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For: Ju CW, Bai H, Li B, Liu R. Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields. J Chem Inf Model 2021;61:1053-65. [PMID: 33620207 DOI: 10.1021/acs.jcim.0c01203] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 9.5] [Reference Citation Analysis]
Number Citing Articles
1 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. [PMID: 36674731 DOI: 10.3390/ijms24021217] [Reference Citation Analysis]
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3 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;13:13347-60. [PMID: 36507166 DOI: 10.1039/d2sc05044g] [Reference Citation Analysis]
4 Fan J, Qian C, Zhou S. Machine Learning Spectroscopy Based on Group Contribution and Molecule Contribution Methods.. [DOI: 10.21203/rs.3.rs-2139666/v1] [Reference Citation Analysis]
5 Ksenofontov AA, Lukanov MM, Bocharov PS. Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes? Spectrochim Acta A Mol Biomol Spectrosc 2022;279:121442. [PMID: 35660154 DOI: 10.1016/j.saa.2022.121442] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Mai J, Lu T, Xu P, Lian Z, Li M, Lu W. Predicting the maximum absorption wavelength of azo dyes using an interpretable machine learning strategy. Dyes and Pigments 2022;206:110647. [DOI: 10.1016/j.dyepig.2022.110647] [Reference Citation Analysis]
7 Gong J, Gong W, Wu B, Wang H, He W, Dai Z, Li Y, Liu Y, Wang Z, Tuo X, Lam JW, Qiu Z, Zhao Z, Tang BZ. ASBase: The universal database for aggregate science. Aggregate 2022. [DOI: 10.1002/agt2.263] [Reference Citation Analysis]
8 Ye Z, Hung S, Chen B, Tsai M. Assessment of Predicting Frontier Orbital Energies for Small Organic Molecules Using Knowledge-Based and Structural Information. ACS Eng Au 2022;2:360-368. [DOI: 10.1021/acsengineeringau.2c00011] [Reference Citation Analysis]
9 Nie H, Wei Z, Ni XL, Liu Y. Assembly and Applications of Macrocyclic-Confinement-Derived Supramolecular Organic Luminescent Emissions from Cucurbiturils. Chem Rev 2022. [PMID: 35312308 DOI: 10.1021/acs.chemrev.1c01050] [Cited by in Crossref: 30] [Cited by in F6Publishing: 32] [Article Influence: 30.0] [Reference Citation Analysis]
10 Shao J, Liu Y, Yan J, Yan ZY, Wu Y, Ru Z, Liao JY, Miao X, Qian L. Prediction of Maximum Absorption Wavelength Using Deep Neural Networks. J Chem Inf Model 2022. [PMID: 35290042 DOI: 10.1021/acs.jcim.1c01449] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
11 Axelrod S, Schwalbe-koda D, Mohapatra S, Damewood J, Greenman KP, Gómez-bombarelli R. Learning Matter: Materials Design with Machine Learning and Atomistic Simulations. Acc Mater Res . [DOI: 10.1021/accountsmr.1c00238] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Fliszkiewicz B. A study of boosting molecular descriptors with quantum-derived features in prediction of maximum emission wavelengths of chromophores. Chemical Data Collections 2022;37:100810. [DOI: 10.1016/j.cdc.2021.100810] [Reference Citation Analysis]
13 Greenman KP, Green WH, Gómez-Bombarelli R. Multi-fidelity prediction of molecular optical peaks with deep learning. Chem Sci 2022;13:1152-62. [PMID: 35211282 DOI: 10.1039/d1sc05677h] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
14 Blanchard AE, Zhang P, Bhowmik D, Mehta K, Gounley J, Reeve ST, Irle S, Pasini ML. Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models. Communications in Computer and Information Science 2022. [DOI: 10.1007/978-3-031-23606-8_1] [Reference Citation Analysis]
15 Gupta A, Chakraborty S, Ghosh D, Ramakrishnan R. Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design. J Chem Phys 2021;155:244102. [PMID: 34972385 DOI: 10.1063/5.0076787] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
16 Mukadum F, Nguyen Q, Adrion DM, Appleby G, Chen R, Dang H, Chang R, Garnett R, Lopez SA. Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives Using Active Search. J Chem Inf Model 2021;61:5524-34. [PMID: 34752100 DOI: 10.1021/acs.jcim.1c00954] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Tang S, Yang T, Zhao Z, Zhu T, Zhang Q, Hou W, Yuan WZ. Nonconventional luminophores: characteristics, advancements and perspectives. Chem Soc Rev 2021;50:12616-55. [PMID: 34610056 DOI: 10.1039/d0cs01087a] [Cited by in Crossref: 55] [Cited by in F6Publishing: 70] [Article Influence: 27.5] [Reference Citation Analysis]
18 Ksenofontov AA, Lukanov MM, Bocharov PS, Berezin MB, Tetko IV. Deep neural network model for highly accurate prediction of BODIPYs absorption. Spectrochim Acta A Mol Biomol Spectrosc 2021;:120577. [PMID: 34776377 DOI: 10.1016/j.saa.2021.120577] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
19 Bao Y. Controlling Molecular Aggregation-Induced Emission by Controlled Polymerization. Molecules 2021;26:6267. [PMID: 34684848 DOI: 10.3390/molecules26206267] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Ju CW, French EJ, Geva N, Kohn AW, Lin Z. Stacked Ensemble Machine Learning for Range-Separation Parameters. J Phys Chem Lett 2021;12:9516-24. [PMID: 34559964 DOI: 10.1021/acs.jpclett.1c02506] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
21 Xu Y, Ju CW, Li B, Ma QS, Chen Z, Zhang L, Chen J. Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors. ACS Appl Mater Interfaces 2021;13:34033-42. [PMID: 34269560 DOI: 10.1021/acsami.1c05536] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
22 Dral PO, Barbatti M. Molecular excited states through a machine learning lens. Nat Rev Chem 2021;5:388-405. [DOI: 10.1038/s41570-021-00278-1] [Cited by in Crossref: 43] [Cited by in F6Publishing: 47] [Article Influence: 21.5] [Reference Citation Analysis]
23 Westermayr J, Marquetand P. Machine Learning for Electronically Excited States of Molecules. Chem Rev 2021;121:9873-926. [PMID: 33211478 DOI: 10.1021/acs.chemrev.0c00749] [Cited by in Crossref: 95] [Cited by in F6Publishing: 107] [Article Influence: 31.7] [Reference Citation Analysis]