1
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Pal Y, Fiala TA, Swords WB, Yoon TP, Schmidt JR. Predicting Emission Spectra of Heteroleptic Iridium Complexes Using Artificial Chemical Intelligence. Chemphyschem 2024; 25:e202400176. [PMID: 38752882 DOI: 10.1002/cphc.202400176] [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: 02/26/2024] [Revised: 05/15/2024] [Indexed: 07/09/2024]
Abstract
We report a deep learning-based approach to accurately predict the emission spectra of phosphorescent heteroleptic [Ir(C ∧ N ${{\rm{C}}^\wedge {\rm{N}}}$ )2(N ∧ N ${{\rm{N}}^\wedge {\rm{N}}}$ )]+ complexes, enabling the rapid discovery of novel Ir(III) chromophores for diverse applications including organic light-emitting diodes and solar fuel cells. The deep learning models utilize graph neural networks and other chemical features in architectures that reflect the inherent structure of the heteroleptic complexes, composed ofC ∧ N ${{\rm{C}}^\wedge {\rm{N}}}$ andN ∧ N ${{\rm{N}}^\wedge {\rm{N}}}$ ligands, and are thus geared towards efficient training over the dataset. By leveraging experimental emission data, our models reliably predict the full emission spectra of these complexes across various emission profiles, surpassing the accuracy of conventional DFT and correlated wavefunction methods, while simultaneously achieving robustness to the presence of imperfect (noisy, low-quality) training spectra. We showcase the potential applications for these and related models for in silico prediction of complexes with tailored emission properties, as well as in "design of experiment" contexts to reduce the synthetic burden of high-throughput screening. In the latter case, we demonstrate that the models allow us to exploit a limited amount of experimental data to explore a wide range of chemical space, thus leveraging a modest synthetic effort.
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Affiliation(s)
- Yudhajit Pal
- Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, United States
| | - Tahoe A Fiala
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, United States
| | - Wesley B Swords
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, United States
| | - Tehshik P Yoon
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, United States
| | - J R Schmidt
- Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, United States
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2
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Tannir S, Pan Y, Josephs N, Cunningham C, Hendrick NR, Beckett A, McNeely J, Beeler A, Jeffries-El M, Kolaczyk ED. Predicting Emission Wavelengths in Benzobisoxazole-Based OLEDs with Gradient Boosted Ensemble Models. J Phys Chem A 2024; 128:6116-6123. [PMID: 39008894 DOI: 10.1021/acs.jpca.4c00077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
We demonstrate the use of gradient-boosted ensemble models that accurately predict emission wavelengths in benzobis[1,2-d:4,5-d']oxazole (BBO) based fluorescent emitters. We have curated a database of 50 molecules from previously published data by the Jeffries-EL group using density functional theory (DFT) computed ground and excited state features. We consider two machine learning (ML) models based on (i) whole cruciform molecules and (ii) their constituent fragment molecules. Both ML models provide accurate predictions with root-mean-square errors between 30 and 36 nm, competitive with state-of-the-art deep learning models trained on orders of magnitude more molecules, and this accuracy holds even when tested on four new BBO emitters unseen by the models. We also provide an interpretable feature importance analysis and discuss the relevant relationships between DFT and changes in predicted emission wavelength.
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Affiliation(s)
- Shambhavi Tannir
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Yuning Pan
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, United States
| | - Nathaniel Josephs
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, United States
| | | | - Nathan R Hendrick
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Annie Beckett
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - James McNeely
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Aaron Beeler
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Malika Jeffries-El
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
- Division of Material Science and Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Eric D Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, United States
- Department of Mathematics and Statistics, McGill University, Montreal, QC H3A 0G4, Canada
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3
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Zhang C, Zhai Y, Gong Z, Duan H, She YB, Yang YF, Su A. Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data. J Cheminform 2024; 16:89. [PMID: 39080777 PMCID: PMC11290278 DOI: 10.1186/s13321-024-00886-1] [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: 03/05/2024] [Accepted: 07/21/2024] [Indexed: 08/02/2024] Open
Abstract
Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.Scientific contributionThis study verifies the feasibility of applying transfer learning to large language models in different chemical fields to help organic materials perform virtual screening. Through the comparison of transfer learning from different chemical fields to a variety of organic material molecules, the high precision virtual screening of organic materials is realized.
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Affiliation(s)
- Chengwei Zhang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - Yushuang Zhai
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - Ziyang Gong
- Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, People's Republic of China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
| | - Yuan-Bin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - Yun-Fang Yang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - An Su
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
- Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, People's Republic of China.
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4
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Xiong S, Yang X. Optical color routing enabled by deep learning. NANOSCALE 2024. [PMID: 38592716 DOI: 10.1039/d4nr00105b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors.
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Affiliation(s)
- Shijie Xiong
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China.
| | - Xianguang Yang
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China.
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5
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Nishide H. Concluding remarks: challenges and prospects in organic photonics and electronics. Faraday Discuss 2024; 250:417-426. [PMID: 38361433 DOI: 10.1039/d3fd00157a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
The Faraday Discussion meeting on 'challenges and prospects in organic and photonics and electronics' was held in Osaka, Japan, after the COVID pandemic and during the subsequent global difficulties, in the traditional face-to-face and condensed style, with many discussions, both after the short presentations and in front of the poster presentations. I would like to take this opportunity to thank the organising members, particularly Youhei Takeda and local professors, for their efforts in organising this meeting.
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Affiliation(s)
- Hiroyuki Nishide
- Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
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6
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Jung SG, Jung G, Cole JM. Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks. J Chem Inf Model 2024; 64:1486-1501. [PMID: 38422386 PMCID: PMC10934802 DOI: 10.1021/acs.jcim.3c01792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024]
Abstract
Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature. These high-throughput electronic structure calculations also incur a substantial computational cost. With the emergence of big-data initiatives, cost-effective, data-driven methods have gained traction, although their usability is highly contingent on the degree of data quality and sparsity. In this study, we present a workflow that employs deep residual convolutional neural networks (DR-CNN) and gradient boosting feature selection to predict peak optical absorption wavelengths (λmax) exclusively from SMILES representations of dye molecules and solvents; one would normally measure λmax using UV-vis absorption spectroscopy. We use a multifidelity modeling approach, integrating 34,893 DFT calculations and 26,395 experimentally derived λmax data, to deliver more accurate predictions via a Bayesian-optimized gradient boosting machine. Our approach is benchmarked against the state of the art that is reported in the scientific literature; results demonstrate that learnt representations via a DR-CNN workflow that is integrated with other machine learning methods can accelerate the design of molecules for specific optical characteristics.
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Affiliation(s)
- Son Gyo Jung
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
- ISIS
Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
- Research
Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, U.K.
| | - Guwon Jung
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
- Research
Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, U.K.
- Scientific
Computing Department, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
| | - Jacqueline M. Cole
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
- ISIS
Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
- Research
Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, U.K.
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7
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Mahato KD, Kumar U. Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123768. [PMID: 38134661 DOI: 10.1016/j.saa.2023.123768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Applications of organic dyes, ranging from basic research to industry, are functions of their photophysical properties. Two important aspects- (1) knowledge of the photophysical properties of existing dyes long before real applications and (2) discovery of new organic dyes with desired photophysical properties for either upgradation of existing or development of new applications-are needed to be addressed. These two cases are coupled together with the common goal of estimating photophysical properties with high accuracy at the minimum cost of time and money long before the hard-core laboratory experiment. For this purpose, machine learning-based techniques are the most suitable approach. In this study, we used optimized machine-learning techniques to assess a dataset of 3066 organic dyes, which were evaluated using three evaluation parameters: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The Quadratic Support Vector Machine (QSVM) was the best predictive model for RMSE-16.614, MAE-10.837, and R2-0.961 for absorption wavelengths and RMSE-23.636, MAE-16.278, and R2-0.929 for emission wavelengths. These R2 values are 0.7% and 0.4% greater than the Gradient Boost Regression Tree (GBRT) model's recently reported values of 0.954 and 0.925 for absorption and emission wavelengths, respectively. Furthermore, we estimated the quantum yield and found that the Coarse Gaussian Support Vector Machine (CGSVM) outperformed all examined models. For more validation of these models, we compared the predicted results with the experimental results of selective dyes. The proposed automated approach can be used for predicting photophysical properties without much computer programming knowledge.
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Affiliation(s)
- Kapil Dev Mahato
- Department of Physics, National Institute of Technology Jamshedpur, Jharkhand 831014, India.
| | - Uday Kumar
- Department of Physics, National Institute of Technology Jamshedpur, Jharkhand 831014, India
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8
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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9
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do Casal MT, Veys K, Bousquet MHE, Escudero D, Jacquemin D. First-Principles Calculations of Excited-State Decay Rate Constants in Organic Fluorophores. J Phys Chem A 2023; 127:10033-10053. [PMID: 37988002 DOI: 10.1021/acs.jpca.3c06191] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
In this Perspective, we discuss recent advances made to evaluate from first-principles the excited-state decay rate constants of organic fluorophores, focusing on the so-called static strategy. In this strategy, one essentially takes advantage of Fermi's golden rule (FGR) to evaluate rate constants at key points of the potential energy surfaces, a procedure that can be refined in a variety of ways. In this way, the radiative rate constant can be straightforwardly obtained by integrating the fluorescence line shape, itself determined from vibronic calculations. Likewise, FGR allows for a consistent calculation of the internal conversion (related to the non-adiabatic couplings) in the weak-coupling regime and intersystem crossing rates, therefore giving access to estimates of the emission yields when no complex photophysical phenomenon is at play. Beyond outlining the underlying theories, we summarize here the results of benchmarks performed for various types of rates, highlighting that both the quality of the vibronic calculations and the accuracy of the relative energies are crucial to reaching semiquantitative estimates. Finally, we illustrate the successes and challenges in determining the fluorescence quantum yields using a series of organic fluorophores.
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Affiliation(s)
- Mariana T do Casal
- Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven, 3001 Leuven, Belgium
| | - Koen Veys
- Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven, 3001 Leuven, Belgium
| | | | - Daniel Escudero
- Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven, 3001 Leuven, Belgium
| | - Denis Jacquemin
- Nantes Université, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
- Institut Universitaire de France (IUF), FR-75005 Paris, France
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10
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Jung SH, Park SH, Kwon NY, Park JY, Kang MJ, Koh CW, Cho MJ, Park S, Choi DH. Novel π-Extended Indolocarbazole-Based Deep-Blue Fluorescent Emitter with Remarkably Narrow Bandwidth for Solution-Processed Organic Light-Emitting Diodes. ACS APPLIED MATERIALS & INTERFACES 2023; 15:56106-56115. [PMID: 37994594 DOI: 10.1021/acsami.3c11702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
In solution-processed organic light-emitting diodes (OLEDs), achieving high color purity and efficiency is as important as that in vacuum processes. Emitters suitable for solution processing must have excellent solubility in organic solvents, high molecular weight, and compatibility with the host materials. In this study, we synthesized a deep-blue emitter that satisfies the above conditions by introducing a 1,4-bis(indolo[3,2,1-jk]carbazol-2-yl)benzene-based planar emitting core (DICz) structure and four 3,6-di-tert-butyl-9-phenyl-9H-carbazole (tCz) peripheral units, namely, 4tCz-DICz. A comparative compound, 4Hex-DICz, incorporating hexyl phenyl groups was synthesized. In contrast to 4Hex-DICz, 4tCz-DICz exhibited exceptional solubility in organic solvents and superior film-forming properties attributed to the presence of tCz units. Additionally, in the film state, the effective encapsulation of the emitting core (DICz) by the tCz units in 4tCz-DICz helps prevent undesirable molecular aggregation. The solution-processed OLEDs employing the CH-2D1 film, doped with 5 wt % 4tCz-DICz as the emitting layer, exhibited a deep-blue emission at 424 nm, characterized by a narrow bandwidth of 22 nm, and achieved a maximum external quantum efficiency (EQE) of approximately 4.0%. In contrast, the 4Hex-DICz-based device demonstrated an EQE of 2.91%. Consequently, we have successfully demonstrated that the introduction of four bulky tCz units into the DICz core is a promising molecular design strategy for the development of soluble indolocarbazole-based emitters, especially those used in high-performance deep-blue fluorescent OLEDs.
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Affiliation(s)
- Sung Hoon Jung
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Su Hong Park
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Na Yeon Kwon
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jin Young Park
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Min Ji Kang
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Chang Woo Koh
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Min Ju Cho
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Sungnam Park
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Dong Hoon Choi
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
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11
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Góger S, Sandonas LM, Müller C, Tkatchenko A. Data-driven tailoring of molecular dipole polarizability and frontier orbital energies in chemical compound space. Phys Chem Chem Phys 2023; 25:22211-22222. [PMID: 37566426 PMCID: PMC10445328 DOI: 10.1039/d3cp02256k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/27/2023] [Indexed: 08/12/2023]
Abstract
Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and the dipole polarizability. Based on the recently introduced QM7-X dataset, augmented with accurate molecular polarizability calculations as well as analysis of functional group compositions, we show that polarizability and HOMO-LUMO gap are uncorrelated when considering sufficiently extended subsets of the chemical compound space. The relation between these two properties is further analyzed on specific examples of molecules with similar composition as well as homooligomers. Remarkably, the freedom brought by the lack of correlation between molecular polarizability and HOMO-LUMO gap enables the design of novel materials, as we demonstrate on the example of organic photodetector candidates.
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Affiliation(s)
- Szabolcs Góger
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| | - Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| | - Carolin Müller
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
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12
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Bousquet MHE, Papineau TV, Veys K, Escudero D, Jacquemin D. Extensive Analysis of the Parameters Influencing Radiative Rates Obtained through Vibronic Calculations. J Chem Theory Comput 2023; 19:5525-5547. [PMID: 37494031 DOI: 10.1021/acs.jctc.3c00191] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Defining a theoretical model systematically delivering accurate ab initio predictions of the fluorescence quantum yields of organic dyes is highly desirable for designing improved fluorophores in a systematic rather than trial-and-error way. To this end, the first required step is to obtain reliable radiative rates (kr), as low kr typically precludes effective emission. In the present contribution, using a series of 10 substituted phenyls with known experimental kr, we analyze the impact of the computational protocol on the kr determined through the thermal vibration correlation function (TVCF) approach on the basis of time-dependent density functional theory (TD-DFT) calculations of the energies, structures, and vibrational parameters. Both the electronic structure (selected exchange-correlation functional, application or not of the Tamm-Dancoff approximation) and the vibronic parameters (line-shape formalism, coordinate system, potential energy surface model, and dipole expansion) are tackled. Considering all possible combinations yields more than 3500 cases, allowing to extract statistically-relevant information regarding the impact of each computational parameter on the magnitude of the estimated kr. It turns out that the selected vibronic model can have a significant impact on the computed kr, especially the potential energy surface model. This effect is of the same order of magnitude as the difference noted between B3LYP and CAM-B3LYP estimates. For the treated compounds, all evaluated functionals do deliver reasonable trends, fitting the experimental values.
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Affiliation(s)
| | | | - Koen Veys
- Department of Chemistry, KU Leuven, B-3001 Leuven, Belgium
| | | | - Denis Jacquemin
- Nantes Université, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
- Institut Universitaire de France (IUF), F-75005 Paris, France
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13
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Ruth M, Gerbig D, Schreiner PR. Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies. J Chem Theory Comput 2023. [PMID: 37418619 DOI: 10.1021/acs.jctc.3c00274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.
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Affiliation(s)
- Marcel Ruth
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Dennis Gerbig
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
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14
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Yang KR, Kyro GW, Batista VS. The landscape of computational approaches for artificial photosynthesis. NATURE COMPUTATIONAL SCIENCE 2023; 3:504-513. [PMID: 38177419 DOI: 10.1038/s43588-023-00450-1] [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: 10/14/2022] [Accepted: 04/11/2023] [Indexed: 01/06/2024]
Abstract
Artificial photosynthesis is an attractive strategy for converting solar energy into fuels, largely because the Earth receives enough solar energy in one hour to meet humanity's energy needs for an entire year. However, developing devices for artificial photosynthesis remains difficult and requires computational approaches to guide and assist the interpretation of experiments. In this Perspective, we discuss current and future computational approaches, as well as the challenges of designing and characterizing molecular assemblies that absorb solar light, transfer electrons between interfaces, and catalyze water-splitting and fuel-forming reactions.
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Affiliation(s)
- Ke R Yang
- Department of Chemistry, Yale University, New Haven, CT, USA
- Energy Sciences Institute, Yale University, West Haven, CT, USA
| | - Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, CT, USA.
- Energy Sciences Institute, Yale University, West Haven, CT, USA.
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15
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Hung SH, Ye ZR, Cheng CF, Chen B, Tsai MK. Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach. J Chem Theory Comput 2023. [PMID: 37126224 DOI: 10.1021/acs.jctc.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
An assessment of modifying the SchNET model for the predictions of experimental molecular photophysical properties, including absorption energy (ΔEabs), emission energy (ΔEemi), and photoluminescence quantum yield (PLQY), was reported. The solution environment was properly introduced outside the interaction layers of SchNET for not overly amplifying the solute-solvent interactions, particularly being supported by the changes of prediction errors between the presence and absence of the solvent effect. Two featurization schemes under the framework of the Schnet-bondstep approach, with featuring the concepts of reduced-atomic-number and reduced-atomic-neighbor, were demonstrated. These featurized models can consequently provide fine predictions for ΔEabs and ΔEemi with errors less than 0.1 eV. The corresponding predictions of PLQY were shown to be comparable to the previous graph convolution network model.
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Affiliation(s)
- Sheng-Hsuan Hung
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Zong-Rong Ye
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Chi-Feng Cheng
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Berlin Chen
- Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Ming-Kang Tsai
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
- Department of Chemistry, Fu-Jen Catholic University, New Taipei City 24205, Taiwan
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16
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Petrusevich EF, Bousquet MHE, Ośmiałowski B, Jacquemin D, Luis JM, Zaleśny R. Cost-Effective Simulations of Vibrationally-Resolved Absorption Spectra of Fluorophores with Machine-Learning-Based Inhomogeneous Broadening. J Chem Theory Comput 2023; 19:2304-2315. [PMID: 37096370 PMCID: PMC10134414 DOI: 10.1021/acs.jctc.2c01285] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
The results of electronic and vibrational structure simulations are an invaluable support for interpreting experimental absorption/emission spectra, which stimulates the development of reliable and cost-effective computational protocols. In this work, we contribute to these efforts and propose an efficient first-principle protocol for simulating vibrationally-resolved absorption spectra, including nonempirical estimations of the inhomogeneous broadening. To this end, we analyze three key aspects: (i) a metric-based selection of density functional approximation (DFA) so to benefit from the computational efficiency of time-dependent density function theory (TD-DFT) while safeguarding the accuracy of the vibrationally-resolved spectra, (ii) an assessment of two vibrational structure schemes (vertical gradient and adiabatic Hessian) to compute the Franck-Condon factors, and (iii) the use of machine learning to speed up nonempirical estimations of the inhomogeneous broadening. In more detail, we predict the absorption band shapes for a set of 20 medium-sized fluorescent dyes, focusing on the bright ππ★ S0 → S1 transition and using experimental results as references. We demonstrate that, for the studied 20-dye set which includes structures with large structural variability, the preselection of DFAs based on an easily accessible metric ensures accurate band shapes with respect to the reference approach and that range-separated functionals show the best performance when combined with the vertical gradient model. As far as band widths are concerned, we propose a new machine-learning-based approach for determining the inhomogeneous broadening induced by the solvent microenvironment. This approach is shown to be very robust offering inhomogeneous broadenings with errors as small as 2 cm-1 with respect to genuine electronic-structure calculations, with a total CPU time reduced by 98%.
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Affiliation(s)
- Elizaveta F. Petrusevich
- Faculty of Chemistry, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, PL-50370 Wrocław, Poland
- Institute of Computational Chemistry and Catalysis and Department of Chemistry, University of Girona, Campus de Montilivi, 17003 Girona, Catalonia, Spain
| | | | - Borys Ośmiałowski
- Faculty of Chemistry, Nicolaus Copernicus University, Gagarina Street 7, PL-87-100 Toruń, Poland
| | - Denis Jacquemin
- Nantes Université, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
- Institut Universitaire de France (IUF), F-75005 Paris, France
| | - Josep M. Luis
- Institute of Computational Chemistry and Catalysis and Department of Chemistry, University of Girona, Campus de Montilivi, 17003 Girona, Catalonia, Spain
| | - Robert Zaleśny
- Faculty of Chemistry, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, PL-50370 Wrocław, Poland
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17
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Kim JM, Lee KH, Lee JY. Extracting Polaron Recombination from Electroluminescence in Organic Light-Emitting Diodes by Artificial Intelligence. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209953. [PMID: 36788120 DOI: 10.1002/adma.202209953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Direct exploring the electroluminescence (EL) of organic light-emitting diodes (OLEDs) is a challenge due to the complicated processes of polarons, excitons, and their interactions. This study demonstrated the extraction of the polaron dynamics from transient EL by predicting the recombination coefficient via artificial intelligence, overcoming multivariable kinetics problems. The performance of a machine learning (ML) model trained by various EL decay curves is significantly improved using a novel featurization method and input node optimization, achieving an R2 value of 0.947. The optimized ML model successfully predicts the recombination coefficients of actual OLEDs based on an exciplex-forming cohost, enabling the quantitative understanding of the overall polaron behavior under various electrical excitation conditions.
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Affiliation(s)
- Jae-Min Kim
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Kyung Hyung Lee
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Jun Yeob Lee
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nano Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of korea
- SKKU Institute of Energy Science and Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of korea
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18
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Glotz G, Püschmann S, Haas M, Gescheidt G. Direct detection of photo-induced reactions by IR: from Brook rearrangement to photo-catalysis. Photochem Photobiol Sci 2023:10.1007/s43630-023-00406-4. [PMID: 36933157 DOI: 10.1007/s43630-023-00406-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023]
Abstract
In situ IR detection of photoreactions induced by the light of LEDs at appropriate wavelengths provides a simple, cost-effective, and versatile method to get insight into mechanistic details. In particular, conversions of functional groups can be selectively followed. Overlapping UV-Vis bands or fluorescence from the reactants and products and the incident light do not obstruct IR detection. Compared with in situ photo-NMR, our setup does not require tedious sample preparation (optical fibers) and offers a selective detection of reactions, even at positions where 1H-NMR lines overlap or 1H resonances are not clear-cut. We illustrate the applicability of our setup following the photo-Brook rearrangement of (adamant-1-yl-carbonyl)-tris(trimethylsilyl)silane, address photo-induced α-bond cleavage (1-hydroxycyclohexyl phenyl ketone), study photoreduction using tris(bipyridine)ruthenium(II), investigate photo-oxygenation of double bonds with molecular oxygen and the fluorescent 2,4,6-triphenylpyrylium photocatalyst, and address photo-polymerization. With the LED/FT-IR combination, reactions can be qualitatively followed in fluid solution, (highly) viscous environments, and in the solid state. Viscosity changes during the reaction (e.g., during a polymerization) do not obstruct the method.
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Affiliation(s)
- Gabriel Glotz
- Institute of Physical and Theoretical Chemistry, Graz University of Technology, Stremayrgasse 9/II, 8010, Graz, Austria.
| | - Sabrina Püschmann
- Institute of Inorganic Chemistry, Graz University of Technology, Stremayrgasse 9/IV, 8010, Graz, Austria
| | - Michael Haas
- Institute of Inorganic Chemistry, Graz University of Technology, Stremayrgasse 9/IV, 8010, Graz, Austria
| | - Georg Gescheidt
- Institute of Physical and Theoretical Chemistry, Graz University of Technology, Stremayrgasse 9/II, 8010, Graz, Austria
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19
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Qin J, Wang H, Xu Y, Shi F, Yang S, Huang H, Liu J, Stewart C, Li L, Li F, Han J, Wu W. A simple array integrating machine learning for identification of flavonoids in red wines. RSC Adv 2023; 13:8882-8889. [PMID: 36936820 PMCID: PMC10019168 DOI: 10.1039/d2ra08049d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation, via optimized machine learning algorithms. As a result, this array demonstrated the parallel detection of 8 different types and origins of red wines with a high accuracy, revealing the excellent potential of the sensor array in food mixtures detection.
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Affiliation(s)
- Jiaojiao Qin
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Hao Wang
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Yu Xu
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Fangfang Shi
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Shijie Yang
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Hui Huang
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden
| | - Jun Liu
- Shandong Yuwang Ecological Food Industry Co., Ltd De Zhou 251200 China
| | - Callum Stewart
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden
| | - Linxian Li
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden
| | - Fei Li
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Jinsong Han
- State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China
| | - Wenwen Wu
- Department of Pharmacy, Children's Hospital of Nanjing Medical University Nanjing Jiangsu Province 211109 China
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20
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McNaughton AD, Joshi RP, Knutson CR, Fnu A, Luebke KJ, Malerich JP, Madrid PB, Kumar N. Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties. J Chem Inf Model 2023; 63:1462-1471. [PMID: 36847578 DOI: 10.1021/acs.jcim.2c01662] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.
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Affiliation(s)
- Andrew D McNaughton
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Rajendra P Joshi
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Carter R Knutson
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Anubhav Fnu
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kevin J Luebke
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Jeremiah P Malerich
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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21
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Sani MJ. Theoretical survey on the electronic, linear and nonlinear optical properties of substituted benzenes and polycondensed π-systems. A density functional theory study. COMPUT THEOR CHEM 2023. [DOI: 10.1016/j.comptc.2023.114100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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22
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Guo J, Sun M, Zhao X, Shi C, Su H, Guo Y, Pu X. General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation. J Chem Inf Model 2023; 63:1143-1156. [PMID: 36734616 DOI: 10.1021/acs.jcim.2c01538] [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: 02/04/2023]
Abstract
Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph.
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Affiliation(s)
- Jiali Guo
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Ming Sun
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Xueyan Zhao
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang621900, China
| | - Chaojie Shi
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Haoming Su
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
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23
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Bertoni AI, Sánchez CG. Data-driven approach for benchmarking DFTB-approximate excited state methods. Phys Chem Chem Phys 2023; 25:3789-3798. [PMID: 36645084 DOI: 10.1039/d2cp04979a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In this work we propose a chemically-informed data-driven approach to benchmark the approximate density-functional tight-binding (DFTB) excited state (ES) methods that are currently available within the DFTB+ suite. By taking advantage of the large volume of low-detail ES-data in the machine learning (ML) dataset, QM8, we were able to extract valuable insights regarding the limitations of the benchmarked methods in terms of the approximations made to the parent formalism, density-functional theory (DFT), while providing recommendations on how to overcome them. For this benchmark, we compared the first singlet-singlet vertical excitation energies (E1) predicted by the DFTB-approximate methods with predictions of less approximate methods from the reference ML-dataset. For the nearly 21800 organic molecules in the GDB-8 chemical space, we were able to identify clear trends in the E1 prediction error distributions, with respect to second-order approximate coupled cluster (CC2), showing a strong dependence on chemical identity.
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Affiliation(s)
- Andrés I Bertoni
- Instituto Interdisciplinario de Ciencias Básicas (ICB-CONICET), Universidad Nacional de Cuyo, Padre Jorge Contreras 1300, Mendoza 5502, Argentina.
| | - Cristián G Sánchez
- Instituto Interdisciplinario de Ciencias Básicas (ICB-CONICET), Universidad Nacional de Cuyo, Padre Jorge Contreras 1300, Mendoza 5502, Argentina.
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24
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Telegin FY, Karpova VS, Makshanova AO, Astrakhantsev RG, Marfin YS. Solvatochromic Sensitivity of BODIPY Probes: A New Tool for Selecting Fluorophores and Polarity Mapping. Int J Mol Sci 2023; 24:ijms24021217. [PMID: 36674731 PMCID: PMC9860957 DOI: 10.3390/ijms24021217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
This research work is devoted to collecting a high-quality dataset of BODIPYs in a series of 10-30 solvents. In total, 115 individual compounds in 71 solvents are represented by 1698 arrays of the spectral and photophysical properties of the fluorophore. Each dye for a series of solvents is characterized by a calculated value of solvatochromic sensitivity according to a semiempirical approach applied to a series of solvents. The whole dataset is classified into 6 and 24 clusters of solvatochromic sensitivity, from high negative to high positive solvatochromism. The results of the analysis are visualized by the polarity mapping plots depicting, in terms of wavenumbers, the absorption versus emission, stokes shift versus - (absorption maxima + emission maxima), and quantum yield versus stokes shift. An analysis of the clusters combining several dyes in an individual series of solvents shows that dyes of a high solvatochromic sensitivity demonstrate regular behaviour of the corresponding plots suitable for polarity and viscosity mapping. The fluorophores collected in this study represent a high quality dataset of pattern dyes for analytical and bioanalytical applications. The developed tools could be applied for the analysis of the applicability domain of the fluorescent sensors.
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Affiliation(s)
- Felix Y. Telegin
- G.A. Krestov Institute of Solution Chemistry of the RAS, 153045 Ivanovo, Russia
| | - Viktoria S. Karpova
- Department of Inorganic Chemistry, Ivanovo State University of Chemistry and Technology, 153000 Ivanovo, Russia
| | - Anna O. Makshanova
- Department of Natural Sciences, Mendeleev University of Chemical Technology of Russia, 125047 Moscow, Russia
| | - Roman G. Astrakhantsev
- HSE Tikhonov Moscow Institute of Electronics and Mathematics, HSE University, 101000 Moscow, Russia
| | - Yuriy S. Marfin
- G.A. Krestov Institute of Solution Chemistry of the RAS, 153045 Ivanovo, Russia
- Correspondence:
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25
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Rybczyński P, Bousquet MHE, Kaczmarek-Kędziera A, Jędrzejewska B, Jacquemin D, Ośmiałowski B. Controlling the fluorescence quantum yields of benzothiazole-difluoroborates by optimal substitution. Chem Sci 2022; 13:13347-13360. [PMID: 36507166 PMCID: PMC9682896 DOI: 10.1039/d2sc05044g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/21/2022] [Indexed: 12/15/2022] Open
Abstract
Precise tuning of the fluorescence quantum yield, vital for countless applications of fluorophores, remains exceptionally challenging due to numerous factors affecting energy dissipation phenomena often leading to its counterintuitive behavior. In contrast to the absorption and emission wavelength which can be precisely shifted to the desired range by simple structural changes, no general strategy exists for controllable modification of the fluorescence quantum yield. The rigidification of the molecular skeleton is known to usually enhance the emission and can be practically realized via the limiting molecular vibrations by aggregation. However, the subtle balance between the abundant possible radiative and non-radiative decay pathways makes the final picture exceptionally sophisticated. In the present study, a series of nine fluorophores obtained by peripheral substitution with two relatively mild electron donating and electron withdrawing groups are reported. The obtained fluorescence quantum yields range from dark to ultra-bright and the extreme values are obtained for the isomeric molecules. These severe changes in emission efficiency have been shown to arise from the complex relationship between the Franck-Condon excited state and conical intersection position. The experimental findings are rationalized by the advanced quantum chemical calculations delivering good correlation between the measured emission parameters and theoretical radiative and internal conversion rate constants. Therefore, the described substituent exchange provides a method to rigorously adjust the properties of molecular probes structurally similar to thioflavin T.
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Affiliation(s)
- Patryk Rybczyński
- Faculty of Chemistry, Nicolaus Copernicus University in ToruńGagarina Street 787-100 ToruńPoland
| | | | - Anna Kaczmarek-Kędziera
- Faculty of Chemistry, Nicolaus Copernicus University in ToruńGagarina Street 787-100 ToruńPoland
| | - Beata Jędrzejewska
- Bydgoszcz University of Science and Technology, Faculty of Chemical Technology and EngineeringSeminaryjna 385-326 BydgoszczPoland
| | - Denis Jacquemin
- Nantes Université, CNRS, CEISAM UMR 6230F-44000 NantesFrance,Institut Universitaire de France (IUF)ParisFR-75005France
| | - Borys Ośmiałowski
- Faculty of Chemistry, Nicolaus Copernicus University in ToruńGagarina Street 787-100 ToruńPoland
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26
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Pakuła A, Żołnowski W, Paśko S, Kursa O, Marć P, Jaroszewicz LR. Multispectral Portable Fibre-Optic Reflectometer for the Classification of the Origin of Chicken Eggshells in the Case of Mycoplasma synoviae Infections. SENSORS (BASEL, SWITZERLAND) 2022; 22:8690. [PMID: 36433286 PMCID: PMC9692302 DOI: 10.3390/s22228690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
The proper classification of the origins of food products is a crucial issue all over the world nowadays. In this paper, the authors present a device-a multispectral portable fibre-optic reflectometer and signal processing patch-together with a machine-learning algorithm for the classification of the origins of chicken eggshells in the case of Mycoplasma synoviae infection. The sensor device was developed based on previous studies with a continuous spectrum in transmittance and selected spectral lines in reflectance. In the described case, the sensor is based on the integration of reflected spectral data from short spectral bands from the VIS and NIR region, which are produced by single-colour LEDs and introduced to the sample via a fibre bundle. The measurement is carried out in a sequence, and the reflected signal is pre-processed to be put in the machine learning algorithm. The support vector machine algorithm is used together with three different types of data normalization. The obtained results of the F-score factor for classification of the origins of samples show that the percentages of eggs coming from Mycoplasma synoviae infected hens are up to 87% for white and 96% for brown eggshells.
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Affiliation(s)
- Anna Pakuła
- Institute of Micromechanics and Photonics, Warsaw University of Technology, Św. A. Boboli 8, 02-525 Warsaw, Poland
- Faculty of New Technologies and Chemistry, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Wojciech Żołnowski
- Institute of Optoelectronics, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Sławomir Paśko
- Institute of Micromechanics and Photonics, Warsaw University of Technology, Św. A. Boboli 8, 02-525 Warsaw, Poland
| | - Olimpia Kursa
- Department of Poultry Diseases, National Veterinary Research Institute, Al. Partyzantów 57, 24-100 Puławy, Poland
| | - Paweł Marć
- Faculty of New Technologies and Chemistry, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Leszek R. Jaroszewicz
- Faculty of New Technologies and Chemistry, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
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Ksenofontov AA, Lukanov MM, Bocharov PS. Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes? SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121442. [PMID: 35660154 DOI: 10.1016/j.saa.2022.121442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In this article, we provide a convenient tool for all researchers to predict the value of the molar absorption coefficient for a wide number of dyes without any computer costs. The new model is based on RFR method (ALogPS, OEstate + Fragmentor + QNPR) and is able to predict the molar absorption coefficient with an accuracy (5-fold cross-validation RMSE) of 0.26 log unit. This accuracy was achieved due to the fact that the model was trained on data for more than 20,000 unique dye molecules. To our knowledge, this is the first model for predicting the molar absorption coefficient trained on such a large and diverse set of dyes. The model is available at https://ochem.eu/article/145413. We hope that the new model will allow researchers to predict dyes with practically significant spectral characteristics and verify existing experimental data.
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Affiliation(s)
- Alexander A Ksenofontov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia.
| | - Michail M Lukanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Pavel S Bocharov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia
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28
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Kim JM, Lim J, Lee JY. Understanding the charge dynamics in organic light-emitting diodes using convolutional neural network. MATERIALS HORIZONS 2022; 9:2551-2563. [PMID: 35861172 DOI: 10.1039/d2mh00373b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Knowledge about the charge dynamics in organic light-emitting diodes (OLEDs) is a critical clue to optimize device architecture for enhancing the power efficiency and driving voltage characteristics in addition to the external quantum efficiency. In this work, we demonstrated that the charge behavior according to the operation voltage of OLEDs could be understood by introducing the convolutional neural network (CNN) of the machine learning framework without additional analysis of the unipolar charge devices. The CNN model trained using a two-dimensional (2D) modulus fingerprint simultaneously predicted the mobilities of the charge transport and emitting layers, realizing a deep understanding of the complicated data that humans cannot interpret. The machine learning model successfully describes the electrical properties of the organic layers in the actual devices configurated by different electron-transporting materials and the composition of cohosts in the emitting layer. For the first time, it was revealed that 2D fingerprints extracted using frequency- and voltage-dependent modulus spectra were effective data to represent comprehensive charge dynamics of OLEDs. The interpretation and perspective of the machine learning approach in this work were also discussed.
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Affiliation(s)
- Jae-Min Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon Campus, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
| | - Junseop Lim
- School of Chemical Engineering, Sungkyunkwan University, Suwon Campus, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
| | - Jun Yeob Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon Campus, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
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Yan J, Rodríguez-Martínez X, Pearce D, Douglas H, Bili D, Azzouzi M, Eisner F, Virbule A, Rezasoltani E, Belova V, Dörling B, Few S, Szumska AA, Hou X, Zhang G, Yip HL, Campoy-Quiles M, Nelson J. Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics. ENERGY & ENVIRONMENTAL SCIENCE 2022; 15:2958-2973. [PMID: 35923416 PMCID: PMC9277517 DOI: 10.1039/d2ee00887d] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of their high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ∼500 π-conjugated molecules. The calculations are first validated by comparison with experimental measurements in solution and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient (ε d,max) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic π-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest algorithm to draw predictions for ε d,max using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in π-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost.
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Affiliation(s)
- Jun Yan
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Xabier Rodríguez-Martínez
- Electronic and Photonic Materials (EFM), Department of Physics, Chemistry and Biology (IFM), Linköping University Linköping SE 581 83 Sweden
- Instituto de Ciencia de Materiales de Barcelona, ICMAB-CSIC, Campus UAB Bellaterra 08193 Spain
| | - Drew Pearce
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Hana Douglas
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Danai Bili
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Mohammed Azzouzi
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Flurin Eisner
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Alise Virbule
- Department of Physics, Imperial College London SW7 2AZ London UK
| | | | - Valentina Belova
- Instituto de Ciencia de Materiales de Barcelona, ICMAB-CSIC, Campus UAB Bellaterra 08193 Spain
| | - Bernhard Dörling
- Instituto de Ciencia de Materiales de Barcelona, ICMAB-CSIC, Campus UAB Bellaterra 08193 Spain
| | - Sheridan Few
- Department of Physics, Imperial College London SW7 2AZ London UK
- Sustainability Research Institute, School of Earth and Environment, University of Leeds LS2 9JT Leeds UK
| | - Anna A Szumska
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Xueyan Hou
- Department of Physics, Imperial College London SW7 2AZ London UK
| | - Guichuan Zhang
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology Guangzhou 510640 P. R. China
| | - Hin-Lap Yip
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology Guangzhou 510640 P. R. China
- Department of Materials Science and Engineering, City University of Hong Kong, Tat Chee Avenue Kowloon Hong Kong
| | - Mariano Campoy-Quiles
- Instituto de Ciencia de Materiales de Barcelona, ICMAB-CSIC, Campus UAB Bellaterra 08193 Spain
| | - Jenny Nelson
- Department of Physics, Imperial College London SW7 2AZ London UK
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30
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Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates. Molecules 2022; 27:molecules27113456. [PMID: 35684394 PMCID: PMC9182218 DOI: 10.3390/molecules27113456] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 02/04/2023] Open
Abstract
Dye aggregates are of interest for excitonic applications, including biomedical imaging, organic photovoltaics, and quantum information systems. Dyes with large transition dipole moments (μ) are necessary to optimize coupling within dye aggregates. Extinction coefficients (ε) can be used to determine the μ of dyes, and so dyes with a large ε (>150,000 M−1cm−1) should be engineered or identified. However, dye properties leading to a large ε are not fully understood, and low-throughput methods of dye screening, such as experimental measurements or density functional theory (DFT) calculations, can be time-consuming. In order to screen large datasets of molecules for desirable properties (i.e., large ε and μ), a computational workflow was established using machine learning (ML), DFT, time-dependent (TD-) DFT, and molecular dynamics (MD). ML models were developed through training and validation on a dataset of 8802 dyes using structural features. A Classifier was developed with an accuracy of 97% and a Regressor was constructed with an R2 of above 0.9, comparing between experiment and ML prediction. Using the Regressor, the ε values of over 18,000 dyes were predicted. The top 100 dyes were further screened using DFT and TD-DFT to identify 15 dyes with a μ relative to a reference dye, pentamethine indocyanine dye Cy5. Two benchmark MD simulations were performed on Cy5 and Cy5.5 dimers, and it was found that MD could accurately capture experimental results. The results of this study exhibit that our computational workflow for identifying dyes with a large μ for excitonic applications is effective and can be used as a tool to develop new dyes for excitonic applications.
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31
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Genin SN, Ryabinkin IG, Paisley NR, Whelan SO, Helander MG, Hudson ZM. Estimating Phosphorescent Emission Energies in Ir
III
Complexes Using Large‐Scale Quantum Computing Simulations**. Angew Chem Int Ed Engl 2022; 61:e202116175. [DOI: 10.1002/anie.202116175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Scott N. Genin
- OTI Lumionics Inc. 100 College St. #351 Toronto Ontario M5G 1L5 Canada
| | - Ilya G. Ryabinkin
- OTI Lumionics Inc. 100 College St. #351 Toronto Ontario M5G 1L5 Canada
| | - Nathan R. Paisley
- Department of Chemistry The University of British Columbia 2036 Main Mall Vancouver British Columbia V6T 1Z1 Canada
| | - Sarah O. Whelan
- OTI Lumionics Inc. 100 College St. #351 Toronto Ontario M5G 1L5 Canada
| | | | - Zachary M. Hudson
- Department of Chemistry The University of British Columbia 2036 Main Mall Vancouver British Columbia V6T 1Z1 Canada
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32
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Joung JF, Han M, Jeong M, Park S. Beyond Woodward-Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy. J Chem Inf Model 2022; 62:2933-2942. [PMID: 35476584 DOI: 10.1021/acs.jcim.2c00173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
An adequate understanding of molecular structure-property relationships is important for developing new molecules with desired properties. Although deep learning optical spectroscopy (DLOS) has been successfully applied to predict the optical and photophysical properties of organic chromophores, how specific functional groups and solvents affect the optical properties is not clearly understood. Here, we employed an explainable DLOS method by applying the integrated gradients method to DLOS. The integrated gradients method allows us to obtain attributions, indicating how much the functional group contributes to the optical properties including the absorption wavelength and bandwidth, extinction coefficients, emission wavelength and bandwidth, photoluminescence quantum yield, and lifetime. The attributions of 54 functional groups and 9 solvent molecules to seven optical properties are quantified and can be used to estimate the optical properties of chromophores as in the Woodward-Fieser rule. Unlike the Woodward-Fieser rule for only the absorption wavelength, the attributions obtained in this work can be applied to estimate all seven optical properties, which makes a significant extension of the Woodward-Fieser rules. In addition, we demonstrated a strategy for utilizing the attributions in the design of molecules and in tuning the optical properties of the molecules. The design of molecular structures using attributions can revolutionize the development of optimal molecules.
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Affiliation(s)
- Joonyoung F Joung
- Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea
| | - Minhi Han
- Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea
| | - Minseok Jeong
- Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea
| | - Sungnam Park
- Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea
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33
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Genin SN, Ryabinkin IG, Paisley NR, Whelan SO, Helander MG, Hudson ZM. Estimating Phosphorescent Emission Energies in Ir
III
Complexes Using Large‐Scale Quantum Computing Simulations**. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202116175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Scott N. Genin
- OTI Lumionics Inc. 100 College St. #351 Toronto Ontario M5G 1L5 Canada
| | - Ilya G. Ryabinkin
- OTI Lumionics Inc. 100 College St. #351 Toronto Ontario M5G 1L5 Canada
| | - Nathan R. Paisley
- Department of Chemistry The University of British Columbia 2036 Main Mall Vancouver British Columbia V6T 1Z1 Canada
| | - Sarah O. Whelan
- OTI Lumionics Inc. 100 College St. #351 Toronto Ontario M5G 1L5 Canada
| | | | - Zachary M. Hudson
- Department of Chemistry The University of British Columbia 2036 Main Mall Vancouver British Columbia V6T 1Z1 Canada
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34
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Ksenofontov AA, Lukanov MM, Bocharov PS, Berezin MB, Tetko IV. Deep neural network model for highly accurate prediction of BODIPYs absorption. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120577. [PMID: 34776377 DOI: 10.1016/j.saa.2021.120577] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 10/12/2021] [Accepted: 10/31/2021] [Indexed: 06/13/2023]
Abstract
A possibility to accurately predict the absorption maximum wavelength of BODIPYs was investigated. We found that previously reported models had a low accuracy (40-57 nm) to predict BODIPYs due to the limited dataset sizes and/or number of BODIPYs (few hundreds). New models developed in this study were based on data of 6000-plus fluorescent dyes (including 4000-plus BODIPYs) and the deep neural network architecture. The high prediction accuracy (five-fold cross-validation room mean squared error (RMSE) of 18.4 nm) was obtained using a consensus model, which was more accurate than individual models. This model provided the excellent accuracy (RMSE of 8 nm) for molecules previously synthesized in our laboratory as well as for prospective validation of three new BODIPYs. We found that solvent properties did not significantly influence the model accuracy since only few BODIPYs exhibited solvatochromism. The analysis of large prediction errors suggested that compounds able to have intermolecular interactions with solvent or salts were likely to be incorrectly predicted. The consensus model is freely available at https://ochem.eu/article/134921 and can help the other researchers to accelerate design of new dyes with desired properties.
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Affiliation(s)
- Alexander A Ksenofontov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia.
| | - Michail M Lukanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Pavel S Bocharov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Michail B Berezin
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia
| | - Igor V Tetko
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Helmholtz Zentrum München‑German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; BIGCHEM GmbH, Valerystr. 49, 85716 Unterschleißheim, Germany
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35
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Greenman KP, Green WH, Gómez-Bombarelli R. Multi-fidelity prediction of molecular optical peaks with deep learning. Chem Sci 2022; 13:1152-1162. [PMID: 35211282 PMCID: PMC8790778 DOI: 10.1039/d1sc05677h] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/04/2022] [Indexed: 01/24/2023] Open
Abstract
Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28 000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties.
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Affiliation(s)
- Kevin P Greenman
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA
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36
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022. [DOI: https://doi.org/10.3390/ijms23031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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37
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022; 23:ijms23031201. [PMID: 35163123 PMCID: PMC8835262 DOI: 10.3390/ijms23031201] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 01/19/2022] [Indexed: 02/05/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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Affiliation(s)
- Aleksey I. Rusanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Olga A. Dmitrieva
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Nugzar Zh. Mamardashvili
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Igor V. Tetko
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
- Helmholtz Munich, Institute of Structural Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany
- BIGCHEM GmbH, D-85716 Unterschleißheim, Germany
- Correspondence: ; Tel.: +49-89-3187-3575
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38
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022. [DOI: https:/doi.org/10.3390/ijms23031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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39
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Kang B, Seok C, Lee J. A benchmark study of machine learning methods for molecular electronic transition: Tree‐based ensemble learning versus graph neural network. B KOREAN CHEM SOC 2022. [DOI: 10.1002/bkcs.12468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Beomchang Kang
- Department of Chemistry Seoul National University Seoul South Korea
| | - Chaok Seok
- Department of Chemistry Seoul National University Seoul South Korea
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry Kangwon National University Chuncheon South Korea
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Mamede R, Pereira F, Aires-de-Sousa J. Machine learning prediction of UV-Vis spectra features of organic compounds related to photoreactive potential. Sci Rep 2021; 11:23720. [PMID: 34887473 PMCID: PMC8660842 DOI: 10.1038/s41598-021-03070-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/22/2021] [Indexed: 11/09/2022] Open
Abstract
Machine learning (ML) algorithms were explored for the classification of the UV-Vis absorption spectrum of organic molecules based on molecular descriptors and fingerprints generated from 2D chemical structures. Training and test data (~ 75 k molecules and associated UV-Vis data) were assembled from a database with lists of experimental absorption maxima. They were labeled with positive class (related to photoreactive potential) if an absorption maximum is reported in the range between 290 and 700 nm (UV/Vis) with molar extinction coefficient (MEC) above 1000 Lmol-1 cm-1, and as negative if no such a peak is in the list. Random forests were selected among several algorithms. The models were validated with two external test sets comprising 998 organic molecules, obtaining a global accuracy up to 0.89, sensitivity of 0.90 and specificity of 0.88. The ML output (UV-Vis spectrum class) was explored as a predictor of the 3T3 NRU phototoxicity in vitro assay for a set of 43 molecules. Comparable results were observed with the classification directly based on experimental UV-Vis data in the same format.
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Affiliation(s)
- Rafael Mamede
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal
| | - Florbela Pereira
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal
| | - João Aires-de-Sousa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal.
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