1
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Kohn JT, Grimme S, Hansen A. A semi-automated quantum-mechanical workflow for the generation of molecular monolayers and aggregates. J Chem Phys 2024; 161:124707. [PMID: 39319657 DOI: 10.1063/5.0230341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 09/10/2024] [Indexed: 09/26/2024] Open
Abstract
Organic electronics (OE) such as organic light-emitting diodes or organic solar cells represent an important and innovative research area to achieve global goals like environmentally friendly energy production. To accelerate OE material discovery, various computational methods are employed. For the initial generation of structures, a molecular cluster approach is employed. Here, we present a semi-automated workflow for the generation of monolayers and aggregates using the GFNn-xTB methods and composite density functional theory (DFT-3c). Furthermore, we present the novel D11A8MERO dye interaction energy benchmark with high-level coupled cluster reference interaction energies for the assessment of efficient quantum chemical and force-field methods. GFN2-xTB performs similar to low-cost DFT, reaching DFT/mGGA accuracy at two orders of magnitude lower computational cost. As an example application, we investigate the influence of the dye aggregate size on the optical and electrical properties and show that at least four molecules in a cluster model are needed for a qualitatively reasonable description.
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Affiliation(s)
- J T Kohn
- Mulliken Center for Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany
| | - S Grimme
- Mulliken Center for Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany
| | - A Hansen
- Mulliken Center for Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany
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2
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Ghalami F, Dohmen PM, Krämer M, Elstner M, Xie W. Nonadiabatic Simulation of Exciton Dynamics in Organic Semiconductors Using Neural Network-Based Frenkel Hamiltonian and Gradients. J Chem Theory Comput 2024; 20:6160-6174. [PMID: 38976696 DOI: 10.1021/acs.jctc.4c00220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In this study, we present a multiscale method to simulate the propagation of Frenkel singlet excitons in organic semiconductors (OSCs). The approach uses neural network models to train a Frenkel-type Hamiltonian and its gradient, obtained by the long-range correction version of density functional tight-binding with self-consistent charges. Our models accurately predict site energies, excitonic couplings, and corresponding gradients, essential for the nonadiabatic molecular dynamics simulations. Combined with the fewest switches surface hopping algorithm, the method was applied to four representative OSCs: anthracene, pentacene, perylenediimide, and diindenoperylene. The simulated exciton diffusion constants align well with experimental and reported theoretical values and offer valuable insights into exciton dynamics in OSCs.
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Affiliation(s)
- Farhad Ghalami
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
- Institute of Nano Technology (INT), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Philipp M Dohmen
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Mila Krämer
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Marcus Elstner
- Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
- Institute of Nano Technology (INT), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Institute of Biological Interfaces (IBG-2), Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Weiwei Xie
- Frontiers Science Center for New Organic Matter, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), State Key Laboratory of Advanced Chemical Power Sources, College of Chemistry, Nankai University, Tianjin 300071, China
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3
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Burke C, Makki H, Troisi A. From Chemical Drawing to Electronic Properties of Semiconducting Polymers in Bulk: A Tool for Chemical Discovery. J Chem Theory Comput 2024; 20:4019-4028. [PMID: 38642040 PMCID: PMC11099970 DOI: 10.1021/acs.jctc.3c01417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/22/2024]
Abstract
A quantum chemistry (QC)/molecular dynamics (MD) scheme is developed to calculate electronic properties of semiconducting polymers in three steps: (i) constructing the polymer force field through a unified workflow, (ii) equilibrating polymer models, and (iii) calculating electronic structure properties (e.g., density of states and localization length) from the equilibrated models by QC approaches. Notably, as the second step of this scheme is generally the most time-consuming one, we introduce an alternative method to compute thermally averaged electronic properties in bulk, based on the simulation of a polymer chain in the solution of its repeat units, which is shown to reproduce the microstructure of polymer chains and their electrostatic effect (successfully tested for five benchmark polymers) 10 times faster than state-of-the-art methods. In fact, this scheme offers a consistent and speedy way of estimating electronic properties of polymers from their chemical drawings, thus ensuring the availability of a homogeneous set of simulations to derive structure-property relationships and material design principles. As an example, we show how the electrostatic effect of the polymer chain environment can disturb the localized electronic states at the band tails and how this effect is more significant in the case of diketopyrrolopyrrole polymers as compared to indacenodithiophene and dithiopheneindenofluorene ones.
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Affiliation(s)
- Colm Burke
- Department of Chemistry and
Materials Innovation Factory, University
of Liverpool, Liverpool L69 7ZD, U.K.
| | - Hesam Makki
- Department of Chemistry and
Materials Innovation Factory, University
of Liverpool, Liverpool L69 7ZD, U.K.
| | - Alessandro Troisi
- Department of Chemistry and
Materials Innovation Factory, University
of Liverpool, Liverpool L69 7ZD, U.K.
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4
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Meng QY, Wang R, Shao HY, Wang YL, Wen XL, Yao CY, Qiao J. Precise Regulation on the Bond Dissociation Energy of Exocyclic C-N Bonds in Various N-Heterocycle Electron Donors via Machine Learning. J Phys Chem Lett 2024; 15:4422-4429. [PMID: 38626393 DOI: 10.1021/acs.jpclett.4c00705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Heterocycles with saturated N atoms (HetSNs) are widely used electron donors in organic light-emitting diode (OLED) materials. Their relatively low bond dissociation energy (BDE) of exocyclic C-N bonds has been closely related to material intrinsic stability and even device lifetime. Thus, it is imperative to realize fast prediction and precise regulation of those C-N BDEs, which demands a deep understanding of the relationship between the molecular structure and BDE. Herein, via machine learning (ML), we rapidly and accurately predicted C-N BDEs in various HetSNs and found that five-membered HetSNs (5-HetSNs) have much higher BDEs than almost all 6-HetSNs, except emerging boron-N blocks. Thorough analysis disclosed that high aromaticity is the foremost factor accounting for the high BDE of 5-HetSNs, and introducing intramolecular hydrogen-bond or electron-withdrawing moieties could also increase BDE. Importantly, the ML models performed well in various realistic OLED materials, showing great potential in characterizing material intrinsic stability for high-throughput virtual-screening and material design efforts.
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Affiliation(s)
- Qing-Yu Meng
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Rui Wang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Hao-Yun Shao
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yi-Lei Wang
- Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Xue-Liang Wen
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Cheng-Yu Yao
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
| | - Juan Qiao
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, People's Republic of China
- Laboratory for Flexible Electronics Technology, Tsinghua University, Beijing 100084, People's Republic of China
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5
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Mao Y, Yao X, Yu Z, An Z, Ma H. Ground-State Orbital Descriptors for Accelerated Development of Organic Room-Temperature Phosphorescent Materials. Angew Chem Int Ed Engl 2024; 63:e202318836. [PMID: 38141053 DOI: 10.1002/anie.202318836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 12/24/2023]
Abstract
Organic materials with room-temperature phosphorescence (RTP) are in high demand for optoelectronics and bioelectronics. Developing RTP materials highly relies on expert experience and costly excited-state calculations. It is a challenge to find a tool for effectively screening RTP materials. Herein we first establish ground-state orbital descriptors (πFMOs ) derived from the π-electron component of the frontier molecular orbitals to characterize the RTP lifetime (τp ), achieving a balance in screening efficiency and accuracy. Using the πFMOs , a data-driven machine learning model gains a high accuracy in classifying long τp , filtering out 836 candidates with long-lived RTP from a virtual library of 19,295 molecules. With the aid of the excited-state calculations, 287 compounds are predicted with high RTP efficiency. Impressively, experiments further confirm the reliability of this workflow, opening a novel avenue for designing high-performance RTP materials for potential applications.
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Affiliation(s)
- Yufeng Mao
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
- The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005 Fujian, China
| | - Xiaokang Yao
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Ze Yu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Zhongfu An
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
- The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005 Fujian, China
| | - Huili Ma
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
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6
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Yuan T, Song X, Shi Y, Wei S, Han Y, Yang L, Zhang Y, Li X, Li Y, Shen L, Fan L. Perspectives on development of optoelectronic materials in artificial intelligence age. Chem Asian J 2024:e202301088. [PMID: 38317532 DOI: 10.1002/asia.202301088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.
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Affiliation(s)
- Ting Yuan
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Xianzhi Song
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yuxin Shi
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Shuyan Wei
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yuyi Han
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Linjuan Yang
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yang Zhang
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Xiaohong Li
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yunchao Li
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Lin Shen
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Louzhen Fan
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
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7
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Kadan A, Ryczko K, Wildman A, Wang R, Roitberg A, Yamazaki T. Accelerated Organic Crystal Structure Prediction with Genetic Algorithms and Machine Learning. J Chem Theory Comput 2023; 19:9388-9402. [PMID: 38059458 DOI: 10.1021/acs.jctc.3c00853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
We present a high-throughput, end-to-end pipeline for organic crystal structure prediction (CSP)─the problem of identifying the stable crystal structures that will form from a given molecule based only on its molecular composition. Our tool uses neural network potentials to allow for efficient screening and structural relaxation of generated crystal candidates. Our pipeline consists of two distinct stages: random search, whereby crystal candidates are randomly generated and screened, and optimization, where a genetic algorithm (GA) optimizes this screened population. We assess the performance of each stage of our pipeline on 21 molecules taken from the Cambridge Crystallographic Data Centre's CSP blind tests. We show that random search alone yields matches for ≈50% of targets. We then validate the potential of our full pipeline, making use of the GA to optimize the root-mean-square deviation between crystal candidates and the experimentally derived structure. With this approach, we are able to find matches for ≈80% of candidates with 10-100 times smaller initial population sizes than when using random search. Lastly, we run our full pipeline with an ANI model that is trained on a small data set of molecules extracted from crystal structures in the Cambridge Structural Database, generating ≈60% of targets. By leveraging machine learning models trained to predict energies at the density functional theory level, our pipeline has the potential to approach the accuracy of ab initio methods and the efficiency of empirical force fields.
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Affiliation(s)
- Amit Kadan
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Kevin Ryczko
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Andrew Wildman
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Rodrigo Wang
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Adrian Roitberg
- Department of Chemistry, University of Florida, P.O. Box 117200, Gainesville, Florida 32611-7200, United States
| | - Takeshi Yamazaki
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
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8
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Li CH, Tabor DP. Generative organic electronic molecular design informed by quantum chemistry. Chem Sci 2023; 14:11045-11055. [PMID: 37860647 PMCID: PMC10583709 DOI: 10.1039/d3sc03781a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023] Open
Abstract
Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to first find a set of diverse promising molecules, then demonstrate the framework's ability to exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties versus synthesizability. Moreover, we are able to find several different design principles used by chemists for the design of singlet fission and triplet-triplet annihilation molecules.
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Affiliation(s)
- Cheng-Han Li
- Department of Chemistry, Texas A&M University College Station TX 77842 USA
| | - Daniel P Tabor
- Department of Chemistry, Texas A&M University College Station TX 77842 USA
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9
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Weiss T, Mayo Yanes E, Chakraborty S, Cosmo L, Bronstein AM, Gershoni-Poranne R. Guided diffusion for inverse molecular design. NATURE COMPUTATIONAL SCIENCE 2023; 3:873-882. [PMID: 38177755 DOI: 10.1038/s43588-023-00532-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/06/2023] [Indexed: 01/06/2024]
Abstract
The holy grail of materials science is de novo molecular design, meaning engineering molecules with desired characteristics. The introduction of generative deep learning has greatly advanced efforts in this direction, yet molecular discovery remains challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion model for inverse molecular design that combines an equivariant graph neural net for property prediction and a generative diffusion model. We demonstrate GaUDI's effectiveness in designing molecules for organic electronic applications by using single- and multiple-objective tasks applied to a generated dataset of 475,000 polycyclic aromatic systems. GaUDI shows improved conditional design, generating molecules with optimal properties and even going beyond the original distribution to suggest better molecules than those in the dataset. In addition to point-wise targets, GaUDI can also be guided toward open-ended targets (for example, a minimum or maximum) and in all cases achieves close to 100% validity of generated molecules.
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Affiliation(s)
- Tomer Weiss
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
| | - Eduardo Mayo Yanes
- Schulich Faculty of Chemistry, Technion-Israel Institute of Technology, Haifa, Israel
| | | | - Luca Cosmo
- University Ca' Foscari of Venice, Venice, Italy
| | - Alex M Bronstein
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel.
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10
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Guo R, Yu J, Guo Z. Virtual Screening and Binding Analysis of Potential CD58 Inhibitors in Colorectal Cancer (CRC). Molecules 2023; 28:6819. [PMID: 37836662 PMCID: PMC10574072 DOI: 10.3390/molecules28196819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Human cell surface receptor CD58, also known as lymphocyte function-associated antigen 3 (LFA-3), plays a critical role in the early stages of immune response through interacting with CD2. Recent research identified CD58 as a surface marker of colorectal cancer (CRC), which can upregulate the Wnt pathway and promote self-renewal of colorectal tumor-initiating cells (CT-ICs) by degradation of Dickkopf 3. In addition, it was also shown that knockdown of CD58 significantly impaired tumor growth. In this study, we developed a structure-based virtual screening pipeline using Autodock Vina and binding analysis and identified a group of small molecular compounds having the potential to bind with CD58. Five of them significantly inhibited the growth of the SW620 cell line in the following in vitro studies. Their proposed binding models were further verified by molecular dynamics (MD) simulations, and some pharmaceutically relevant chemical and physical properties were predicted. The hits described in this work may be considered interesting leads or structures for the development of new and more efficient CD58 inhibitors.
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Affiliation(s)
- Rong Guo
- Computational Biology, Bioinformatics and Genomics Program, Department of Biological Sciences, University of Maryland, College Park, MD 20742, USA
| | - Jiangnan Yu
- International Cancer Center, Shenzhen University Medical School, Shenzhen 518054, China
| | - Zhikun Guo
- International Cancer Center, Shenzhen University Medical School, Shenzhen 518054, China
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11
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Omar Ö, Xie X, Troisi A, Padula D. Identification of Unknown Inverted Singlet-Triplet Cores by High-Throughput Virtual Screening. J Am Chem Soc 2023; 145:19790-19799. [PMID: 37639703 PMCID: PMC10510316 DOI: 10.1021/jacs.3c05452] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 08/31/2023]
Abstract
Molecules where the energy of the lowest excited singlet state is found below the energy of the lowest triplet state (inverted singlet-triplet molecules) are extremely rare. It is particularly challenging to discover new ones through virtual screening because the required wavefunction-based methods are expensive and unsuitable for high-throughput calculations. Here, we devised a virtual screening approach where the molecules to be considered with advanced methods are pre-selected with increasingly more sophisticated filters that include the evaluation of the HOMO-LUMO exchange integral and approximate CASSCF calculations. A final set of 7 candidates (0.05% of the initial 15 000) were verified to possess inversion between singlet and triplet states with state-of-the-art multireference methods (MS-CASPT2). One of them is deemed of particular interest because it is unrelated to other proposals made in the literature.
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Affiliation(s)
- Ömer
H. Omar
- Department
of Chemistry, University of Liverpool, Liverpool L69 7ZD, U.K.
| | - Xiaoyu Xie
- Department
of Chemistry, University of Liverpool, Liverpool L69 7ZD, U.K.
| | - Alessandro Troisi
- Department
of Chemistry, University of Liverpool, Liverpool L69 7ZD, U.K.
| | - Daniele Padula
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università
di Siena, Via A. Moro
2, Siena 53100, Italy
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12
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Wang X, Wang S, Wang J, Yin S. Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods. J Phys Chem A 2023. [PMID: 37418642 DOI: 10.1021/acs.jpca.3c01714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Genetic algorithm (GA) optimization coupled with the semiempirical intermediate neglect of differential overlap (INDO)/CIS method is presented to inversely design the red thermally activation delayed fluorescent (TADF) molecules. According to the predefined donor-acceptor (DA) library to build an ADn-type TADF candidate, we utilized the chemical notation language SMILES code to generate a TADF molecule and apply the RDKit program to produce the initial 3D molecular structure. A combined fitness function is proposed to evaluate the performance of the functional-lead TADF molecule. The fitness function includes three key parameters, i.e., the emission wavelength, the energy gap (ΔEST) between the lowest singlet (S1)- and triplet (T1)-excited states, and the oscillator strengths for electron transition from S0 and S1. A cheap QM method, i.e., INDO/CIS, on the basis of an xTB-optimized molecular geometry is applied to quickly calculate the fitness function. Finally, the GA approach is utilized to globally search for the wavelength-specific TADF molecules under our predefined DA library, and the optimum 630 nm red and 660 nm deep red TADF molecules are inversely designed according to the evolution of molecular fitness functions.
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Affiliation(s)
- Xubin Wang
- School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xian 710119, China
| | - Shiqi Wang
- School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xian 710119, China
| | - Jingwen Wang
- School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xian 710119, China
| | - Shiwei Yin
- School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xian 710119, China
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13
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Su A, Zhang X, Zhang C, Ding D, Yang YF, Wang K, She YB. Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts. Phys Chem Chem Phys 2023; 25:10536-10549. [PMID: 36987933 DOI: 10.1039/d3cp00917c] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
A deep transfer learning approach is used to predict HOMO/LUMO energies of organic materials with a small amount of training data.
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Affiliation(s)
- An Su
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Xin Zhang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Chengwei Zhang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Debo Ding
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Yun-Fang Yang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Keke Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Yuan-Bin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
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14
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Sami S, Alessandri R, W. Wijaya JB, Grünewald F, de Vries AH, Marrink SJ, Broer R, Havenith RWA. Strategies for Enhancing the Dielectric Constant of Organic Materials. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2022; 126:19462-19469. [PMID: 36425002 PMCID: PMC9677499 DOI: 10.1021/acs.jpcc.2c05682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/21/2022] [Indexed: 05/30/2023]
Abstract
High dielectric constant organic semiconductors, often obtained by the use of ethylene glycol (EG) side chains, have gained attention in recent years in the efforts of improving the device performance for various applications. Dielectric constant enhancements due to EGs have been demonstrated extensively, but various effects, such as the choice of the particular molecule and the frequency and temperature regime, that determine the extent of this enhancement require further understanding. In this work, we study these effects by means of polarizable molecular dynamics simulations on a carefully selected set of fullerene derivatives with EG side chains. The selection allows studying the dielectric response in terms of both the number and length of EG chains and also the choice of the group connecting the fullerene to the EG chain. The computed time- and frequency-dependent dielectric responses reveal that the experimentally observed rise of the dielectric constant within the kilo/megahertz regime for some molecules is likely due to the highly stretched dielectric response of the EGs: the initial sharp increase over the first few nanoseconds is followed by a smaller but persistent increase in the range of microseconds. Additionally, our computational protocol allows the separation of different factors that contribute to the overall dielectric constant, providing insights to make several molecular design guides for future organic materials in order to enhance their dielectric constant further.
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Affiliation(s)
- Selim Sami
- Stratingh
Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AGGroningen, The Netherlands
- Zernike
Institute for Advanced Materials, University
of Groningen, Nijenborgh
4, 9747 AGGroningen, The Netherlands
| | - Riccardo Alessandri
- Zernike
Institute for Advanced Materials, University
of Groningen, Nijenborgh
4, 9747 AGGroningen, The Netherlands
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AGGroningen, The Netherlands
| | - Jeff B. W. Wijaya
- Zernike
Institute for Advanced Materials, University
of Groningen, Nijenborgh
4, 9747 AGGroningen, The Netherlands
| | - Fabian Grünewald
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AGGroningen, The Netherlands
| | - Alex H. de Vries
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AGGroningen, The Netherlands
| | - Siewert J. Marrink
- Zernike
Institute for Advanced Materials, University
of Groningen, Nijenborgh
4, 9747 AGGroningen, The Netherlands
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AGGroningen, The Netherlands
| | - Ria Broer
- Zernike
Institute for Advanced Materials, University
of Groningen, Nijenborgh
4, 9747 AGGroningen, The Netherlands
| | - Remco W. A. Havenith
- Stratingh
Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AGGroningen, The Netherlands
- Zernike
Institute for Advanced Materials, University
of Groningen, Nijenborgh
4, 9747 AGGroningen, The Netherlands
- Department
of Chemistry, Ghent University, Krijgslaan 281-(S3), B-9000Ghent, Belgium
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15
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Nematiaram T, Troisi A. Feasibility of p-Doped Molecular Crystals as Transparent Conductive Electrodes via Virtual Screening. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2022; 34:4050-4061. [PMID: 35573107 PMCID: PMC9097283 DOI: 10.1021/acs.chemmater.2c00281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/14/2022] [Indexed: 06/15/2023]
Abstract
Transparent conducting materials are an essential component of optoelectronic devices. It is proven difficult, however, to develop high-performance materials that combine the often-incompatible properties of transparency and conductivity, especially for p-type-doped materials. In this work, we have employed a large set of molecular semiconductors extracted from the Cambridge Structural Database to evaluate the likelihood of transparent conducting material technology based on p-type-doped molecular crystals. Candidates are identified imposing the condition of high highest occupied molecular orbital (HOMO) energy level (for the material to be easily dopable), high charge carrier mobility (for the material to display large conductivity when doped), and a high threshold for energy absorption (for the material to absorb radiation only in the ultraviolet). The latest condition is found to be the most stringent criterion in a virtual screening protocol on a database composed of structures with sufficiently wide two-dimensional (2D) electronic bands. Calculation of excited-state energy is shown to be essential as the HOMO-lowest unoccupied molecular orbital (LUMO) gap cannot be reliably used to predict the transparency of this material class. Molecular semiconductors with desirable mobility are transparent because they display either forbidden electronic transition(s) to the lower excited states or small exchange energy between the frontier orbitals. Both features are difficult to design but can be found in a good number of compounds through virtual screening.
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16
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Omar ÖH, Nematiaram T, Troisi A, Padula D. Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds. Sci Data 2022; 9:54. [PMID: 35165288 PMCID: PMC8844419 DOI: 10.1038/s41597-022-01142-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/21/2021] [Indexed: 01/28/2023] Open
Abstract
We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure. For each entry we provide a set of electronic properties relevant for organic materials research, and the electronic wavefunction for further calculations and/or analyses. This data set has low bias because it was not built from a set of materials designed for organic electronics, and thus it provides an excellent starting point in the search of new applications for known materials, with a great potential for novel physical insight. The data set contains molecules used as benchmarks in many fields of organic materials research, allowing to test the reliability of computational screenings for the desired application, "rediscovering" well-known molecules. This is demonstrated by a series of different applications in the field of organic materials, confirming the potential for the repurposing of known organic molecules.
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Affiliation(s)
- Ömer H Omar
- University of Liverpool, Department of Chemistry, Liverpool, L69 7ZD, UK
| | - Tahereh Nematiaram
- University of Liverpool, Department of Chemistry, Liverpool, L69 7ZD, UK
| | - Alessandro Troisi
- University of Liverpool, Department of Chemistry, Liverpool, L69 7ZD, UK.
| | - Daniele Padula
- Università di Siena, Dipartimento di Biotecnologie, Chimica e Farmacia, Siena, 53100, Italy.
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