Feng
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon‐Based Functional Materials & Devices Soochow University Suzhou Jiangsu China
| | - Hongshuai Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon‐Based Functional Materials & Devices Soochow University Suzhou Jiangsu China
| | - Yujin Ji
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon‐Based Functional Materials & Devices Soochow University Suzhou Jiangsu China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon‐Based Functional Materials & Devices Soochow University Suzhou Jiangsu China
- Macao Institute of Materials Science and Engineering Macau University of Science and Technology, Taipa, Macau SAR Macau China
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17
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Abstract
Four decades of molecular theory and computation have helped form the modern understanding of the physical chemistry of organic semiconductors. Whereas these efforts have historically centered around characterizations of electronic structure at the single-molecule or dimer scale, emerging trends in noncrystalline molecular and polymeric semiconductors are motivating the need for modeling techniques capable of morphological and electronic structure predictions at the mesoscale. Provided the challenges associated with these prediction tasks, the community has begun to evolve a computational toolkit for organic semiconductors incorporating techniques from the fields of soft matter, coarse-graining, and machine learning. Here, we highlight recent advances in coarse-grained methodologies aimed at the multiscale characterization of noncrystalline organic semiconductors. As organic semiconductor performance is dependent on the interplay of mesoscale morphology and molecular electronic structure, specific emphasis is placed on coarse-grained modeling approaches capable of both structural and electronic predictions without recourse to all-atom representations.
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Affiliation(s)
- Nicholas E Jackson
- Department of Chemistry, University of Illinois, Urbana-Champaign, Urbana, Illinois 61801, United States
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18
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Aggarwal A, Vinayak V, Bag S, Bhattacharyya C, Waghmare UV, Maiti PK. Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model. J Chem Inf Model 2020; 61:106-114. [PMID: 33320660 DOI: 10.1021/acs.jcim.0c01072] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics and biological research. The charge migration rate is controlled by the electronic couplings between the two nucleobases of DNA/RNA. These electronic couplings strongly depend on the intermolecular geometry and orientation. Estimating these electronic couplings for all the possible relative geometries of molecules using the computationally demanding first-principles calculations requires a lot of time and computational resources. In this article, we present a machine learning (ML)-based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA and bypass the computationally expensive first-principles calculations. Using the Coulomb matrix representation which encodes the atomic identities and coordinates of the DNA base pairs to prepare the input dataset, we train a feedforward neural network model. Our neural network (NN) model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a mean absolute error (MAE) of less than 0.014 eV. We further use the NN-predicted electronic coupling values to compute the dsDNA/dsRNA conductance.
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Affiliation(s)
- Abhishek Aggarwal
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Vinayak Vinayak
- Undergraduate Program, Indian Institute of Science, Bangalore 560012, India
| | - Saientan Bag
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Chiranjib Bhattacharyya
- Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India
| | - Umesh V Waghmare
- Theoretical Sciences Unit, Jawaharlal Nehru Center for Advanced Scientific Research, Jakkur P.O., Bangalore 560064, India
| | - Prabal K Maiti
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
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19
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Wang CI, Joanito I, Lan CF, Hsu CP. Artificial neural networks for predicting charge transfer coupling. J Chem Phys 2020; 153:214113. [PMID: 33291923 DOI: 10.1063/5.0023697] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Quantum chemistry calculations have been very useful in providing many key detailed properties and enhancing our understanding of molecular systems. However, such calculation, especially with ab initio models, can be time-consuming. For example, in the prediction of charge-transfer properties, it is often necessary to work with an ensemble of different thermally populated structures. A possible alternative to such calculations is to use a machine-learning based approach. In this work, we show that the general prediction of electronic coupling, a property that is very sensitive to intermolecular degrees of freedom, can be obtained with artificial neural networks, with improved performance as compared to the popular kernel ridge regression method. We propose strategies for optimizing the learning rate and batch size, improving model performance, and further evaluating models to ensure that the physical signatures of charge-transfer coupling are well reproduced. We also address the effect of feature representation as well as statistical insights obtained from the loss function and the data structure. Our results pave the way for designing a general strategy for training such neural-network models for accurate prediction.
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Affiliation(s)
- Chun-I Wang
- Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan
| | | | - Chang-Feng Lan
- Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan
| | - Chao-Ping Hsu
- Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan
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20
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Bahlke MP, Mogos N, Proppe J, Herrmann C. Exchange Spin Coupling from Gaussian Process Regression. J Phys Chem A 2020; 124:8708-8723. [DOI: 10.1021/acs.jpca.0c05983] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Marc Philipp Bahlke
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
| | - Natnael Mogos
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
| | - Jonny Proppe
- Institute of Physical Chemistry, Georg-August University, Tammannstr. 6, 37077 Göttingen, Germany
| | - Carmen Herrmann
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
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21
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Bag S, Aggarwal A, Maiti PK. Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA. J Phys Chem A 2020; 124:7658-7664. [DOI: 10.1021/acs.jpca.0c04368] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Saientan Bag
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Abhishek Aggarwal
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Prabal K. Maiti
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
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22
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Farahvash A, Lee CK, Sun Q, Shi L, Willard AP. Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics. J Chem Phys 2020; 153:074111. [DOI: 10.1063/5.0016009] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Affiliation(s)
- Ardavan Farahvash
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | | | - Qiming Sun
- Tencent America, Palo Alto, California 94306, USA
| | - Liang Shi
- Department of Chemistry and Chemical Biology, University of California, Merced, California 95343, USA
| | - Adam P. Willard
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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23
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Krämer M, Dohmen PM, Xie W, Holub D, Christensen AS, Elstner M. Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians. J Chem Theory Comput 2020; 16:4061-4070. [DOI: 10.1021/acs.jctc.0c00246] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mila Krämer
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
- Institute of Biological Interfaces (IBG2), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Philipp M. Dohmen
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
- Institute of Biological Interfaces (IBG2), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Weiwei Xie
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Daniel Holub
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Anders S. Christensen
- Department of Chemistry, National Center for Computational Design and Discovery of Novel Materials (MARVEL), Institute of Physical Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Marcus Elstner
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
- Institute of Biological Interfaces (IBG2), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
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24
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Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties. TRENDS IN CHEMISTRY 2020. [DOI: 10.1016/j.trechm.2020.03.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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25
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Kraka E, Zou W, Tao Y. Decoding chemical information from vibrational spectroscopy data: Local vibrational mode theory. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1480] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Elfi Kraka
- Department of Chemistry Southern Methodist University Dallas Texas USA
| | - Wenli Zou
- Institute of Modern Physics Northwest University and Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi'an Shaanxi PR China
| | - Yunwen Tao
- Department of Chemistry Southern Methodist University Dallas Texas USA
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26
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Hsu CP. Reorganization energies and spectral densities for electron transfer problems in charge transport materials. Phys Chem Chem Phys 2020; 22:21630-21641. [DOI: 10.1039/d0cp02994g] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Various contributions to the outer reorganization energy of an electron transfer system and their theoretical and computational aspects have been discussed.
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Affiliation(s)
- Chao-Ping Hsu
- 128 Academia Road Section 2
- Institute of Chemistry
- Academia Sinica
- Taipei
- Taiwan
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